US20110125550A1 - Method for determining customer value and potential from social media and other public data sources - Google Patents
Method for determining customer value and potential from social media and other public data sources Download PDFInfo
- Publication number
- US20110125550A1 US20110125550A1 US12/762,854 US76285410A US2011125550A1 US 20110125550 A1 US20110125550 A1 US 20110125550A1 US 76285410 A US76285410 A US 76285410A US 2011125550 A1 US2011125550 A1 US 2011125550A1
- Authority
- US
- United States
- Prior art keywords
- social media
- dialog
- criteria
- score
- user
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Abandoned
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M3/00—Automatic or semi-automatic exchanges
- H04M3/42—Systems providing special services or facilities to subscribers
- H04M3/50—Centralised arrangements for answering calls; Centralised arrangements for recording messages for absent or busy subscribers ; Centralised arrangements for recording messages
- H04M3/51—Centralised call answering arrangements requiring operator intervention, e.g. call or contact centers for telemarketing
- H04M3/523—Centralised call answering arrangements requiring operator intervention, e.g. call or contact centers for telemarketing with call distribution or queueing
- H04M3/5232—Call distribution algorithms
- H04M3/5233—Operator skill based call distribution
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0204—Market segmentation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0241—Advertisements
- G06Q30/0251—Targeted advertisements
- G06Q30/0255—Targeted advertisements based on user history
- G06Q30/0256—User search
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0241—Advertisements
- G06Q30/0251—Targeted advertisements
- G06Q30/0264—Targeted advertisements based upon schedule
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0241—Advertisements
- G06Q30/0251—Targeted advertisements
- G06Q30/0269—Targeted advertisements based on user profile or attribute
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/01—Social networking
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/005—Language recognition
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/20—Services signaling; Auxiliary data signalling, i.e. transmitting data via a non-traffic channel
- H04W4/21—Services signaling; Auxiliary data signalling, i.e. transmitting data via a non-traffic channel for social networking applications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M2203/00—Aspects of automatic or semi-automatic exchanges
- H04M2203/65—Aspects of automatic or semi-automatic exchanges related to applications where calls are combined with other types of communication
- H04M2203/655—Combination of telephone service and social networking
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M3/00—Automatic or semi-automatic exchanges
- H04M3/42—Systems providing special services or facilities to subscribers
- H04M3/42025—Calling or Called party identification service
- H04M3/42034—Calling party identification service
- H04M3/42059—Making use of the calling party identifier
- H04M3/42068—Making use of the calling party identifier where the identifier is used to access a profile
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M3/00—Automatic or semi-automatic exchanges
- H04M3/42—Systems providing special services or facilities to subscribers
- H04M3/50—Centralised arrangements for answering calls; Centralised arrangements for recording messages for absent or busy subscribers ; Centralised arrangements for recording messages
- H04M3/51—Centralised call answering arrangements requiring operator intervention, e.g. call or contact centers for telemarketing
- H04M3/5183—Call or contact centers with computer-telephony arrangements
- H04M3/5191—Call or contact centers with computer-telephony arrangements interacting with the Internet
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M7/00—Arrangements for interconnection between switching centres
- H04M7/0024—Services and arrangements where telephone services are combined with data services
Definitions
- Contact centers generally exchange information with consumers through directed contacts. Directed contacts consist of emails, phone calls, or other forms of communication that are directed to the contact center or the consumer. However, many people today, exchange information or interact through non-direct methods. Non-direct communications require users to post communications to third party sites or forums, but not to direct those communications to a specific person or organization. Non-direct communication methods include social media, which may include websites, networks, blogs, micro-blogs, RSS feeds, social media websites (such as, Linked-In, Facebook, Twitter, MySpace, etc.), and other types of social media. Generally, it is not possible for contact centers to communicate with consumers through non-direct methods. As such, the contact centers may be unable to interact with consumers that use social media to offer certain types of customer service.
- contact centers have limited amounts of resources. As such, to be efficient, and to drive down costs, contact centers like to use resources only on customers that have a likely chance of buying products or responding to an interaction. As such, contact centers would like to project where they may use their resources best. Unfortunately, there are no current ways of deducing whether marketing efforts are directed to a high value customer when that customer is using social media.
- Embodiments presented herein provide systems and methods for determining the value of a customer that uses social media to communicate.
- An enterprise that desires to understand the value of a customer can provide an identity or identities of customers or provide a profile of one or more customers to a contact center 102 . Further, the enterprise can provide one or more criteria in which the enterprise wants to base the customer value.
- the criteria can include such things as the number of friends or followers for that user on a social media site, the number of posts the user creates in a pre-determined period of time (e.g. hour, day, week, etc.), the number of responses to the user's postings, the tenor of the user's postings, and other data.
- the different parameters can be scored to provide a score for each parameter, then summed to create a customer value.
- the parameters associated with the social media site can also be combined with other data from public or other sources. For example, a determination of the wealth or income of the user can be made, for example, in the neighborhood from which the user lives, or from other data. The additional data and social media data can be combined to create a more comprehensive customer value.
- a Social Media Gateway can be used to gather information on specific social network users.
- data may be collected about the user that may exist on public blogs, wikis, etc.
- data sources with public information e.g., census, zillow.com, etc., may be used to gather more user information.
- the system can create a value of the source or destination user.
- the value profile is generated from information on the above mentioned sites.
- a key aspect of the value profile is the derivative information, or how influential the user might be.
- Information about number of followers (twitter), number of friends (Facebook), blog readers, etc. help to determine how far the user's influence may spread, thus increasing the customer's value.
- Adding to the influence attribute can be an activity factor. If a user posts a lot and has a lot of replies in return, then they may be a more valuable customer that would spread information through that influence.
- text analysis could be used to determine the general emotion of the posts and responses. A user who has upbeat and happy posts might be more likely to spread good words about an enterprise.
- the system can utilize both stored data and realtime search data.
- Stored data may include information that is discovered, including: social network accounts, blog sites, demographic data, etc. The discovered can be information that does not change very often. A complete picture may be generated over time as different modes of communication are used.
- the realtime data can be queried as needed by the contact center. Since influence and value may change over time, the stored/realtime information may be searched whenever a contact center application requires it. Queries done at the time of an inbound/outbound contact can enable the system to pick up on recent posts, accurate follower counts, and any other changes in critical data.
- the system utilizes realtime and stored data on each contact. Both inbound and outbound contacts may make use of this data.
- the system performs the data lookup and search. The result of compiling the information is a profile of the potential value and influence of this particular user. With this data, the system can make decisions about routing to a suitable agent, options for self-service, or any other treatment choices made by the contact center.
- Another key implementation of this method is to use the value profile with existing voice calls and other channels already in use today. By maintaining data reaching back into the social media or public data world, the real time influence/intent/value can be calculated and used for simple voice calls too.
- a Twitter post is ingested by the contact center from a user asking if anyone has flown CJet airlines and if they liked them.
- the method gathers recent posts by this user from Social Network sites. The analysis is run to see if this is a good natured or intent customer. Next a value number would be computed. If this customer had several posts discussing European Business Class flights, then the potential score would be high. Coupled with the intent score this may be a customer that CJet should reach out to offer special deals.
- a customer sends an email question requesting help.
- the system analyzes their social media posts and determines this is a happy customer that is well connected.
- the email is then quickly routed to an agent who composes a special email response to try and answer the problem but also offering a link to an online (without a wait) chat, if that is desired by the customer.
- the monitoring process ingests the message and analyzes the message and historical posts.
- the process sees the number of posts, the emotion of the posts, and the number of followers.
- the process determines that this is an “influential” customer and sends and @ reply message to the blogger about special CJet airline deals for them.
- each of the expressions “at least one of A, B and C”, “at least one of A, B, or C”, “one or more of A, B, and C”, “one or more of A, B, or C” and “A, B, and/or C” means A alone, B alone, C alone, A and B together, A and C together, B and C together, or A, B and C together.
- automated refers to any process or operation done without material human input when the process or operation is performed. However, a process or operation can be automatic, even though performance of the process or operation uses material or immaterial human input, if the input is received before performance of the process or operation. Human input is deemed to be material if such input influences how the process or operation will be performed. Human input that consents to the performance of the process or operation is not deemed to be “material”.
- Non-volatile media includes, for example, NVRAM, or magnetic or optical disks.
- Volatile media includes dynamic memory, such as main memory.
- Computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, or any other magnetic medium, magneto-optical medium, a CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, a solid state medium like a memory card, any other memory chip or cartridge, or any other medium from which a computer can read.
- the computer-readable media is configured as a database, it is to be understood that the database may be any type of database, such as relational, hierarchical, object-oriented, and/or the like. Accordingly, the invention is considered to include a tangible storage medium and prior art-recognized equivalents and successor media, in which the software implementations of the present invention are stored.
- module refers to any known or later developed hardware, software, firmware, artificial intelligence, fuzzy logic, or combination of hardware and software that is capable of performing the functionality associated with that element. Also, while the invention is described in terms of exemplary embodiments, it should be appreciated that individual aspects of the invention can be separately claimed.
- in communication with refers to any coupling, connection, or interaction using electrical signals to exchange information or data, using any system, hardware, software, protocol, or format.
- a user context, an extended user context, and/or a user social context as used herein means information about a user of a social media network that can be used to determine a “value” of that user.
- social media network or “social media” is a service provider that builds online communities of people, who share interests and/or activities, or who are interested in exploring the interests and activities of others.
- social media are web-based and provide a variety of ways for users to interact, such as e-mail and instant messaging services.
- FIG. 1 is a block diagram of an embodiment of a communication system operable to interact with persons using a social media network
- FIG. 2A is a block diagram of an embodiment of a social media gateway
- FIG. 2B is a block diagram of an embodiment of a dialog system
- FIG. 3 is a block diagram of an embodiment of a dialog data structure
- FIG. 4 is a block diagram of an embodiment of a customer value data structure
- FIG. 5 is a flow diagram of an embodiment of a process for creating a customer value data structure for a user of social media
- FIG. 6 is a flow diagram of an embodiment a process for generating a customer value for a user of a social media
- FIG. 7 is a flow diagram of an embodiment a process for modifying an interaction with a user of social media based on the customer value associated with the user;
- FIG. 8 is a block diagram of an embodiment of a computing environment.
- FIG. 9 is a block diagram of an embodiment of a computer system.
- the communication system 100 can include a contact center 102 , a network 108 , and one or more types of social media networks or systems, such as social media network 1 112 , social media network 2 114 , and/or social media network 3 116 .
- Social media networks 112 , 114 , and/or 116 can be any social media including, but not limited to, networks, websites, or computer enabled systems.
- a social media network may be MySpace, Facebook, Twitter, Linked-In, Spoke, or other similar computer enabled systems or websites.
- the communication system 100 can communicate with more or fewer social media networks 112 , 114 , and/or 116 than those shown FIG. 1 , as represented by ellipses 118 .
- the network 108 can be any network or system operable to allow communication between the contact center 102 and the one or more social media networks 112 , 114 , and/or 116 .
- the network 108 can represent any communication system, whether wired or wireless, using any protocol and/or format.
- the network 108 provides communication capability for the contact center 102 to communicate with websites or systems corresponding to the one or more social media networks 112 , 114 , and/or 116 .
- the network 108 can represent two or more networks, where each network is a different communication system using different communication protocols and/or formats and/or different hardware and software.
- network 108 can be a wide area network, local area network, the Internet, a cellular telephone network, or some other type of communication system.
- the network 108 may be as described in conjunction with FIGS. 8 and 9 .
- a contact center 102 can be a system that can communicate with one or more persons that use social media networking sites 112 , 114 , and/or 116 .
- the contact center 102 can be hardware, software, or a combination of hardware and software.
- the contact center 102 can be executed by one or more servers or computer systems, as described in conjunction with FIGS. 8 and 9 .
- the contact center 102 can include all systems, whether hardware or software, that allow the contact center 102 to receive, service, and respond to directed and non-directed contacts.
- the contact center 102 can include the telephone or email system, an interface to human agents, systems to allow human agents to service and respond to received contacts, and one or more systems operable to analyze and improve the function of agent interaction.
- the contact center 102 may include a dialog system 104 and a social media gateway 106 . While the dialog system 104 and the social media gateway 106 are shown as being a part of the contact system 102 , in other embodiments, the dialog system 104 and/or the social media gateway 106 are separate systems or functions executed separately from the contact center 102 and/or executed by a third party.
- the dialog system 104 may process and receive messages.
- the social media gateway 106 can receive and translate messages from the one or more social media networks 112 , 114 , and/or 116 .
- An embodiment of the dialog system 104 is described in conjunction with FIG. 2B .
- An embodiment of the social media gateway 106 is described in conjunction with FIG. 2A .
- the contact center 102 may also communicate with one or more communication devices 110 .
- the communication devices 110 can represent a customer's or user's cell phone, email system, personal digital assistant, laptop computer, or other device that allows the contact center 102 to interact with the customer.
- the contact center 102 can modify a non-direct contact, from a social media network 112 , 114 , and/or 116 , into a directed contact by sending a response message directly to a customer's communication device 110 .
- the social media gateway 106 can include one or more components which may include hardware, software, or combination of hardware and software.
- the social media gateway 106 can be executed by a computer system, such as those described in conjunction with FIGS. 7 and 8 .
- the components described in conjunction with FIG. 2A are logic circuits or other specially-designed hardware that are embodied in a field programmable gate array (FPGA) application specific integrated circuit (ASIC), or other hardware.
- FPGA field programmable gate array
- ASIC application specific integrated circuit
- the social media gateway 106 can include one or more content filters 202 a, 202 b, and/or 202 c.
- a content filter 202 can receive all of the messages for the contact center 102 from a social media network 112 , 114 , and/or 116 and eliminate or delete those messages that do not require a response. For example, a message between two friends on a Facebook page, if not pertaining to a product or a service of the company operating the contact center 102 , may not need a response.
- the content filter 202 can filter out or delete the non-suitable message from the messages that are received by the social media network application programming interface (API) 1 204 a, social media network API 2 204 b, and/or social media network API 3 204 c.
- the social media network API 204 only needs to translate those messages that should be received by the dialog system 104 . Translation typically requires the conversion of the message into a different format.
- the content filter 202 is provided with one or more heuristics for filter rules from a filter database (not shown). These filter rules can be created by the external customer or internal user (e.g. agent or administrator) of the communication system 100 . Thus, the user or customer of the communication system 100 can customize the filtering of messages from social media networks 112 , 114 , and/or 116 . Further, different rules may be applied to different social media networks 112 , 114 , and/or 116 , as some social media networks 112 , 114 , and/or 116 may have different types of messages or postings than other types of social media networks 112 , 114 , and/or 116 .
- the content filter 202 is shown as part of the social media gateway 106 , it is to be appreciated that the content filter 202 may be a part of the social media network API 204 .
- the content filter 202 may correspond to query terms used by the social media network API 204 .
- the content filter 202 or query terms are an argument to the social media network API 204 call.
- the social media network API 204 can be an application that the social media network 112 , 114 , and/or 116 provides to access the social media network 112 , 114 , and/or 116 .
- the social media network API 204 is called and connects the social media gateway 106 to the social media network 112 , 114 , and/or 116 .
- Any suitable filter criteria may be employed for social media API 204 . Examples of filter criteria include positive content of positive the source of posting, an address field, destination or recipient address fields, a time stamp field, a subject matter field, and a message body field.
- a type of searchable content can be name of the business enterprise running or employing the contact center 102 and/or the products or services of the enterprise.
- the social media gateway 106 can include one or more social media network APIs 204 . As shown in FIG. 2A , the social media gateway 106 may include a social media network API 204 for each social media network 112 , 114 , and/or 116 . As such, the social media gateway 106 can interact with each social media network 112 , 114 , and/or 116 in the particular (often unique) format or protocol used by the social media network 112 , 114 , and/or 116 . Further, when new social media networks are created, the social media gateway 106 can be easily expanded to interact with those social media networks by adding another social media network API 204 . Where social media networks 112 are more standardized, or use substantially similar formats or protocols, a single social media network API 204 can be shared by multiple social media networks 112 - 116 .
- the social media network API 204 can receive messages from and send messages to the social media network 112 , 114 , and/or 116 .
- the social media network API 204 can translate a message received from a social media network 112 , 114 , and/or 116 and send the translated message to a message filter 206 .
- the social media network API 204 can translate the received message into a standard formatted file.
- the translated message may be represented by an extensible mark-up language (XML) file or other file having a general format.
- XML extensible mark-up language
- the social media network API 204 can receive a generally or standard format response message, from the dialog system 104 , and translate that response into a particularly or specifically formatted response message that can be posted to the corresponding social media network 112 , 114 , and/or 116 .
- Messages to the contact center 102 are addressed to the contact center 102 .
- a customer may become a “friend” of the contact center 102 on a social media network 114 , such as Facebook.
- the customer may then address a message to the contact center 102 on Facebook.
- This non-direct contact is a message that is not sent directly to the contact center 102 but to the contact center's Facebook page.
- the contact center 102 receives messages not addressed to the contact center 102 .
- the contact center 102 can receive tweets from Twitter that are “broadcast” rather than addressed to the contact center 102 .
- the contact center 102 may also search for messages or content on the social media networks 112 , 114 , and/or 116 .
- Exemplary search criteria include customer name, customer profession, customer home address, customer business address, customer employer name, customer educational or professional background, customer hobby, personal or business interests, customer family profile, and the like.
- the social media gateway 106 of the contact center 102 can query, gather, or connect to a live feed of data from a social media network 112 , 114 , and/or 116 and then apply a filter to the indirect information.
- the social media network API 204 can also retrieve user context or other extended information from the social media networks 112 , 114 , and/or 116 .
- User context or other extended information can include historical posts, historical tweets, or other historical communications that a user may have received or sent. Further, user context or other extended information can include, but is not limited to, account information for a user, the user's followers or friends, information on where historical messages were posted (e.g., geo-location, time/date, what type of device, etc.), trending analysis that the social media network 112 , 114 , and/or 116 might provide the user, etc.
- the social media network API 204 can retrieve information that is associated with a user and a social media network 112 , 114 , and/or 116 but not necessarily a part of a current message.
- the social media network API 204 is a gatherer of data, which can be used to determine a value for the user of the social media networks 112 , 114 , and/or 116 .
- the translated messages from the social media network API 204 can be received by a message filter 206 .
- a message filter 206 can perform some or all of the functions of the content filter 202 and eliminate messages before being sent to the dialog system 104 .
- the message filter 206 eliminates information from within the messages before the redacted messages are sent to the dialog system 104 .
- a message from a social media network 112 may have three or four interactions between two parties not associated with the contact center 102 . Only one of the several postings may be pertinent to the dialog system 104 .
- the message filter 206 can eliminate or delete at least a portion of the other messages for the dialog system 104 .
- the dialog system 104 receives a message where some of the content of the message has been deleted.
- the message filter 206 can retrieve heuristics or filter rules from a filter database (not shown), similar to the content filter 202 .
- a substantial difference between the content and message filters 202 and 206 is that the content filter 202 is specific to a particular message format associated with a corresponding social media network 112 , 114 , and/or 116 , while the message filter 206 is applied to a standardized or universal format and is therefore common to multiple social media networks 112 , 114 , and/or 116 .
- One skilled in the art will understand the type of rules that may be used to filter information from messages such that only pertinent questions, facts, requests, or information is sent to the dialog system 104 .
- a message aggregator 208 may also be included with the social media gateway 106 .
- a message aggregator 208 can, in contrast to the message filter 206 , combine two or more messages into a packet or grouping that is sent to the dialog system 104 . Therefore, the message aggregator 208 can interrelate or combine messages based on information within the messages. For example, two messages may be combined based on any of the message fields referenced above, such as the person that posted the message, the subject, the request or question asked, the person the message was sent to, or other information that may be pertinent to the dialog system 104 . Thus, the dialog system 104 may be able to respond concurrently to two or more messages based on a grouping provided by the message aggregator 208 . Regardless of whether the messages are aggregated, each message or grouping of messages can be sent from the social media gateway 106 to the dialog system 104 .
- the social media gateway 106 can also send responses back to the social media networks 112 , 114 , and/or 116 .
- a response from an agent in the contact center 102 can be sent to the social media gateway 106 .
- the response may be in a general format and translated.
- the translated response may then be posted to the appropriate social media network 112 , 114 , and/or 116 by the social media gateway 106 .
- the agent may post the response directly to the social media network 112 , 114 , and/or 116 without sending the response to the social media gateway 106 .
- the dialog system 104 can include one or more components which may be hardware, software, or a combination of hardware and software.
- the dialog system 104 can be executed by a computer system such as those described in conjunction with FIGS. 8 and 9 .
- the components described in conjunction with FIG. 2B are logic circuits or other specially-designed hardware that are embodied in a FPGA or ASIC.
- the components contained within the dialog system 104 can include a dialog core 210 that is communication with a message history database 222 , an agent interface 224 , and a heuristic rules and dialogs database 218 . Further, the heuristic rules and dialogs database 218 can be in communication with a dialog creator 220 .
- the dialog core 210 can include one or more sub-components.
- the dialog core 210 can include a trend analysis component 212 , a text processing component 214 , and an analysis tools component 216 .
- These components similar to the components for the dialog system 104 , can be hardware, software, or combination of hardware and software.
- the dialog core 210 may step through the states of a dialog data structure.
- a dialog data structure can include a set of inputs and associated actions that can be taken which allow for the automatic and structured response to social media requests or messages. For example, if a user asks for a manual, the input of the text word “manual” can cause the dialog system 104 in accordance with a dialog data structure, to send information about one or more manuals.
- the receiver of the response may respond, in kind, with the selection of a certain user manual.
- the dialog data structure may then instruct the dialog core to send the user to a website where the user can retrieve an electronic version of the manual.
- the dialog data structure provides a script a dialog that allows the dialog core 210 to automate the interaction between the contact center 102 and a person. This automation eliminates the need for agent involvement, in some situations, and makes the contact center 102 more efficient and more effective. Further, the automation expands the contact center's ability to answer numerous messages from the plethora of postings on the numerous social media networks 112 , 114 , and/or 116 .
- the dialog creator 220 will create a dialog data structure 300 that includes instructions for various states for each social media message that comes into the contact center 102 .
- the first instruction might be to send the social media message to the trend analysis component 212 , then to the text processing component 214 , and then execute a query of a Customer Relationship Management (CRM) database 232 (to determine if this user has an existing order).
- CRM Customer Relationship Management
- a CRM database 232 can be a database as described in conjunction with FIGS. 8 & 9 and can store information about customers or other data related to customer relations.
- the dialog data structure 220 might decide that the social media message should be sent to a human agent 228 for processing.
- the instructions or node transitions are executed in the dialog core 210 and make use of many different components that the dialog creator 220 combines in any way the user desires to handle the social media messages.
- the dialog core 210 can make use of the trend analysis component 212 , text processing component 214 , or other systems.
- the dialog core 210 may also interface with a CRM system and/or database 232 , external databases, social media user information (e.g., followers, friends, post history, etc. from the social media site), or other systems.
- the trend analysis component 212 is operable to analyze trends that occur between two or more messages received by the social media networks 112 , 114 , and/or 116 .
- the two messages can be from different social media networks, so that the trend analysis component 212 can identify trends across several different social media networks 112 , 114 , and/or 116 .
- Trends can include multiple occurrences of the same word or phrase, multiple occurrences of a customer identity, product name or service, or multiple occurrences of some other information that might indicate a trend.
- the trend analysis component 212 may be able to identify escalations in the occurrences of particular text, identities, or other information, or may identify multiple occurrences over a period of time.
- the trend analysis component 212 may also be able to apply one or more different algorithms to occurrences of information within the social media networks 112 , 114 , and/or 116 .
- the trend analysis component 212 can match the number of occurrences of a phrase or word over a period of time and apply analysis to determine if the occurrences are increasing or decreasing over the period of time.
- the text processing component 214 is operable to analyze text of one or more messages from social media networks 112 , 114 , or 116 .
- Some possible methods for text processing can include Regular Expression, Latent Semantic Indexing (LSI), text part of speech tagging, text clustering, N-Gram document analysis, etc.
- the text processing component 214 may execute one or more methods of document summarization.
- the summarization may occur if the social media message will be sent to an agent 228 of the contact center 102 ; the summarization can reduce the amount of information that the agent 228 may manage.
- the text processing rules or models may be stored in and/or retrieved from a text processing rules database 230 .
- the text processing rules database 230 can be a database as described in conjunction with FIGS. 7 and 8 that stores rules or models used by the text processing component 214 .
- the text processing component 214 can identify one or more occurrences of a particular text, such as using one or more of the message fields referenced above, in order to associate that social media message with one or more dialogs data structures in the heuristic rules and dialog database 218 . For example, the text processing component 214 can look for the word “manual,” in the social media message. If the word “manual” is found, the text processing component 214 may retrieve a dialog data structure from the heuristic rules and dialogs database 218 and, as the dialog data structure instructs, communicate with the customer about one or more owner's manuals, repair manuals, or other types of manuals.
- the text processing component 214 can retrieve one or more dialog data structures from the heuristic rules and dialogs database 218 that can provide instruction to assist the customer in purchasing products or services from the enterprise.
- the analysis tools component 216 is operable to analyze response messages received back from an agent interface 224 . In analyzing the agent's responses, the analysis tools component 216 can determine if the dialog data structures 300 ( FIG. 3 ) originally retrieved by the text processing component 214 met the needs of the customer. In the analysis, the agent 228 may enter one or more items of information, for the analysis tools component 216 , about the response and about how the response matched with the dialog data structures 300 . The analysis tools component 216 can review the response and determine if it was similar to the response provided by the dialog data structure 300 ( FIG. 3 ). Thus, the analysis tools component 216 can provide information to the dialog core 210 or the dialog creator 220 to improve the dialog data structures 300 ( FIG. 3 ) that are included in the heuristic rules and dialogs database 218 .
- the message history database 222 can be any database or data storage system as described in conjunction with FIGS. 8 and 9 .
- the message history database 222 can store data in data fields, objects, or other data structures to allow other systems to retrieve that information at a later time.
- the message history database 222 can store previous messages or information about previous messages.
- the trend analysis component 212 can retrieve information about previous messages associated with the current analysis from the message history database 222 .
- the trend analysis component 212 can better detect trends occurring at the social media networks 112 , 114 , and/or 116 .
- the data stored by the message history database 222 can include the entire message or only a portion of the message, and in some circumstances, include metadata about the message(s).
- the heuristic rules and dialogs database 218 can be any type of database or data storage system as described in conjunction with FIGS. 8 and 9 .
- the heuristic rules and dialogs database 218 can store information in data fields, data objects, and/or any other data structures. An example of information stored within the heuristic rules and dialogs database 218 is described in conjunction with FIG. 3 .
- the heuristic rules and dialogs database 218 stores rules and dialogs data structures that automate responses to received social media messages.
- the dialogs data structures control the interaction between the dialog core 210 and the social media network 112 , 114 , and/or 116 .
- the dialogs or heuristic rules can be created by a dialog creator 220 .
- the dialog creator 220 can interface with user input 226 to receive information about dialogs.
- the user input 226 is then used to form the states and responses for a dialog data structure.
- An agent interface 224 is a communication system operable to send action items to contact center agents 228 , in the contact center 102 .
- An agent can be a person or other system that is operable to respond to certain questions or requests from a customer.
- the agent 228 can be a person that has specialized expertise in a topic area, such as technical support.
- the agent interface 224 can format the social message into an action item and forward that message to one or more agents 228 .
- the agent interface 224 can also receive response(s) back from the agents 228 .
- the information provided by the agent 228 may be used by the dialog core 210 to complete a response to the social media message. For example, the information may classify the social media message (e.g., sales, service, etc.). In other embodiments, the response is a complete response to the social media message that can be posted to the social media network 112 , 114 , and/or 116 .
- the dialog data structure 300 can be stored in several different forms of databases, such as relational databases, flat files, object-oriented databases, etc.
- data field or “segment” is used herein, the data may be stored in an object, an attribute of an object, or some other form of data structure.
- the dialog data structure 300 can be stored, retrieved, sent, or received during the processing of dialogs by the dialog core 210 or the dialog creator 220 .
- the dialog data structure 300 stores one or more items of information in one or more segments.
- the numeric identifiers (e.g. 302 , 304 , etc.) shown in FIG. 3 can identify, the one or more segments.
- the dialog data structure 300 can include one or more input segments, such as, input segment 1 302 and input segment 2 304 , a rules segment 306 , and/or a dialog script segment 308 .
- Input segments 302 and 304 each include one or more inputs that may be required to associate a social media message with the dialog data structure 300 .
- the inputs segments 302 and 304 may include a customer identity, a respective customer type, a text word, a phrase, or other information that indicates that the dialog data structure 300 is associated with or pertaining to the social media messages.
- the input segments 302 and 304 may also include certain trends that the trend analysis component 212 can identify. As such, if a trend is identified and associated with the inputs 302 and/or 304 , the dialog data structure 300 can be retrieved and used by the dialog core 210 . While there are only two input segments 302 and 304 shown in FIG. 3 , there may be more or fewer input segments associated with the dialog data structure 300 , as indicated by ellipses 310 .
- the rules segment 306 can include one or more heuristic rules that either help with the association of the respective dialog data structure 300 with the social media message or control the interaction between the dialog core 210 and the social media customer.
- the rule 306 can state that the dialog data structure 300 applies only if the social media message includes input segment 1 302 but not input segment 2 304 .
- One skilled in the art will be able to identify other types of rules that may govern the association of the dialog data structure 300 with the social media message.
- the rules segment 306 states that if the social media message includes inputs 302 and/or 304 , then the dialog core 210 should respond with a certain type of action.
- a dialog script segment 308 includes a script of actions or responses that direct one or more other components, such as the dialog core 210 ( FIG. 2B ), to conduct actions or send the responses.
- the dialog script segment 308 can include the one or more states and corresponding responses or actions required by the dialog core 210 . If the dialog script segment 308 applies (that is, if the social media message is requesting a certain type of information), the dialog script segment 308 may include the one or more responses that the dialog core 210 should communicate to respond to that social media message.
- the dialog script segment 308 can also include a response and a pointer to another dialog script segment 308 or another dialog data structure 300 .
- dialog script segment 308 may have one or more actions that may be taken by another component after a secondary response is received by a customer.
- the dialog script segment 308 can direct or instruct an interaction to continue with a social media user over a period of time and over several interactions between the user and the contact center 102 .
- dialog script segment 308 can reference one or more other dialog data structures 300 .
- the dialog script segment 308 can direct the dialog core 210 to reference at least one other dialog data structure 300 to further act on the social media message.
- the social media message can be subject of two or more dialog script segments 308 , and direct the dialog core 210 to complete two dialog script segments 308 on the social media message.
- dialog script segments 308 may not be associated with a response but direct the dialog core 210 to complete other actions, such as populating databases or gathering information.
- This customer value profile 400 can include one or more data segments that contain one or more criteria.
- data segments 402 through 404 and 406 can each include a type of criteria, a score, a weight, or other information.
- criteria 1 408 can be the criteria that is being evaluated.
- a criteria can be a parameter, metadata, or other information from a social media network or from another source, such as a public data source.
- this criteria can include the number of followers or friends the user has on a social media network 112 , a number of posts a user makes during a pre-determined period of time (e.g.
- a tenor can be a mood or some other valuation of the text within a posting or response.
- Data segment 402 can also include a weight 410 .
- a weight segment 410 can include a weight 410 that can be a measure of the importance of the criteria in determining the customer value score.
- a weight 410 can be a multiplier, whether the multiplier is a decimal or an integer, which changes the value of the criteria score. The weight may be provided in comparison to other weights.
- one criteria may be more important than another, which is reflected in the weight value.
- the number of friends or followers may be more important than the number of responses the user makes to other postings. As such, the weight for the number of friends or followers can be greater than the weight for the number of responses.
- the data segment 402 can also include a score segment 412 .
- the score segment 412 can include a score that can include the algorithm used to score the criteria. For example, a criteria may include the number of followers the user has and that number of followers may be stored in the criteria segment 408 . However, to score that, the score may include an algorithm which determines the number of standard deviations from a mean number of followers for that social media network. For example, if the mean user has 100 friends on Facebook, and the current user has 1000 friends, they may be two standard deviations from the mean. As such, the score may be determined by a number of standard deviations. For example, if a user is two standard deviations from the mean, the number of 2 standard deviations from the mean may result in a score of 10.
- the actual score may also be stored in the score segment 412 .
- a weighed score segment 414 may store the result of the computation of the weight and the score. As such, the weighted score includes the total score for that criteria.
- the total value score segment 418 can store the sum of all weighted scores added. The total value score 418 represents the value of this customer or the customer profile.
- FIG. 5 An embodiment of a method 500 to create a customer value data structure is shown in FIG. 5 .
- the method 500 begins with a start operation 502 and terminates with an end operation 518 . While a general order for the steps of the method 500 are shown in FIG. 5 , the method 500 can include more or fewer steps or arrange the order of the steps differently than those shown in FIG. 5 .
- the method 500 can be executed as a set of computer-executable instructions executed by a computer system and encoded or stored on a computer readable medium.
- the method 500 shall be explained with reference to the systems, components, modules, software, data structures, etc. described in conjunction with FIGS. 1-4 .
- a contact center 102 can receive an identity, in step 504 .
- An identity can be a user name, a name, an address, a cell phone number, an email address, or some other identifying information for a person.
- the identity can include a profile of two or more people.
- the profile can be of any person that may desire a certain product or service.
- the profile may also be for any customer using social media.
- the dialog creator 220 can receive the profile or the identity.
- the dialog creator 220 may also receive the one or more social media networks, 112 , 114 , and/or 116 that need to be analyzed for users to determine customer value.
- the networks to be analyzed can be received as user input 228 at the dialog creator 220 .
- the different social networks to analyze can be social network 1 112 , 2 114 , and/or 3 116 .
- the social networks may be Facebook, Twitter, Spoke, etc.
- the dialog creator 220 can also receive one or more criteria, in step 508 .
- the criteria can apply to the criteria segment 408 and the customer profile 400 .
- the standard criteria can determine a general customer value without specifics received from the user.
- the dialog creator 220 may also receive one or more weightings for the criteria, in step 510 .
- the weights or weightings can be a multiplier for the criteria.
- the weightings may be a decimal number or an integer that is multiplied to the criteria to give a weighted score. In other embodiments, it may be a portion of a 100%, such as 25%.
- the percentage can be a reflection of the value of the criteria amongst all other criteria, such that adding all the percentage weights of all the criteria equals 100%.
- the dialog creator 220 can receive, as a user input 228 , one or more scoring algorithms, in step 512 .
- the scoring algorithms can be a mathematical function for how the criteria are calculated. For example, criteria may be calculated by the distance above or below, mean value. For example, a criteria can be the number of standard deviations away from a mean. For example, if a user has 200 friends or followers on Facebook and the mean is 100, that user may be one standard deviation above the mean value. The one standard deviation value can be a score or may be scored on another scale. In other embodiments, the algorithms can differently calculate scores for different criteria using different mathematical functions.
- a campaign value parameter can be a parameter that defines how values are going to be determined for one or more people.
- the value campaign can instruct which social networks 112 , 114 , 116 to investigate, can identify from dates to use for evaluating, may have other types of parameters that define how a value determination is made for one or more people.
- the criteria used to analyze identities and profiles, weights, algorithms, and value campaign parameters may be stored in a dialog data structure 300 in step 516 .
- the campaign parameters can be stored in the rules segment 306 , meanwhile, the identity or profiles can be stored in the user input segment 302 or 304 .
- the other received information, such as the social networks to analyze, the criteria, the weights, and the algorithms may be stored in a dialog script that includes a customer value data structure 400 in the dialog script segment 308 .
- a dialog system 104 can read the dialog script and be directed to applying the algorithms to one or more criteria to determine scores and then calculating the weighted score by multiplying the criteria scores with the weight.
- FIG. 6 An embodiment of a method 600 for determining a customer value is shown in FIG. 6 .
- the method 600 begins with a start operation 602 and terminates with an end operation 618 . While a general order for the steps of the method 600 are shown in FIG. 6 , the method 600 can include more or fewer steps or arrange the order of the steps differently than those shown in FIG. 6 .
- the method 600 can be executed as a set of computer-executable instructions executed by a computer system and encoded or stored on a computer readable medium.
- the method 600 shall be explained with reference to the systems, components, modules, software, data structures, etc. described in conjunction with FIGS. 1-4 .
- a contact center 102 can receive an identity or profile, in step 604 .
- An identity can include any identifying information for a person such as a name, a user name, an address, a phone number, or other data that may be used to identify a person using a social media network 112 , 114 or 116 .
- a profile is provided, for example, anyone using Facebook, any female between the ages of 18 through 30, or other types of profiles.
- the dialog system 104 can retrieve a data dialog structure 300 from the heuristic rules and dialogs database 216 , in step 606 .
- the contact center 100 searches the heuristic rules and dialogs database 216 for a dialog data structure 300 that includes the identity or profile stored in the input segment 302 or 304 .
- the dialog system 104 retrieves a value campaign data structure 400 and inserts the identity or profile in the input segment 302 or 304 .
- the dialog core 210 of the dialog system 104 can read the dialog script in a dialog data structure 300 .
- the dialog script can direct the dialog core 210 to access one or more accounts for a social media user associated with the identity or profile, in step 608 , to obtain a user context, an extended user context, and/or a user social context.
- the dialog core 210 can retrieve information to access the social media account from a CRM database 232 a message history database 222 , or some other source or may directly access the social media account in the social media network 112 114 , and/or 116 .
- the dialog core 210 reads information about the user from the social media account. The information retrieved can describe the person or their usage of the social media account.
- the dialog core 210 may retrieve the number of followers the user has on Facebook, may retrieve former messages from a message history on Facebook, or may retrieve and read other information available in the social media network 112 , 114 , and/or 116 .
- the dialog core 210 can determine a number of hits on a blog, page, or video blog to determine values.
- Trend analysis component 212 reads and evaluates the parameters, in step 610 .
- information that is required for the dialog script is retrieved from the social media account and stored in a temporary data structure by the dialog core 210 .
- the parameters are then evaluated to determine a value score.
- a dialog core 210 can read the algorithms from the dialog script and apply those algorithms to the parameters to obtain a score in step 612 .
- the dialog core 210 executes a mathematical function to determine the value of the parameters. The mathematical function will provide a score for the parameter.
- Dialog core 210 may then apply a weight to the score, in step 614 .
- the dialog core 210 can read the weighting from the dialog script and multiply the weight by the value of the parameter determined in step 612 .
- the result of the applying the weighting is a weighted value score.
- a value weighted score is created.
- a dialog core 210 may then calculate a value score for the user by adding the two or more weighted value scores together in step 616 . This summation produces a total value score for that user.
- FIG. 7 An embodiment of a method 700 to create a customer value data structure is shown in FIG. 7 .
- the method 700 begins with a start operation 702 and terminates with an end operation 710 . While a general order for the steps of the method 700 are shown in FIG. 7 , the method 700 can include more or fewer steps or arrange the order of the steps differently than those shown in FIG. 7 .
- the method 700 can be executed as a set of computer-executable instructions executed by a computer system and encoded or stored on a computer readable medium.
- the method 700 shall be explained with reference to the systems, components, modules, software, data structures, etc. described in conjunction with FIGS. 1-4 .
- a contact center 102 can receive an identity, in step 704 .
- the identity can be an identity of a user of a social media network 112 , 114 , and/or 116 .
- An identity identifies the user and can include a user name, a name, an address, a phone number, other contact information, or other information that can uniquely identify the user.
- the contact center 102 can retrieve a customer value score, in step 707 .
- the customer value score may be stored in a CRM database 232 or a message history database 222 and be available for retrieval by the contact center 102 .
- the customer value score can represent a value with that user for the identity received in step 704 .
- the contact center 102 then may modify an interaction with that user based on the customer value score, in step 708 .
- the contact center 102 may provide a higher level of customer service, such as, present an agent 228 to interact with the customer who has more skill or more knowledge about a typical subject.
- the customer may be subject to a higher level of interaction including more messages sent to the user in order to gain the user's attention or affiliation with the enterprise.
- a customer value score an enterprise can better direct resources to the customers that may have the greatest potential for consuming products or promoting those products to other consumers.
- FIG. 8 illustrates a block diagram of a computing environment 800 that may function as servers, computers, or other systems provided herein.
- the environment 800 includes one or more user computers 805 , 810 , and 815 .
- the user computers 805 , 810 , and 815 may be general purpose personal computers (including, merely by way of example, personal computers, and/or laptop computers running various versions of Microsoft Corp.'s WindowsTM and/or Apple Corp.'s MacintoshTM operating systems) and/or workstation computers running any of a variety of commercially-available UNIXTM or UNIX-like operating systems.
- These user computers 805 , 810 , 815 may also have any of a variety of applications, including for example, database client and/or server applications, and web browser applications.
- the user computers 805 , 810 , and 815 may be any other electronic device, such as a thin-client computer, Internet-enabled mobile telephone, and/or personal digital assistant, capable of communicating via a network 820 and/or displaying and navigating web pages or other types of electronic documents.
- a thin-client computer such as a thin-client computer, Internet-enabled mobile telephone, and/or personal digital assistant, capable of communicating via a network 820 and/or displaying and navigating web pages or other types of electronic documents.
- exemplary computer environment 800 is shown with three user computers, any number of user computers may be supported.
- the Environment 800 further includes a network 820 .
- the network 820 may be any type of network familiar to those skilled in the art that can support data communications using any of a variety of commercially-available protocols, including without limitation SIP, TCP/IP, SNA, IPX, AppleTalk, and the like.
- the network 820 maybe a local area network (“LAN”), such as an Ethernet network, a Token-Ring network and/or the like; a wide-area network; a virtual network, including without limitation a virtual private network (“VPN”); the Internet; an intranet; an extranet; a public switched telephone network (“PSTN”); an infra-red network; a wireless network (e.g., a network operating under any of the IEEE 802.11 suite of protocols, the BluetoothTM protocol known in the art, and/or any other wireless protocol); and/or any combination of these and/or other networks.
- LAN local area network
- VPN virtual private network
- PSTN public switched telephone network
- wireless network e.g., a network operating under any of the IEEE 802.11 suite of protocols, the BluetoothTM protocol known in the art, and/or any other wireless protocol
- the system may also include one or more server 825 , 830 .
- server 825 is shown as a web server and server 830 is shown as an application server.
- the web server 825 which may be used to process requests for web pages or other electronic documents from user computers 805 , 810 , and 815 .
- the web server 825 can be running an operating system including any of those discussed above, as well as any commercially-available server operating systems.
- the web server 825 can also run a variety of server applications, including SIP servers, HTTP servers, FTP servers, CGI servers, database servers, Java servers, and the like. In some instances, the web server 825 may publish operations available operations as one or more web services.
- the environment 800 may also include one or more file and or/application servers 830 , which can, in addition to an operating system, include one or more applications accessible by a client running on one or more of the user computers 805 , 810 , 815 .
- the server(s) 830 and/or 825 may be one or more general purpose computers capable of executing programs or scripts in response to the user computers 805 , 810 and 815 .
- the server 830 , 825 may execute one or more web applications.
- the web application may be implemented as one or more scripts or programs written in any programming language, such as JavaTM, C, C#TM, or C++, and/or any scripting language, such as Perl, Python, or TCL, as well as combinations of any programming/scripting languages.
- the application server(s) 830 may also include database servers, including without limitation those commercially available from Oracle, Microsoft, SybaseTM, IBMTM and the like, which can process requests from database clients running on a user computer 805 .
- the web pages created by the server 825 and/or 830 may be forwarded to a user computer 805 via a web (file) server 825 , 830 .
- the web server 825 may be able to receive web page requests, web services invocations, and/or input data from a user computer 805 and can forward the web page requests and/or input data to the web (application) server 830 .
- the web server 830 may function as a file server.
- FIGS. 1-4 illustrate a separate web server 825 and file/application server 830 , those skilled in the art will recognize that the functions described with respect to servers 825 , 830 may be performed by a single server and/or a plurality of specialized servers, depending on implementation-specific needs and parameters.
- the computer systems 805 , 810 , and 815 , web (file) server 825 and/or web (application) server 830 may function as the system, devices, or components described in FIGS. 1-4 .
- the environment 800 may also include a database 835 .
- the database 835 may reside in a variety of locations.
- database 835 may reside on a storage medium local to (and/or resident in) one or more of the computers 805 , 810 , 815 , 825 , 830 . Alternatively, it may be remote from any or all of the computers 805 , 810 , 815 , 825 , 830 , and in communication (e.g., via the network 820 ) with one or more of these.
- the database 835 may reside in a storage-area network (“SAN”) familiar to those skilled in the art.
- SAN storage-area network
- any necessary files for performing the functions attributed to the computers 805 , 810 , 815 , 825 , 830 may be stored locally on the respective computer and/or remotely, as appropriate.
- the database 835 may be a relational database, such as Oracle 10iTM, that is adapted to store, update, and retrieve data in response to SQL-formatted commands.
- FIG. 9 illustrates one embodiment of a computer system 900 upon which the servers, computers, or other systems or components described herein may be deployed or executed.
- the computer system 900 is shown comprising hardware elements that may be electrically coupled via a bus 955 .
- the hardware elements may include one or more central processing units (CPUs) 905 ; one or more input devices 910 (e.g., a mouse, a keyboard, etc.); and one or more output devices 915 (e.g., a display device, a printer, etc.).
- the computer system 900 may also include one or more storage devices 920 .
- storage device(s) 920 may be disk drives, optical storage devices, solid-state storage devices such as a random access memory (“RAM”) and/or a read-only memory (“ROM”), which can be programmable, flash-updateable and/or the like.
- RAM random access memory
- ROM read-only memory
- the computer system 900 may additionally include a computer-readable storage media reader 925 ; a communications system 930 (e.g., a modem, a network card (wireless or wired), an infra-red communication device, etc.); and working memory 940 , which may include RAM and ROM devices as described above.
- the computer system 900 may also include a processing acceleration unit 935 , which can include a DSP, a special-purpose processor, and/or the like.
- the computer-readable storage media reader 925 can further be connected to a computer-readable storage medium, together (and, optionally, in combination with storage device(s) 920 ) comprehensively representing remote, local, fixed, and/or removable storage devices plus storage media for temporarily and/or more permanently containing computer-readable information.
- the communications system 930 may permit data to be exchanged with the network 820 ( FIG. 8 ) and/or any other computer described above with respect to the computer system 900 .
- the term “storage medium” may represent one or more devices for storing data, including read only memory (ROM), random access memory (RAM), magnetic RAM, core memory, magnetic disk storage mediums, optical storage mediums, flash memory devices and/or other machine readable mediums for storing information.
- the computer system 900 may also comprise software elements, shown as being currently located within a working memory 940 , including an operating system 945 and/or other code 950 . It should be appreciated that alternate embodiments of a computer system 900 may have numerous variations from that described above. For example, customized hardware might also be used and/or particular elements might be implemented in hardware, software (including portable software, such as applets), or both. Further, connection to other computing devices such as network input/output devices may be employed.
- machine-executable instructions may be stored on one or more machine readable mediums, such as CD-ROMs or other type of optical disks, floppy diskettes, ROMs, RAMs, EPROMs, EEPROMs, magnetic or optical cards, flash memory, or other types of machine-readable mediums suitable for storing electronic instructions.
- machine readable mediums such as CD-ROMs or other type of optical disks, floppy diskettes, ROMs, RAMs, EPROMs, EEPROMs, magnetic or optical cards, flash memory, or other types of machine-readable mediums suitable for storing electronic instructions.
- the methods may be performed by a combination of hardware and software.
- a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged.
- a process is terminated when its operations are completed, but could have additional steps not included in the figure.
- a process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination corresponds to a return of the function to the calling function or the main function.
- embodiments may be implemented by hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof.
- the program code or code segments to perform the necessary tasks may be stored in a machine readable medium such as storage medium.
- a processor(s) may perform the necessary tasks.
- a code segment may represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a class, or any combination of instructions, data structures, or program statements.
- a code segment may be coupled to another code segment or a hardware circuit by passing and/or receiving information, data, arguments, parameters, or memory contents. Information, arguments, parameters, data, etc. may be passed, forwarded, or transmitted via any suitable means including memory sharing, message passing, token passing, network transmission, etc.
- the contact center system 102 can also access public sources of data to further enhance the customer value score.
- a contact center 102 may access public records about addresses for the user. From that information, the contact center 102 may determine a possible income level or wealth level for the user. As such, the contact center 102 can determine if the customer is likely to buy or purchase a product. Other information may be accessed from other data sources and applied to the customer value score. As such, the customer value score can reflect a total more robust determination of the value of the customer to the enterprise.
- the nature of the parameters used to determine the value score of the customer value score or the customer value score itself may be temporal.
- the weight associated with a parameter may be dynamic and decay based on the age of the parameter value. Decaying may be accomplished using a leaky integrator approach that can function as a moving average. The size of the integration window can be adjusted to highlight the trends without including too much noise. There can be multiple dimensions to any trend “tracking” that can be included as modifiers. For example, the weight of the score may decrease 5% every week based on the age of a parameter.
- the customer value score can also be dynamic and change based on age or other temporal, geographic, or other information about parameters of the customer value score. Further, the customer value score may also change depending on the time of day, time of week, time of month or year. For example, a user may have a higher customer value score during the work week or work day compared to nights or weekends.
- the dialog core 210 may detect trending by a customer or user.
- Each individual social media user may be “trending” on a topic. For example, “Bob” occasionally talks (tweets/facebook posts) about possible summer vacation plans, but, starting in the month of March, the frequency of mentions for “summer vacation” increases from 0.5 mentions per month across all social media networks 112 , 114 , and/or 116 to 3 mentions per month across all social media networks 112 , 114 , and/or 116 . The increase may be a positive short term trend for an individual user that may trigger a value assessment that “Bob” should be targeted for a summer travel outreach campaign.
- a baseline of activity on a topic may be determined then the dialog core 210 can notice significant changes (i.e., statistically significant changes) that may be valuable information for a business.
- significant changes i.e., statistically significant changes
- trends are meaningful with an understanding of what the base “noise” level is for a social media user.
- a trend for one user may be compared to the trends of other users, either as individuals or as aggregated groups.
- threshold levels i.e., the levels at which a customer becomes valuable
- a major media outlet e.g., Time Magazine
- the average level of conversation on time share condos for vacation may increase across a large body of users.
- An average number of mentions, for the whole of social media users, of time shares may increase.
- trying to determine a valuable customer may not function with an unadjusted threshold because too many people may go over the threshold.
- the dialog core 210 may automatically adjust the threshold based on the actions of the whole of social media users to cull out those users that post in excess of the larger general trend.
- the average number of mentions can fluctuate with external factors. Users that exceed the average number of mentions per unit time might be showing greater interest or potential. This analysis optimizes the use of contact center resources in attempting to connect to interested/interesting/influential customers.
Abstract
Description
- This application claims priority to U.S. Provisional Application Ser. No. 61/263,013, filed Nov. 20, 2009, entitled “GEO POD SYSTEM,” which is incorporated herein by reference in its entirety.
- Contact centers generally exchange information with consumers through directed contacts. Directed contacts consist of emails, phone calls, or other forms of communication that are directed to the contact center or the consumer. However, many people today, exchange information or interact through non-direct methods. Non-direct communications require users to post communications to third party sites or forums, but not to direct those communications to a specific person or organization. Non-direct communication methods include social media, which may include websites, networks, blogs, micro-blogs, RSS feeds, social media websites (such as, Linked-In, Facebook, Twitter, MySpace, etc.), and other types of social media. Generally, it is not possible for contact centers to communicate with consumers through non-direct methods. As such, the contact centers may be unable to interact with consumers that use social media to offer certain types of customer service.
- Further, contact centers have limited amounts of resources. As such, to be efficient, and to drive down costs, contact centers like to use resources only on customers that have a likely chance of buying products or responding to an interaction. As such, contact centers would like to project where they may use their resources best. Unfortunately, there are no current ways of deducing whether marketing efforts are directed to a high value customer when that customer is using social media.
- It is with respect to the above issues and other problems that the embodiments presented herein were contemplated. Embodiments presented herein provide systems and methods for determining the value of a customer that uses social media to communicate. An enterprise that desires to understand the value of a customer can provide an identity or identities of customers or provide a profile of one or more customers to a
contact center 102. Further, the enterprise can provide one or more criteria in which the enterprise wants to base the customer value. The criteria can include such things as the number of friends or followers for that user on a social media site, the number of posts the user creates in a pre-determined period of time (e.g. hour, day, week, etc.), the number of responses to the user's postings, the tenor of the user's postings, and other data. The different parameters can be scored to provide a score for each parameter, then summed to create a customer value. - In further embodiments, the parameters associated with the social media site can also be combined with other data from public or other sources. For example, a determination of the wealth or income of the user can be made, for example, in the neighborhood from which the user lives, or from other data. The additional data and social media data can be combined to create a more comprehensive customer value.
- A Social Media Gateway can be used to gather information on specific social network users. In addition, data may be collected about the user that may exist on public blogs, wikis, etc. Finally, data sources with public information, e.g., census, zillow.com, etc., may be used to gather more user information.
- The system can create a value of the source or destination user. The value profile is generated from information on the above mentioned sites. A key aspect of the value profile is the derivative information, or how influential the user might be. Information about number of followers (twitter), number of friends (Facebook), blog readers, etc. help to determine how far the user's influence may spread, thus increasing the customer's value. Adding to the influence attribute can be an activity factor. If a user posts a lot and has a lot of replies in return, then they may be a more valuable customer that would spread information through that influence. Finally, text analysis could be used to determine the general emotion of the posts and responses. A user who has upbeat and happy posts might be more likely to spread good words about an enterprise.
- To obtain this information, the system can utilize both stored data and realtime search data. Stored data may include information that is discovered, including: social network accounts, blog sites, demographic data, etc. The discovered can be information that does not change very often. A complete picture may be generated over time as different modes of communication are used. The realtime data can be queried as needed by the contact center. Since influence and value may change over time, the stored/realtime information may be searched whenever a contact center application requires it. Queries done at the time of an inbound/outbound contact can enable the system to pick up on recent posts, accurate follower counts, and any other changes in critical data.
- The system utilizes realtime and stored data on each contact. Both inbound and outbound contacts may make use of this data. When a user interaction is identified as being of interested by the contact center (see previous base level patent application), the system performs the data lookup and search. The result of compiling the information is a profile of the potential value and influence of this particular user. With this data, the system can make decisions about routing to a suitable agent, options for self-service, or any other treatment choices made by the contact center.
- Another key implementation of this method is to use the value profile with existing voice calls and other channels already in use today. By maintaining data reaching back into the social media or public data world, the real time influence/intent/value can be calculated and used for simple voice calls too.
- Following are a few example use cases. A Twitter post is ingested by the contact center from a user asking if anyone has flown CJet airlines and if they liked them. The method gathers recent posts by this user from Social Network sites. The analysis is run to see if this is a good natured or intent customer. Next a value number would be computed. If this customer had several posts discussing European Business Class flights, then the potential score would be high. Coupled with the intent score this may be a customer that CJet should reach out to offer special deals.
- A customer sends an email question requesting help. The system analyzes their social media posts and determines this is a happy customer that is well connected. The email is then quickly routed to an agent who composes a special email response to try and answer the problem but also offering a link to an online (without a wait) chat, if that is desired by the customer.
- A well connected blogger Tweets a question about CJet airlines. The monitoring process ingests the message and analyzes the message and historical posts. The process sees the number of posts, the emotion of the posts, and the number of followers. The process determines that this is an “influential” customer and sends and @ reply message to the blogger about special CJet airline deals for them.
- The phrases “at least one”, “one or more”, and “and/or” are open-ended expressions that are both conjunctive and disjunctive in operation. For example, each of the expressions “at least one of A, B and C”, “at least one of A, B, or C”, “one or more of A, B, and C”, “one or more of A, B, or C” and “A, B, and/or C” means A alone, B alone, C alone, A and B together, A and C together, B and C together, or A, B and C together.
- The term “a” or “an” entity refers to one or more of that entity. As such, the terms “a” (or “an”), “one or more” and “at least one” can be used interchangeably herein. It is also to be noted that the terms “comprising”, “including”, and “having” can be used interchangeably.
- The term “automatic” and variations thereof, as used herein, refers to any process or operation done without material human input when the process or operation is performed. However, a process or operation can be automatic, even though performance of the process or operation uses material or immaterial human input, if the input is received before performance of the process or operation. Human input is deemed to be material if such input influences how the process or operation will be performed. Human input that consents to the performance of the process or operation is not deemed to be “material”.
- The term “computer-readable medium” as used herein refers to any tangible storage that participates in providing instructions to a processor for execution. Such a medium may take many forms, including but not limited to, non-volatile media, volatile media, and transmission media. Non-volatile media includes, for example, NVRAM, or magnetic or optical disks. Volatile media includes dynamic memory, such as main memory. Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, or any other magnetic medium, magneto-optical medium, a CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, a solid state medium like a memory card, any other memory chip or cartridge, or any other medium from which a computer can read. When the computer-readable media is configured as a database, it is to be understood that the database may be any type of database, such as relational, hierarchical, object-oriented, and/or the like. Accordingly, the invention is considered to include a tangible storage medium and prior art-recognized equivalents and successor media, in which the software implementations of the present invention are stored.
- The terms “determine”, “calculate”, and “compute,” and variations thereof, as used herein, are used interchangeably and include any type of methodology, process, mathematical operation, or technique.
- The term “module” as used herein refers to any known or later developed hardware, software, firmware, artificial intelligence, fuzzy logic, or combination of hardware and software that is capable of performing the functionality associated with that element. Also, while the invention is described in terms of exemplary embodiments, it should be appreciated that individual aspects of the invention can be separately claimed.
- The term “in communication with” as used herein refers to any coupling, connection, or interaction using electrical signals to exchange information or data, using any system, hardware, software, protocol, or format.
- A user context, an extended user context, and/or a user social context as used herein means information about a user of a social media network that can be used to determine a “value” of that user.
- The term “social media network” or “social media” is a service provider that builds online communities of people, who share interests and/or activities, or who are interested in exploring the interests and activities of others. Generally, social media are web-based and provide a variety of ways for users to interact, such as e-mail and instant messaging services.
- The present disclosure is described in conjunction with the appended figures:
-
FIG. 1 is a block diagram of an embodiment of a communication system operable to interact with persons using a social media network; -
FIG. 2A is a block diagram of an embodiment of a social media gateway; -
FIG. 2B is a block diagram of an embodiment of a dialog system; -
FIG. 3 is a block diagram of an embodiment of a dialog data structure; -
FIG. 4 is a block diagram of an embodiment of a customer value data structure; -
FIG. 5 is a flow diagram of an embodiment of a process for creating a customer value data structure for a user of social media; -
FIG. 6 is a flow diagram of an embodiment a process for generating a customer value for a user of a social media; -
FIG. 7 is a flow diagram of an embodiment a process for modifying an interaction with a user of social media based on the customer value associated with the user; -
FIG. 8 is a block diagram of an embodiment of a computing environment; and -
FIG. 9 is a block diagram of an embodiment of a computer system. - In the appended figures, similar components and/or features may have the same reference label. Further, various components of the same type may be distinguished by following the reference label by a letter that distinguishes among the similar components. If only the first reference label is used in the specification, the description is applicable to any one of the similar components having the same first reference label irrespective of the second reference label.
- The ensuing description provides embodiments only, and is not intended to limit the scope, applicability, or configuration of the claims. Rather, the ensuing description will provide those skilled in the art with an enabling description for implementing the embodiments. Various changes may be made in the function and arrangement of elements of the embodiment without departing from the spirit and scope of the appended claims.
- A
communication system 100, for interacting with persons using social media is shown inFIG. 1 . Thecommunication system 100 can include acontact center 102, anetwork 108, and one or more types of social media networks or systems, such associal media network 1 112,social media network 2 114, and/orsocial media network 3 116.Social media networks communication system 100 can communicate with more or fewersocial media networks FIG. 1 , as represented byellipses 118. - The
network 108 can be any network or system operable to allow communication between thecontact center 102 and the one or moresocial media networks network 108 can represent any communication system, whether wired or wireless, using any protocol and/or format. Thenetwork 108 provides communication capability for thecontact center 102 to communicate with websites or systems corresponding to the one or moresocial media networks network 108 can represent two or more networks, where each network is a different communication system using different communication protocols and/or formats and/or different hardware and software. For example,network 108 can be a wide area network, local area network, the Internet, a cellular telephone network, or some other type of communication system. Thenetwork 108 may be as described in conjunction withFIGS. 8 and 9 . - A
contact center 102 can be a system that can communicate with one or more persons that use socialmedia networking sites contact center 102 can be hardware, software, or a combination of hardware and software. Thecontact center 102 can be executed by one or more servers or computer systems, as described in conjunction withFIGS. 8 and 9 . Thecontact center 102 can include all systems, whether hardware or software, that allow thecontact center 102 to receive, service, and respond to directed and non-directed contacts. For example thecontact center 102 can include the telephone or email system, an interface to human agents, systems to allow human agents to service and respond to received contacts, and one or more systems operable to analyze and improve the function of agent interaction. - The
contact center 102 may include adialog system 104 and asocial media gateway 106. While thedialog system 104 and thesocial media gateway 106 are shown as being a part of thecontact system 102, in other embodiments, thedialog system 104 and/or thesocial media gateway 106 are separate systems or functions executed separately from thecontact center 102 and/or executed by a third party. Thedialog system 104 may process and receive messages. Thesocial media gateway 106 can receive and translate messages from the one or moresocial media networks dialog system 104 is described in conjunction withFIG. 2B . An embodiment of thesocial media gateway 106 is described in conjunction withFIG. 2A . - The
contact center 102 may also communicate with one ormore communication devices 110. Thecommunication devices 110 can represent a customer's or user's cell phone, email system, personal digital assistant, laptop computer, or other device that allows thecontact center 102 to interact with the customer. Thecontact center 102 can modify a non-direct contact, from asocial media network communication device 110. - An embodiment of the
social media gateway 106 is shown inFIG. 2A . Thesocial media gateway 106 can include one or more components which may include hardware, software, or combination of hardware and software. Thesocial media gateway 106 can be executed by a computer system, such as those described in conjunction withFIGS. 7 and 8 . However, in other embodiments, the components described in conjunction withFIG. 2A are logic circuits or other specially-designed hardware that are embodied in a field programmable gate array (FPGA) application specific integrated circuit (ASIC), or other hardware. - Herein, the
social media gateway 106 can include one ormore content filters contact center 102 from asocial media network contact center 102, may not need a response. As such, the content filter 202 can filter out or delete the non-suitable message from the messages that are received by the social media network application programming interface (API) 1 204 a, socialmedia network API 2 204 b, and/or socialmedia network API 3 204 c. With the content filter 202, the social media network API 204 only needs to translate those messages that should be received by thedialog system 104. Translation typically requires the conversion of the message into a different format. - The content filter 202 is provided with one or more heuristics for filter rules from a filter database (not shown). These filter rules can be created by the external customer or internal user (e.g. agent or administrator) of the
communication system 100. Thus, the user or customer of thecommunication system 100 can customize the filtering of messages fromsocial media networks social media networks social media networks social media networks social media gateway 106, it is to be appreciated that the content filter 202 may be a part of the social media network API 204. The content filter 202 may correspond to query terms used by the social media network API 204. The content filter 202 or query terms are an argument to the social media network API 204 call. - The social media network API 204 can be an application that the
social media network social media network social media gateway 106 to thesocial media network contact center 102 and/or the products or services of the enterprise. - The
social media gateway 106 can include one or more social media network APIs 204. As shown inFIG. 2A , thesocial media gateway 106 may include a social media network API 204 for eachsocial media network social media gateway 106 can interact with eachsocial media network social media network social media gateway 106 can be easily expanded to interact with those social media networks by adding another social media network API 204. Wheresocial media networks 112 are more standardized, or use substantially similar formats or protocols, a single social media network API 204 can be shared by multiple social media networks 112-116. - The social media network API 204 can receive messages from and send messages to the
social media network social media network message filter 206. The social media network API 204 can translate the received message into a standard formatted file. For example, the translated message may be represented by an extensible mark-up language (XML) file or other file having a general format. As such, each specific and particular social media network message can be translated into a standard format for use by thedialog system 104. Further, the social media network API 204 can receive a generally or standard format response message, from thedialog system 104, and translate that response into a particularly or specifically formatted response message that can be posted to the correspondingsocial media network - Messages to the
contact center 102 are addressed to thecontact center 102. For example, a customer may become a “friend” of thecontact center 102 on asocial media network 114, such as Facebook. The customer may then address a message to thecontact center 102 on Facebook. This non-direct contact is a message that is not sent directly to thecontact center 102 but to the contact center's Facebook page. In other embodiments, thecontact center 102 receives messages not addressed to thecontact center 102. For example, thecontact center 102 can receive tweets from Twitter that are “broadcast” rather than addressed to thecontact center 102. Thecontact center 102 may also search for messages or content on thesocial media networks social media gateway 106 of thecontact center 102 can query, gather, or connect to a live feed of data from asocial media network - Further, the social media network API 204 can also retrieve user context or other extended information from the
social media networks social media network social media network social media networks - The translated messages from the social media network API 204 can be received by a
message filter 206. Amessage filter 206 can perform some or all of the functions of the content filter 202 and eliminate messages before being sent to thedialog system 104. However, in other embodiments, themessage filter 206 eliminates information from within the messages before the redacted messages are sent to thedialog system 104. For example, a message from asocial media network 112 may have three or four interactions between two parties not associated with thecontact center 102. Only one of the several postings may be pertinent to thedialog system 104. As such, themessage filter 206 can eliminate or delete at least a portion of the other messages for thedialog system 104. Thus, thedialog system 104 receives a message where some of the content of the message has been deleted. Themessage filter 206 can retrieve heuristics or filter rules from a filter database (not shown), similar to the content filter 202. A substantial difference between the content and message filters 202 and 206 is that the content filter 202 is specific to a particular message format associated with a correspondingsocial media network message filter 206 is applied to a standardized or universal format and is therefore common to multiplesocial media networks dialog system 104. - A
message aggregator 208 may also be included with thesocial media gateway 106. Amessage aggregator 208 can, in contrast to themessage filter 206, combine two or more messages into a packet or grouping that is sent to thedialog system 104. Therefore, themessage aggregator 208 can interrelate or combine messages based on information within the messages. For example, two messages may be combined based on any of the message fields referenced above, such as the person that posted the message, the subject, the request or question asked, the person the message was sent to, or other information that may be pertinent to thedialog system 104. Thus, thedialog system 104 may be able to respond concurrently to two or more messages based on a grouping provided by themessage aggregator 208. Regardless of whether the messages are aggregated, each message or grouping of messages can be sent from thesocial media gateway 106 to thedialog system 104. - The
social media gateway 106 can also send responses back to thesocial media networks contact center 102 can be sent to thesocial media gateway 106. The response may be in a general format and translated. The translated response may then be posted to the appropriatesocial media network social media gateway 106. In other embodiments, the agent may post the response directly to thesocial media network social media gateway 106. - An embodiment of the
dialog system 104 is shown inFIG. 2B . Thedialog system 104 can include one or more components which may be hardware, software, or a combination of hardware and software. Thedialog system 104 can be executed by a computer system such as those described in conjunction withFIGS. 8 and 9 . However, in other embodiments, the components described in conjunction withFIG. 2B , are logic circuits or other specially-designed hardware that are embodied in a FPGA or ASIC. The components contained within thedialog system 104 can include adialog core 210 that is communication with amessage history database 222, anagent interface 224, and a heuristic rules anddialogs database 218. Further, the heuristic rules anddialogs database 218 can be in communication with adialog creator 220. - The
dialog core 210 can include one or more sub-components. For example, thedialog core 210 can include atrend analysis component 212, atext processing component 214, and ananalysis tools component 216. These components, similar to the components for thedialog system 104, can be hardware, software, or combination of hardware and software. Thedialog core 210 may step through the states of a dialog data structure. A dialog data structure can include a set of inputs and associated actions that can be taken which allow for the automatic and structured response to social media requests or messages. For example, if a user asks for a manual, the input of the text word “manual” can cause thedialog system 104 in accordance with a dialog data structure, to send information about one or more manuals. In turn, the receiver of the response may respond, in kind, with the selection of a certain user manual. In which case, the dialog data structure may then instruct the dialog core to send the user to a website where the user can retrieve an electronic version of the manual. As such, the dialog data structure provides a script a dialog that allows thedialog core 210 to automate the interaction between thecontact center 102 and a person. This automation eliminates the need for agent involvement, in some situations, and makes thecontact center 102 more efficient and more effective. Further, the automation expands the contact center's ability to answer numerous messages from the plethora of postings on the numeroussocial media networks - The
dialog creator 220 will create adialog data structure 300 that includes instructions for various states for each social media message that comes into thecontact center 102. The first instruction might be to send the social media message to thetrend analysis component 212, then to thetext processing component 214, and then execute a query of a Customer Relationship Management (CRM) database 232 (to determine if this user has an existing order). ACRM database 232 can be a database as described in conjunction withFIGS. 8 & 9 and can store information about customers or other data related to customer relations. Finally thedialog data structure 220 might decide that the social media message should be sent to ahuman agent 228 for processing. The instructions or node transitions are executed in thedialog core 210 and make use of many different components that thedialog creator 220 combines in any way the user desires to handle the social media messages. Thedialog core 210 can make use of thetrend analysis component 212,text processing component 214, or other systems. Thedialog core 210 may also interface with a CRM system and/ordatabase 232, external databases, social media user information (e.g., followers, friends, post history, etc. from the social media site), or other systems. - The
trend analysis component 212 is operable to analyze trends that occur between two or more messages received by thesocial media networks trend analysis component 212 can identify trends across several differentsocial media networks trend analysis component 212 may be able to identify escalations in the occurrences of particular text, identities, or other information, or may identify multiple occurrences over a period of time. Thetrend analysis component 212 may also be able to apply one or more different algorithms to occurrences of information within thesocial media networks trend analysis component 212 can match the number of occurrences of a phrase or word over a period of time and apply analysis to determine if the occurrences are increasing or decreasing over the period of time. - The
text processing component 214 is operable to analyze text of one or more messages fromsocial media networks text processing component 214 may execute one or more methods of document summarization. The summarization may occur if the social media message will be sent to anagent 228 of thecontact center 102; the summarization can reduce the amount of information that theagent 228 may manage. The text processing rules or models may be stored in and/or retrieved from a textprocessing rules database 230. The textprocessing rules database 230 can be a database as described in conjunction withFIGS. 7 and 8 that stores rules or models used by thetext processing component 214. - The
text processing component 214 can identify one or more occurrences of a particular text, such as using one or more of the message fields referenced above, in order to associate that social media message with one or more dialogs data structures in the heuristic rules anddialog database 218. For example, thetext processing component 214 can look for the word “manual,” in the social media message. If the word “manual” is found, thetext processing component 214 may retrieve a dialog data structure from the heuristic rules anddialogs database 218 and, as the dialog data structure instructs, communicate with the customer about one or more owner's manuals, repair manuals, or other types of manuals. In another example, if the social media message includes the words, “buy”, “sell”, “price, “discount” or other types of words that may indicate the user or customer wishes to buy a product, thetext processing component 214 can retrieve one or more dialog data structures from the heuristic rules anddialogs database 218 that can provide instruction to assist the customer in purchasing products or services from the enterprise. - The
analysis tools component 216 is operable to analyze response messages received back from anagent interface 224. In analyzing the agent's responses, theanalysis tools component 216 can determine if the dialog data structures 300 (FIG. 3 ) originally retrieved by thetext processing component 214 met the needs of the customer. In the analysis, theagent 228 may enter one or more items of information, for theanalysis tools component 216, about the response and about how the response matched with thedialog data structures 300. Theanalysis tools component 216 can review the response and determine if it was similar to the response provided by the dialog data structure 300 (FIG. 3 ). Thus, theanalysis tools component 216 can provide information to thedialog core 210 or thedialog creator 220 to improve the dialog data structures 300 (FIG. 3 ) that are included in the heuristic rules anddialogs database 218. - The
message history database 222 can be any database or data storage system as described in conjunction withFIGS. 8 and 9 . Thus, themessage history database 222 can store data in data fields, objects, or other data structures to allow other systems to retrieve that information at a later time. Themessage history database 222 can store previous messages or information about previous messages. Thus, for example, if thetrend analysis component 212 is analyzing several messages over a period of time, thetrend analysis component 212 can retrieve information about previous messages associated with the current analysis from themessage history database 222. As such, thetrend analysis component 212 can better detect trends occurring at thesocial media networks message history database 222 can include the entire message or only a portion of the message, and in some circumstances, include metadata about the message(s). - The heuristic rules and
dialogs database 218 can be any type of database or data storage system as described in conjunction withFIGS. 8 and 9 . The heuristic rules anddialogs database 218 can store information in data fields, data objects, and/or any other data structures. An example of information stored within the heuristic rules anddialogs database 218 is described in conjunction withFIG. 3 . The heuristic rules anddialogs database 218 stores rules and dialogs data structures that automate responses to received social media messages. The dialogs data structures control the interaction between thedialog core 210 and thesocial media network dialog creator 220. Thus, thedialog creator 220 can interface with user input 226 to receive information about dialogs. The user input 226 is then used to form the states and responses for a dialog data structure. - An
agent interface 224 is a communication system operable to send action items to contactcenter agents 228, in thecontact center 102. An agent can be a person or other system that is operable to respond to certain questions or requests from a customer. For example, theagent 228 can be a person that has specialized expertise in a topic area, such as technical support. Theagent interface 224 can format the social message into an action item and forward that message to one ormore agents 228. Theagent interface 224 can also receive response(s) back from theagents 228. The information provided by theagent 228 may be used by thedialog core 210 to complete a response to the social media message. For example, the information may classify the social media message (e.g., sales, service, etc.). In other embodiments, the response is a complete response to the social media message that can be posted to thesocial media network - An embodiment of a
dialog data structure 300 is shown inFIG. 3 . Thedialog data structure 300 can be stored in several different forms of databases, such as relational databases, flat files, object-oriented databases, etc. Thus, while the term “data field” or “segment” is used herein, the data may be stored in an object, an attribute of an object, or some other form of data structure. Further, thedialog data structure 300 can be stored, retrieved, sent, or received during the processing of dialogs by thedialog core 210 or thedialog creator 220. Thedialog data structure 300 stores one or more items of information in one or more segments. The numeric identifiers (e.g. 302, 304, etc.) shown inFIG. 3 can identify, the one or more segments. - The
dialog data structure 300 can include one or more input segments, such as,input segment 1 302 andinput segment 2 304, arules segment 306, and/or adialog script segment 308.Input segments dialog data structure 300. Theinputs segments dialog data structure 300 is associated with or pertaining to the social media messages. - The
input segments trend analysis component 212 can identify. As such, if a trend is identified and associated with theinputs 302 and/or 304, thedialog data structure 300 can be retrieved and used by thedialog core 210. While there are only twoinput segments FIG. 3 , there may be more or fewer input segments associated with thedialog data structure 300, as indicated byellipses 310. - The
rules segment 306 can include one or more heuristic rules that either help with the association of the respectivedialog data structure 300 with the social media message or control the interaction between thedialog core 210 and the social media customer. For example, therule 306 can state that thedialog data structure 300 applies only if the social media message includesinput segment 1 302 but not inputsegment 2 304. One skilled in the art will be able to identify other types of rules that may govern the association of thedialog data structure 300 with the social media message. In other embodiments, therules segment 306 states that if the social media message includesinputs 302 and/or 304, then thedialog core 210 should respond with a certain type of action. - Generally, a
dialog script segment 308 includes a script of actions or responses that direct one or more other components, such as the dialog core 210 (FIG. 2B ), to conduct actions or send the responses. Thedialog script segment 308 can include the one or more states and corresponding responses or actions required by thedialog core 210. If thedialog script segment 308 applies (that is, if the social media message is requesting a certain type of information), thedialog script segment 308 may include the one or more responses that thedialog core 210 should communicate to respond to that social media message. Thedialog script segment 308 can also include a response and a pointer to anotherdialog script segment 308 or anotherdialog data structure 300. Further, thedialog script segment 308 may have one or more actions that may be taken by another component after a secondary response is received by a customer. Thus, thedialog script segment 308 can direct or instruct an interaction to continue with a social media user over a period of time and over several interactions between the user and thecontact center 102. - It should be noted that the
dialog script segment 308 can reference one or more otherdialog data structures 300. Thus, thedialog script segment 308 can direct thedialog core 210 to reference at least one otherdialog data structure 300 to further act on the social media message. Further, the social media message can be subject of two or moredialog script segments 308, and direct thedialog core 210 to complete twodialog script segments 308 on the social media message. Also,dialog script segments 308 may not be associated with a response but direct thedialog core 210 to complete other actions, such as populating databases or gathering information. - An
embodiment 400 of a data structure to store or create a customer value is shown inFIG. 4 . Thiscustomer value profile 400 can include one or more data segments that contain one or more criteria. For example,data segments 402 through 404 and 406 can each include a type of criteria, a score, a weight, or other information. For example, for onecriteria 402, there iscriteria 1 408,criteria 1 408 can be the criteria that is being evaluated. A criteria can be a parameter, metadata, or other information from a social media network or from another source, such as a public data source. For example, this criteria can include the number of followers or friends the user has on asocial media network 112, a number of posts a user makes during a pre-determined period of time (e.g. an hour, a week, a month), a number of responses the user receives to a post, a number of responses the user posts to other postings, a score for the tenor of a posting. A tenor can be a mood or some other valuation of the text within a posting or response. - From public data sources, it may be possible to determine the address of the user which may then be used to determine an approximate wealth or income for the user, the phone number, the name, or some other data that may be used to improve or better clarify the customer value.
Data segment 402 can also include aweight 410. Aweight segment 410 can include aweight 410 that can be a measure of the importance of the criteria in determining the customer value score. Aweight 410 can be a multiplier, whether the multiplier is a decimal or an integer, which changes the value of the criteria score. The weight may be provided in comparison to other weights. Thus, one criteria may be more important than another, which is reflected in the weight value. For example, the number of friends or followers may be more important than the number of responses the user makes to other postings. As such, the weight for the number of friends or followers can be greater than the weight for the number of responses. - The
data segment 402 can also include ascore segment 412. Thescore segment 412 can include a score that can include the algorithm used to score the criteria. For example, a criteria may include the number of followers the user has and that number of followers may be stored in thecriteria segment 408. However, to score that, the score may include an algorithm which determines the number of standard deviations from a mean number of followers for that social media network. For example, if the mean user has 100 friends on Facebook, and the current user has 1000 friends, they may be two standard deviations from the mean. As such, the score may be determined by a number of standard deviations. For example, if a user is two standard deviations from the mean, the number of 2 standard deviations from the mean may result in a score of 10. The actual score may also be stored in thescore segment 412. A weighedscore segment 414 may store the result of the computation of the weight and the score. As such, the weighted score includes the total score for that criteria. The totalvalue score segment 418 can store the sum of all weighted scores added. Thetotal value score 418 represents the value of this customer or the customer profile. - An embodiment of a
method 500 to create a customer value data structure is shown inFIG. 5 . Generally, themethod 500 begins with astart operation 502 and terminates with anend operation 518. While a general order for the steps of themethod 500 are shown inFIG. 5 , themethod 500 can include more or fewer steps or arrange the order of the steps differently than those shown inFIG. 5 . Themethod 500 can be executed as a set of computer-executable instructions executed by a computer system and encoded or stored on a computer readable medium. Hereinafter, themethod 500 shall be explained with reference to the systems, components, modules, software, data structures, etc. described in conjunction withFIGS. 1-4 . - A
contact center 102 can receive an identity, instep 504. An identity can be a user name, a name, an address, a cell phone number, an email address, or some other identifying information for a person. In other embodiments, the identity can include a profile of two or more people. For example, the profile can be of any person that may desire a certain product or service. The profile may also be for any customer using social media. Thedialog creator 220 can receive the profile or the identity. - Optionally, the
dialog creator 220 may also receive the one or more social media networks, 112, 114, and/or 116 that need to be analyzed for users to determine customer value. The networks to be analyzed can be received asuser input 228 at thedialog creator 220. The different social networks to analyze can besocial network 1 112, 2 114, and/or 3 116. For example, the social networks may be Facebook, Twitter, Spoke, etc. - The
dialog creator 220 can also receive one or more criteria, instep 508. The criteria can apply to thecriteria segment 408 and thecustomer profile 400. In other embodiments, there may be a standard set of criteria which the user inputs or selects. The standard criteria can determine a general customer value without specifics received from the user. - The
dialog creator 220 may also receive one or more weightings for the criteria, instep 510. The weights or weightings can be a multiplier for the criteria. For example, the weightings may be a decimal number or an integer that is multiplied to the criteria to give a weighted score. In other embodiments, it may be a portion of a 100%, such as 25%. The percentage can be a reflection of the value of the criteria amongst all other criteria, such that adding all the percentage weights of all the criteria equals 100%. - The
dialog creator 220 can receive, as auser input 228, one or more scoring algorithms, instep 512. The scoring algorithms can be a mathematical function for how the criteria are calculated. For example, criteria may be calculated by the distance above or below, mean value. For example, a criteria can be the number of standard deviations away from a mean. For example, if a user has 200 friends or followers on Facebook and the mean is 100, that user may be one standard deviation above the mean value. The one standard deviation value can be a score or may be scored on another scale. In other embodiments, the algorithms can differently calculate scores for different criteria using different mathematical functions. - The
dialog creator 220 can then receive value campaign parameters, instep 514. A campaign value parameter can be a parameter that defines how values are going to be determined for one or more people. For example the value campaign can instruct whichsocial networks - The criteria used to analyze identities and profiles, weights, algorithms, and value campaign parameters may be stored in a
dialog data structure 300 instep 516. The campaign parameters can be stored in therules segment 306, meanwhile, the identity or profiles can be stored in theuser input segment value data structure 400 in thedialog script segment 308. Thus, to determine values, adialog system 104 can read the dialog script and be directed to applying the algorithms to one or more criteria to determine scores and then calculating the weighted score by multiplying the criteria scores with the weight. - An embodiment of a
method 600 for determining a customer value is shown inFIG. 6 . Generally, themethod 600 begins with astart operation 602 and terminates with anend operation 618. While a general order for the steps of themethod 600 are shown inFIG. 6 , themethod 600 can include more or fewer steps or arrange the order of the steps differently than those shown inFIG. 6 . Themethod 600 can be executed as a set of computer-executable instructions executed by a computer system and encoded or stored on a computer readable medium. Hereinafter, themethod 600 shall be explained with reference to the systems, components, modules, software, data structures, etc. described in conjunction withFIGS. 1-4 . - A
contact center 102 can receive an identity or profile, instep 604. An identity can include any identifying information for a person such as a name, a user name, an address, a phone number, or other data that may be used to identify a person using asocial media network dialog system 104 can retrieve adata dialog structure 300 from the heuristic rules anddialogs database 216, instep 606. Thecontact center 100 searches the heuristic rules anddialogs database 216 for adialog data structure 300 that includes the identity or profile stored in theinput segment dialog system 104 retrieves a valuecampaign data structure 400 and inserts the identity or profile in theinput segment - The
dialog core 210 of thedialog system 104 can read the dialog script in adialog data structure 300. The dialog script can direct thedialog core 210 to access one or more accounts for a social media user associated with the identity or profile, instep 608, to obtain a user context, an extended user context, and/or a user social context. Thus, thedialog core 210 can retrieve information to access the social media account from a CRM database 232 amessage history database 222, or some other source or may directly access the social media account in thesocial media network 112 114, and/or 116. In accessing the social media account, thedialog core 210 reads information about the user from the social media account. The information retrieved can describe the person or their usage of the social media account. For example, thedialog core 210 may retrieve the number of followers the user has on Facebook, may retrieve former messages from a message history on Facebook, or may retrieve and read other information available in thesocial media network dialog core 210 can determine a number of hits on a blog, page, or video blog to determine values. -
Trend analysis component 212 reads and evaluates the parameters, instep 610. Thus, information that is required for the dialog script is retrieved from the social media account and stored in a temporary data structure by thedialog core 210. The parameters are then evaluated to determine a value score. Adialog core 210 can read the algorithms from the dialog script and apply those algorithms to the parameters to obtain a score instep 612. In applying the algorithms, thedialog core 210 executes a mathematical function to determine the value of the parameters. The mathematical function will provide a score for the parameter. -
Dialog core 210 may then apply a weight to the score, instep 614. For example, thedialog core 210 can read the weighting from the dialog script and multiply the weight by the value of the parameter determined instep 612. The result of the applying the weighting is a weighted value score. For each criteria, a value weighted score is created. Adialog core 210 may then calculate a value score for the user by adding the two or more weighted value scores together instep 616. This summation produces a total value score for that user. - An embodiment of a
method 700 to create a customer value data structure is shown inFIG. 7 . Generally, themethod 700 begins with astart operation 702 and terminates with anend operation 710. While a general order for the steps of themethod 700 are shown inFIG. 7 , themethod 700 can include more or fewer steps or arrange the order of the steps differently than those shown inFIG. 7 . Themethod 700 can be executed as a set of computer-executable instructions executed by a computer system and encoded or stored on a computer readable medium. Hereinafter, themethod 700 shall be explained with reference to the systems, components, modules, software, data structures, etc. described in conjunction withFIGS. 1-4 . - A
contact center 102 can receive an identity, instep 704. The identity can be an identity of a user of asocial media network contact center 102 can retrieve a customer value score, instep 707. The customer value score may be stored in aCRM database 232 or amessage history database 222 and be available for retrieval by thecontact center 102. The customer value score can represent a value with that user for the identity received instep 704. - The
contact center 102 then may modify an interaction with that user based on the customer value score, instep 708. For example, if the user has a high customer value score, thecontact center 102 may provide a higher level of customer service, such as, present anagent 228 to interact with the customer who has more skill or more knowledge about a typical subject. Further, the customer may be subject to a higher level of interaction including more messages sent to the user in order to gain the user's attention or affiliation with the enterprise. Thus, by determining a customer value score, an enterprise can better direct resources to the customers that may have the greatest potential for consuming products or promoting those products to other consumers. -
FIG. 8 illustrates a block diagram of acomputing environment 800 that may function as servers, computers, or other systems provided herein. Theenvironment 800 includes one ormore user computers user computers user computers user computers network 820 and/or displaying and navigating web pages or other types of electronic documents. Although theexemplary computer environment 800 is shown with three user computers, any number of user computers may be supported. -
Environment 800 further includes anetwork 820. Thenetwork 820 may can be any type of network familiar to those skilled in the art that can support data communications using any of a variety of commercially-available protocols, including without limitation SIP, TCP/IP, SNA, IPX, AppleTalk, and the like. Merely by way of example, thenetwork 820 maybe a local area network (“LAN”), such as an Ethernet network, a Token-Ring network and/or the like; a wide-area network; a virtual network, including without limitation a virtual private network (“VPN”); the Internet; an intranet; an extranet; a public switched telephone network (“PSTN”); an infra-red network; a wireless network (e.g., a network operating under any of the IEEE 802.11 suite of protocols, the Bluetooth™ protocol known in the art, and/or any other wireless protocol); and/or any combination of these and/or other networks. - The system may also include one or
more server server 825 is shown as a web server andserver 830 is shown as an application server. Theweb server 825, which may be used to process requests for web pages or other electronic documents fromuser computers web server 825 can be running an operating system including any of those discussed above, as well as any commercially-available server operating systems. Theweb server 825 can also run a variety of server applications, including SIP servers, HTTP servers, FTP servers, CGI servers, database servers, Java servers, and the like. In some instances, theweb server 825 may publish operations available operations as one or more web services. - The
environment 800 may also include one or more file and or/application servers 830, which can, in addition to an operating system, include one or more applications accessible by a client running on one or more of theuser computers user computers server user computer 805. - The web pages created by the
server 825 and/or 830 may be forwarded to auser computer 805 via a web (file)server web server 825 may be able to receive web page requests, web services invocations, and/or input data from auser computer 805 and can forward the web page requests and/or input data to the web (application)server 830. In further embodiments, theweb server 830 may function as a file server. Although for ease of description,FIG. 6 illustrates aseparate web server 825 and file/application server 830, those skilled in the art will recognize that the functions described with respect toservers computer systems server 825 and/or web (application)server 830 may function as the system, devices, or components described inFIGS. 1-4 . - The
environment 800 may also include adatabase 835. Thedatabase 835 may reside in a variety of locations. By way of example,database 835 may reside on a storage medium local to (and/or resident in) one or more of thecomputers computers database 835 may reside in a storage-area network (“SAN”) familiar to those skilled in the art. Similarly, any necessary files for performing the functions attributed to thecomputers database 835 may be a relational database, such as Oracle 10i™, that is adapted to store, update, and retrieve data in response to SQL-formatted commands. -
FIG. 9 illustrates one embodiment of acomputer system 900 upon which the servers, computers, or other systems or components described herein may be deployed or executed. Thecomputer system 900 is shown comprising hardware elements that may be electrically coupled via abus 955. The hardware elements may include one or more central processing units (CPUs) 905; one or more input devices 910 (e.g., a mouse, a keyboard, etc.); and one or more output devices 915 (e.g., a display device, a printer, etc.). Thecomputer system 900 may also include one ormore storage devices 920. By way of example, storage device(s) 920 may be disk drives, optical storage devices, solid-state storage devices such as a random access memory (“RAM”) and/or a read-only memory (“ROM”), which can be programmable, flash-updateable and/or the like. - The
computer system 900 may additionally include a computer-readablestorage media reader 925; a communications system 930 (e.g., a modem, a network card (wireless or wired), an infra-red communication device, etc.); and workingmemory 940, which may include RAM and ROM devices as described above. Thecomputer system 900 may also include aprocessing acceleration unit 935, which can include a DSP, a special-purpose processor, and/or the like. - The computer-readable
storage media reader 925 can further be connected to a computer-readable storage medium, together (and, optionally, in combination with storage device(s) 920) comprehensively representing remote, local, fixed, and/or removable storage devices plus storage media for temporarily and/or more permanently containing computer-readable information. Thecommunications system 930 may permit data to be exchanged with the network 820 (FIG. 8 ) and/or any other computer described above with respect to thecomputer system 900. Moreover, as disclosed herein, the term “storage medium” may represent one or more devices for storing data, including read only memory (ROM), random access memory (RAM), magnetic RAM, core memory, magnetic disk storage mediums, optical storage mediums, flash memory devices and/or other machine readable mediums for storing information. - The
computer system 900 may also comprise software elements, shown as being currently located within a workingmemory 940, including anoperating system 945 and/orother code 950. It should be appreciated that alternate embodiments of acomputer system 900 may have numerous variations from that described above. For example, customized hardware might also be used and/or particular elements might be implemented in hardware, software (including portable software, such as applets), or both. Further, connection to other computing devices such as network input/output devices may be employed. - In the foregoing description, for the purposes of illustration, methods were described in a particular order. It should be appreciated that in alternate embodiments, the methods may be performed in a different order than that described. It should also be appreciated that the methods described above may be performed by hardware components or may be embodied in sequences of machine-executable instructions, which may be used to cause a machine, such as a general-purpose or special-purpose processor or logic circuits programmed with the instructions to perform the methods. These machine-executable instructions may be stored on one or more machine readable mediums, such as CD-ROMs or other type of optical disks, floppy diskettes, ROMs, RAMs, EPROMs, EEPROMs, magnetic or optical cards, flash memory, or other types of machine-readable mediums suitable for storing electronic instructions. Alternatively, the methods may be performed by a combination of hardware and software.
- Specific details were given in the description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits may be shown in block diagrams in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.
- Also, it is noted that the embodiments were described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed, but could have additional steps not included in the figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination corresponds to a return of the function to the calling function or the main function.
- Furthermore, embodiments may be implemented by hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof. When implemented in software, firmware, middleware or microcode, the program code or code segments to perform the necessary tasks may be stored in a machine readable medium such as storage medium. A processor(s) may perform the necessary tasks. A code segment may represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a class, or any combination of instructions, data structures, or program statements. A code segment may be coupled to another code segment or a hardware circuit by passing and/or receiving information, data, arguments, parameters, or memory contents. Information, arguments, parameters, data, etc. may be passed, forwarded, or transmitted via any suitable means including memory sharing, message passing, token passing, network transmission, etc.
- In alternative embodiments, the
contact center system 102 can also access public sources of data to further enhance the customer value score. For example, acontact center 102 may access public records about addresses for the user. From that information, thecontact center 102 may determine a possible income level or wealth level for the user. As such, thecontact center 102 can determine if the customer is likely to buy or purchase a product. Other information may be accessed from other data sources and applied to the customer value score. As such, the customer value score can reflect a total more robust determination of the value of the customer to the enterprise. - In still another embodiment, the nature of the parameters used to determine the value score of the customer value score or the customer value score itself may be temporal. In other words, the weight associated with a parameter may be dynamic and decay based on the age of the parameter value. Decaying may be accomplished using a leaky integrator approach that can function as a moving average. The size of the integration window can be adjusted to highlight the trends without including too much noise. There can be multiple dimensions to any trend “tracking” that can be included as modifiers. For example, the weight of the score may decrease 5% every week based on the age of a parameter.
- Further, the customer value score can also be dynamic and change based on age or other temporal, geographic, or other information about parameters of the customer value score. Further, the customer value score may also change depending on the time of day, time of week, time of month or year. For example, a user may have a higher customer value score during the work week or work day compared to nights or weekends.
- In further alternatives, the
dialog core 210 may detect trending by a customer or user. Each individual social media user may be “trending” on a topic. For example, “Bob” occasionally talks (tweets/facebook posts) about possible summer vacation plans, but, starting in the month of March, the frequency of mentions for “summer vacation” increases from 0.5 mentions per month across allsocial media networks social media networks dialog core 210 can notice significant changes (i.e., statistically significant changes) that may be valuable information for a business. In other words, trends are meaningful with an understanding of what the base “noise” level is for a social media user. - In still further embodiments, a trend for one user may be compared to the trends of other users, either as individuals or as aggregated groups. With the comparison information, threshold levels (i.e., the levels at which a customer becomes valuable) can be automatically adjusted. For example, if a major media outlet (e.g., Time Magazine) writes an article on time share condos as a vacation option, the average level of conversation on time share condos for vacation may increase across a large body of users. An average number of mentions, for the whole of social media users, of time shares may increase. As such, trying to determine a valuable customer may not function with an unadjusted threshold because too many people may go over the threshold. However, the
dialog core 210 may automatically adjust the threshold based on the actions of the whole of social media users to cull out those users that post in excess of the larger general trend. Thus, for any given topic, there is an average number of mentions. The average number of mentions can fluctuate with external factors. Users that exceed the average number of mentions per unit time might be showing greater interest or potential. This analysis optimizes the use of contact center resources in attempting to connect to interested/interesting/influential customers. - While illustrative embodiments have been described in detail herein, it is to be understood that the inventive concepts may be otherwise variously embodied and employed, and that the appended claims are intended to be construed to include such variations, except as limited by the prior art.
Claims (20)
Priority Applications (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US12/762,854 US20110125550A1 (en) | 2009-11-20 | 2010-04-19 | Method for determining customer value and potential from social media and other public data sources |
DE102011017442A DE102011017442A1 (en) | 2009-11-20 | 2011-04-18 | Method for determining customer value and potential from social media and other public data sources |
GB1106592A GB2479825A (en) | 2009-11-20 | 2011-04-19 | Customisation of consumer service level at a contact centre according to influence credentials on a social networking site, e.g. facebook |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US26301309P | 2009-11-20 | 2009-11-20 | |
US12/762,854 US20110125550A1 (en) | 2009-11-20 | 2010-04-19 | Method for determining customer value and potential from social media and other public data sources |
Publications (1)
Publication Number | Publication Date |
---|---|
US20110125550A1 true US20110125550A1 (en) | 2011-05-26 |
Family
ID=44062080
Family Applications (4)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US12/704,244 Abandoned US20110125697A1 (en) | 2009-11-20 | 2010-02-11 | Social media contact center dialog system |
US12/709,135 Active 2031-03-14 US8331550B2 (en) | 2009-11-20 | 2010-02-19 | Social media language identification and routing |
US12/762,854 Abandoned US20110125550A1 (en) | 2009-11-20 | 2010-04-19 | Method for determining customer value and potential from social media and other public data sources |
US12/762,856 Abandoned US20110125580A1 (en) | 2009-11-20 | 2010-04-19 | Method for discovering customers to fill available enterprise resources |
Family Applications Before (2)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US12/704,244 Abandoned US20110125697A1 (en) | 2009-11-20 | 2010-02-11 | Social media contact center dialog system |
US12/709,135 Active 2031-03-14 US8331550B2 (en) | 2009-11-20 | 2010-02-19 | Social media language identification and routing |
Family Applications After (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US12/762,856 Abandoned US20110125580A1 (en) | 2009-11-20 | 2010-04-19 | Method for discovering customers to fill available enterprise resources |
Country Status (3)
Country | Link |
---|---|
US (4) | US20110125697A1 (en) |
DE (1) | DE102011017442A1 (en) |
GB (1) | GB2479825A (en) |
Cited By (51)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20110246578A1 (en) * | 2010-03-31 | 2011-10-06 | Technische Universitat Berlin | Method and system for analyzing messages |
US20110282943A1 (en) * | 2010-05-11 | 2011-11-17 | Vitrue, Inc. | Systems and methods for determining value of social media pages |
US20120011208A1 (en) * | 2010-07-09 | 2012-01-12 | Avaya Inc. | Conditioning responses to emotions of text communications |
US20120095770A1 (en) * | 2010-10-19 | 2012-04-19 | International Business Machines Corporation | Defining Marketing Strategies Through Derived E-Commerce Patterns |
US20120166348A1 (en) * | 2010-12-26 | 2012-06-28 | International Business Machines Corporation | Statistical analysis of data records for automatic determination of activity of non-customers |
US20120185892A1 (en) * | 2011-01-19 | 2012-07-19 | Fliptop, Inc., a corporation of CA | System and method for managing multiple content channels and engagement scoring |
US20120203584A1 (en) * | 2011-02-07 | 2012-08-09 | Amnon Mishor | System and method for identifying potential customers |
US20120324007A1 (en) * | 2011-06-20 | 2012-12-20 | Myspace Llc | System and method for determining the relative ranking of a network resource |
WO2013019363A1 (en) * | 2011-08-04 | 2013-02-07 | Pitney Bowes Inc. | Method and system for creating targeted advertising utilizing social media activity |
US20130054480A1 (en) * | 2011-08-25 | 2013-02-28 | Bank Of America Corporation | Determining network value of customer |
US8412512B1 (en) * | 2011-05-20 | 2013-04-02 | Google Inc. | Feed translation for a social network |
US20130117281A1 (en) * | 2011-11-03 | 2013-05-09 | Cgi Technologies And Solutions Inc. | Method and apparatus for social media advisor for retention and treatment (smart) |
US20130132202A1 (en) * | 2011-11-23 | 2013-05-23 | Disney Enterprises, Inc. | Awarding achievements |
US20130173333A1 (en) * | 2011-12-28 | 2013-07-04 | Sap Ag | Prioritizing social activity postings |
US20130231975A1 (en) * | 2012-03-02 | 2013-09-05 | Elizabeth Ann High | Product cycle analysis using social media data |
US20130275212A1 (en) * | 2010-09-15 | 2013-10-17 | Deepak K. Agarwal | Determining whether to provide an advertisement to a user of a social network |
US20140006372A1 (en) * | 2011-05-12 | 2014-01-02 | Microsoft Corporation | Identifying and recommending experts using shared posts and interactions |
US20140149422A1 (en) * | 2012-11-28 | 2014-05-29 | Dell Products L.P. | Automating Management of Social Media Data |
US20140156538A1 (en) * | 2012-12-05 | 2014-06-05 | At&T Intellectual Property I, L.P. | Customer Contact Management |
US20140180788A1 (en) * | 2009-08-19 | 2014-06-26 | Oracle International Corporation | Method and system for implementing a cloud-based social media marketing method and system |
US20140289006A1 (en) * | 2013-03-20 | 2014-09-25 | Kaptivating Hospitality LLC | Method and System For Social Media Sales |
US20140310616A1 (en) * | 2012-05-18 | 2014-10-16 | Artashes Valeryevich Ikonomov | System for interactive communication |
US9092492B2 (en) | 2011-05-24 | 2015-07-28 | Avaya Inc. | Social media identity discovery and mapping |
US20150348064A1 (en) * | 2011-05-10 | 2015-12-03 | Restaurant Revolution Technologies, Inc. | Systems and methods for take-out order analytics |
US9213961B2 (en) | 2008-09-21 | 2015-12-15 | Oracle International Corporation | Systems and methods for generating social index scores for key term analysis and comparisons |
US9213996B2 (en) | 2012-11-19 | 2015-12-15 | Wal-Mart Stores, Inc. | System and method for analyzing social media trends |
US20150379647A1 (en) * | 2014-06-30 | 2015-12-31 | Linkedln Corporation | Suggested accounts or leads |
US9247061B2 (en) | 2013-03-15 | 2016-01-26 | Avaya Inc. | Answer based agent routing and display method |
US9357022B1 (en) * | 2012-06-28 | 2016-05-31 | Google Inc. | Measuring effectiveness of social networking activity |
US20160164927A1 (en) * | 2011-08-25 | 2016-06-09 | Google Inc. | Social media session access |
US20160321370A1 (en) * | 2012-07-09 | 2016-11-03 | Facebook, Inc. | Acquiring structured user data using composer interface having input fields corresponding to acquired structured data |
US20170064759A1 (en) * | 2015-05-28 | 2017-03-02 | Andrew Egendorf | Communication method and apparatus |
US9727925B2 (en) | 2012-09-09 | 2017-08-08 | Oracle International Corporation | Method and system for implementing semantic analysis of internal social network content |
US20170366828A1 (en) * | 2012-04-27 | 2017-12-21 | Comcast Cable Communications, Llc | Processing and delivery of segmented video |
US20180255046A1 (en) * | 2015-02-24 | 2018-09-06 | Nelson A. Cicchitto | Method and apparatus for a social network score system communicably connected to an id-less and password-less authentication system |
US10134391B2 (en) | 2012-09-15 | 2018-11-20 | Avaya Inc. | System and method for dynamic ASR based on social media |
US10318914B1 (en) | 2015-12-07 | 2019-06-11 | Amazon Technologies, Inc. | Creating group orders |
US10339541B2 (en) | 2009-08-19 | 2019-07-02 | Oracle International Corporation | Systems and methods for creating and inserting application media content into social media system displays |
US10380535B1 (en) * | 2015-12-07 | 2019-08-13 | Amazon Technologies, Inc. | Creating group orders through geofencing |
CN110431576A (en) * | 2017-04-07 | 2019-11-08 | 金伯利-克拉克环球有限公司 | The method and system of resource is distributed for talking in response to social media |
US10521807B2 (en) | 2013-09-05 | 2019-12-31 | TSG Technologies, LLC | Methods and systems for determining a risk of an emotional response of an audience |
US10922657B2 (en) | 2014-08-26 | 2021-02-16 | Oracle International Corporation | Using an employee database with social media connections to calculate job candidate reputation scores |
US20210090187A1 (en) * | 2002-02-06 | 2021-03-25 | Konrad Hernblad | Customer-based wireless food ordering and payment system and method |
US11049084B2 (en) | 2011-05-10 | 2021-06-29 | Rrt Holdings, Llc | Systems and methods for take-out order management |
US11093557B2 (en) * | 2016-08-29 | 2021-08-17 | Zoominfo Apollo Llc | Keyword and business tag extraction |
US11122034B2 (en) | 2015-02-24 | 2021-09-14 | Nelson A. Cicchitto | Method and apparatus for an identity assurance score with ties to an ID-less and password-less authentication system |
US11171941B2 (en) | 2015-02-24 | 2021-11-09 | Nelson A. Cicchitto | Mobile device enabled desktop tethered and tetherless authentication |
US11201931B1 (en) * | 2012-03-14 | 2021-12-14 | Liferay, Inc. | Managing social equity in a portal platform |
US11483265B2 (en) | 2009-08-19 | 2022-10-25 | Oracle International Corporation | Systems and methods for associating social media systems and web pages |
US20220374813A1 (en) * | 2021-05-19 | 2022-11-24 | Mitel Networks Corporation | Customer request routing based on social media clout of customers and agents |
US11620660B2 (en) | 2009-08-19 | 2023-04-04 | Oracle International Corporation | Systems and methods for creating and inserting application media content into social media system displays |
Families Citing this family (60)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US9071650B1 (en) | 2008-09-17 | 2015-06-30 | Socialware, Inc. | Method, system and computer program product for enforcing access controls to features and subfeatures on uncontrolled web application |
US20110153642A1 (en) * | 2009-12-21 | 2011-06-23 | International Business Machines Corporation | Client Relationship Management |
US20140254790A1 (en) | 2013-03-07 | 2014-09-11 | Avaya Inc. | System and method for selecting agent in a contact center for improved call routing |
US8601114B1 (en) * | 2010-05-21 | 2013-12-03 | Socialware, Inc. | Method, system and computer program product for interception, quarantine and moderation of internal communications of uncontrolled systems |
US20120116982A1 (en) * | 2010-06-02 | 2012-05-10 | Salesforce. com. Inc. | Method and system for escalating content of discussions to particular memory locations |
US20120005106A1 (en) * | 2010-06-30 | 2012-01-05 | Cisco Technology | Customer care based on social media |
US9197448B2 (en) * | 2010-07-19 | 2015-11-24 | Babar Mahmood Bhatti | Direct response and feedback system |
US20120036200A1 (en) * | 2010-08-09 | 2012-02-09 | Bank Of America Corporation | Social media engagement system |
US20120036207A1 (en) * | 2010-08-09 | 2012-02-09 | Bank Of America Corporation | Social media engagement system message routing and queuing |
US20120072358A1 (en) * | 2010-09-16 | 2012-03-22 | Cisco Technology, Inc. | Customer care replies on social media |
US20120143592A1 (en) * | 2010-12-06 | 2012-06-07 | Moore Jr James L | Predetermined code transmission for language interpretation |
AU2011338160B2 (en) * | 2010-12-09 | 2016-07-21 | Salesforce.Com, Inc. | System, method, and computer-readable program for real-time monitoring of activity |
US8761377B2 (en) * | 2011-02-01 | 2014-06-24 | Cisco Technology, Inc. | Routing contact center interactions |
JP5614463B2 (en) * | 2011-02-15 | 2014-10-29 | 富士通株式会社 | Operator selection device, operator selection program, and operator selection method |
GB2502736A (en) * | 2011-02-23 | 2013-12-04 | Bottlenose Inc | System and method for analyzing messages in a network or across networks |
US20120254053A1 (en) * | 2011-03-30 | 2012-10-04 | Bank of America Legal Deparment | On Demand Information Network |
US8175244B1 (en) | 2011-07-22 | 2012-05-08 | Frankel David P | Method and system for tele-conferencing with simultaneous interpretation and automatic floor control |
US8606869B2 (en) * | 2011-10-12 | 2013-12-10 | Credibility Corp. | Method and system for directly targeting and blasting messages to automatically identified entities on social media |
EP2801063A4 (en) * | 2012-01-06 | 2015-08-05 | David S Kidder | System and method for managing advertising intelligence and customer relations management data |
WO2013133870A2 (en) * | 2012-03-07 | 2013-09-12 | Snap Trends, Inc. | Methods and systems of aggregating information of social networks based on geographical locations via a network |
US20130268516A1 (en) * | 2012-04-06 | 2013-10-10 | Imran Noor Chaudhri | Systems And Methods For Analyzing And Visualizing Social Events |
US9374374B2 (en) * | 2012-06-19 | 2016-06-21 | SecureMySocial, Inc. | Systems and methods for securing social media for users and businesses and rewarding for enhancing security |
US9069763B2 (en) | 2012-07-02 | 2015-06-30 | International Business Machines Corporation | Services management application integrating social media and automated infrastructure monitoring |
US9386144B2 (en) * | 2012-08-07 | 2016-07-05 | Avaya Inc. | Real-time customer feedback |
US8929536B2 (en) | 2012-08-30 | 2015-01-06 | Liveops, Inc. | Multi-channel pivoting |
US9031827B2 (en) | 2012-11-30 | 2015-05-12 | Zip DX LLC | Multi-lingual conference bridge with cues and method of use |
US8879718B2 (en) | 2012-12-04 | 2014-11-04 | Genesys Telecommunications Laboratories, Inc. | Distributed event delivery |
US20140156341A1 (en) * | 2012-12-05 | 2014-06-05 | CoreSystems AG | Identifying potential customers using social networks |
US9959579B2 (en) | 2013-03-12 | 2018-05-01 | Microsoft Technology Licensing, Llc | Derivation and presentation of expertise summaries and interests for users |
US9430802B2 (en) * | 2013-03-14 | 2016-08-30 | International Business Machines Corporation | Method and process for collaboratively built content filtering |
US9892193B2 (en) | 2013-03-22 | 2018-02-13 | International Business Machines Corporation | Using content found in online discussion sources to detect problems and corresponding solutions |
US9408051B2 (en) | 2013-05-29 | 2016-08-02 | Avaya Inc. | Context-aware social media disaster response and emergency management |
US9477991B2 (en) | 2013-08-27 | 2016-10-25 | Snap Trends, Inc. | Methods and systems of aggregating information of geographic context regions of social networks based on geographical locations via a network |
US10467287B2 (en) * | 2013-12-12 | 2019-11-05 | Google Llc | Systems and methods for automatically suggesting media accompaniments based on identified media content |
US9888119B2 (en) * | 2014-03-05 | 2018-02-06 | Cisco Technology, Inc. | Contacts service for call center |
US9426110B2 (en) | 2014-07-31 | 2016-08-23 | International Business Machines Corporation | Automatic determination of additional languages used in social networks |
US10122670B2 (en) * | 2014-12-31 | 2018-11-06 | Facebook, Inc. | Providing translations of electronic messages via a social networking system |
US20160225030A1 (en) * | 2015-02-02 | 2016-08-04 | Adobe Systems Incorporated | Social data collection and automated social replies |
US10003559B2 (en) * | 2015-11-12 | 2018-06-19 | International Business Machines Corporation | Aggregating redundant messages in a group chat |
US9674363B1 (en) | 2015-11-24 | 2017-06-06 | Avaya Inc. | Establishing a social connection with a business during a conversation |
US11568426B2 (en) | 2015-11-24 | 2023-01-31 | Avaya Inc. | Sharing virtual business venues and feedback with social connections |
US10135983B2 (en) | 2015-11-24 | 2018-11-20 | Avaya Inc. | On-call sharing of social media context and content |
US10430835B2 (en) * | 2016-04-14 | 2019-10-01 | Google Llc | Methods, systems, and media for language identification of a media content item based on comments |
US10026092B2 (en) * | 2016-12-09 | 2018-07-17 | Nuance Communications, Inc. | Learning and automating agent actions |
WO2018118983A1 (en) * | 2016-12-19 | 2018-06-28 | Interactive Intelligence Group, Inc. | System and method for managing contact center system |
US20180329877A1 (en) * | 2017-05-09 | 2018-11-15 | International Business Machines Corporation | Multilingual content management |
US10275451B2 (en) | 2017-07-11 | 2019-04-30 | International Business Machines Corporation | Counterintuitive recommendations based upon temporary conditions |
US11308540B2 (en) | 2017-07-11 | 2022-04-19 | International Business Machines Corporation | Real time recommendation engine |
US11100535B2 (en) | 2017-07-11 | 2021-08-24 | International Business Machines Corporation | Group recommendations based on external factors |
US11074484B2 (en) * | 2019-01-31 | 2021-07-27 | International Business Machines Corporation | Self-improving transferring in bot conversation |
US20210004821A1 (en) | 2019-07-05 | 2021-01-07 | Talkdesk, Inc. | System and method for automated agent assistance next actions within a cloud-based contact center |
US11328205B2 (en) | 2019-08-23 | 2022-05-10 | Talkdesk, Inc. | Generating featureless service provider matches |
US20210117882A1 (en) | 2019-10-16 | 2021-04-22 | Talkdesk, Inc | Systems and methods for workforce management system deployment |
US20210136220A1 (en) | 2019-10-31 | 2021-05-06 | Talkdesk, Inc. | Monitoring and listening tools across omni-channel inputs in a graphically interactive voice response system |
US11189290B2 (en) * | 2019-12-04 | 2021-11-30 | International Business Machines Corporation | Interactive selection and modification |
US11736615B2 (en) | 2020-01-16 | 2023-08-22 | Talkdesk, Inc. | Method, apparatus, and computer-readable medium for managing concurrent communications in a networked call center |
US11677875B2 (en) | 2021-07-02 | 2023-06-13 | Talkdesk Inc. | Method and apparatus for automated quality management of communication records |
US11856140B2 (en) | 2022-03-07 | 2023-12-26 | Talkdesk, Inc. | Predictive communications system |
US11736616B1 (en) | 2022-05-27 | 2023-08-22 | Talkdesk, Inc. | Method and apparatus for automatically taking action based on the content of call center communications |
US11943391B1 (en) | 2022-12-13 | 2024-03-26 | Talkdesk, Inc. | Method and apparatus for routing communications within a contact center |
Citations (79)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US4866707A (en) * | 1987-03-03 | 1989-09-12 | Hewlett-Packard Company | Secure messaging systems |
US6064973A (en) * | 1998-04-17 | 2000-05-16 | Andersen Consulting Llp | Context manager and method for a virtual sales and service center |
US6183362B1 (en) * | 1996-05-24 | 2001-02-06 | Harrah's Operating Co. | National customer recognition system and method |
US6356633B1 (en) * | 1999-08-19 | 2002-03-12 | Mci Worldcom, Inc. | Electronic mail message processing and routing for call center response to same |
US20020059166A1 (en) * | 2000-11-02 | 2002-05-16 | Waytech Development Inc | Method and system for extracting contents of web pages |
US20020107715A1 (en) * | 2000-11-03 | 2002-08-08 | Pace Mark C. | Automated service scheduling system based on customer value |
US20030135512A1 (en) * | 1997-07-29 | 2003-07-17 | Morgan Charles D. | Data linking system and method using encoded links |
US20030188037A1 (en) * | 2002-03-28 | 2003-10-02 | Arik Elberse | Using existing web-based information to generate responses to user queries |
US6712698B2 (en) * | 2001-09-20 | 2004-03-30 | Igt | Game service interfaces for player tracking touch screen display |
US20040062383A1 (en) * | 2002-10-01 | 2004-04-01 | Nortel Networks Limited | Presence information for telephony users |
US20040107246A1 (en) * | 2002-12-02 | 2004-06-03 | Sony Corporation | Control system and control method, method and apparatus for processing information, information processing terminal and method thereof, storage medium, and program |
US6859529B2 (en) * | 2000-04-12 | 2005-02-22 | Austin Logistics Incorporated | Method and system for self-service scheduling of inbound inquiries |
US20050154556A1 (en) * | 2004-01-13 | 2005-07-14 | Keller Edward B. | System and method of identifying individuals of influence |
US20050160083A1 (en) * | 2004-01-16 | 2005-07-21 | Yahoo! Inc. | User-specific vertical search |
US20050177414A1 (en) * | 2004-02-11 | 2005-08-11 | Sigma Dynamics, Inc. | Method and apparatus for automatically and continuously pruning prediction models in real time based on data mining |
US20050216550A1 (en) * | 2004-03-26 | 2005-09-29 | Paseman William G | Communication mode and group integration for social networks |
US6962531B2 (en) * | 2000-11-03 | 2005-11-08 | Harrah's Operating Company, Inc. | Automated service scheduling system |
US20060009994A1 (en) * | 2004-07-07 | 2006-01-12 | Tad Hogg | System and method for reputation rating |
US20060042483A1 (en) * | 2004-09-02 | 2006-03-02 | Work James D | Method and system for reputation evaluation of online users in a social networking scheme |
US7050567B1 (en) * | 2000-01-27 | 2006-05-23 | Avaya Technology Corp. | Call management system using dynamic queue position |
US20060143081A1 (en) * | 2004-12-23 | 2006-06-29 | International Business Machines Corporation | Method and system for managing customer network value |
US20060218225A1 (en) * | 2005-03-28 | 2006-09-28 | Hee Voon George H | Device for sharing social network information among users over a network |
US7197470B1 (en) * | 2000-10-11 | 2007-03-27 | Buzzmetrics, Ltd. | System and method for collection analysis of electronic discussion methods |
US20070121843A1 (en) * | 2005-09-02 | 2007-05-31 | Ron Atazky | Advertising and incentives over a social network |
US20070198510A1 (en) * | 2006-02-03 | 2007-08-23 | Customerforce.Com | Method and system for assigning customer influence ranking scores to internet users |
US7266537B2 (en) * | 2004-01-14 | 2007-09-04 | Intelligent Results | Predictive selection of content transformation in predictive modeling systems |
US20070214097A1 (en) * | 2006-02-28 | 2007-09-13 | Todd Parsons | Social analytics system and method for analyzing conversations in social media |
US20070269783A1 (en) * | 2006-05-05 | 2007-11-22 | Mcculler Patrick | Determining social activity profile of a participant in a communication network |
US20070293238A1 (en) * | 2006-06-20 | 2007-12-20 | Seven Networks, Inc. | Location-based operations and messaging |
US20070294281A1 (en) * | 2006-05-05 | 2007-12-20 | Miles Ward | Systems and methods for consumer-generated media reputation management |
US7330873B2 (en) * | 2002-08-23 | 2008-02-12 | International Buisness Machines Corporation | Method and apparatus for routing call agents to website customers based on customer activities |
US7353182B1 (en) * | 2000-06-30 | 2008-04-01 | Accenture Llp | System and method for providing a multi-channel customer interaction center |
US20080109419A1 (en) * | 2006-01-22 | 2008-05-08 | Akiko Murakami | Computer apparatus, computer program and method, for calculating importance of electronic document on computer network, based on comments on electronic document included in another electronic document associated with former electronic document |
US20080109491A1 (en) * | 2006-11-03 | 2008-05-08 | Sezwho Inc. | Method and system for managing reputation profile on online communities |
US20080120261A1 (en) * | 2006-11-16 | 2008-05-22 | Avaya Technology Llc | Cohesive Team Selection Based on a Social Network Model |
US20080147487A1 (en) * | 2006-10-06 | 2008-06-19 | Technorati Inc. | Methods and apparatus for conversational advertising |
US20080162260A1 (en) * | 2006-12-29 | 2008-07-03 | Google Inc. | Network node ad targeting |
US20080215607A1 (en) * | 2007-03-02 | 2008-09-04 | Umbria, Inc. | Tribe or group-based analysis of social media including generating intelligence from a tribe's weblogs or blogs |
US20080318592A1 (en) * | 2007-06-22 | 2008-12-25 | International Business Machines Corporation | Delivering telephony communications to devices proximate to a recipient after automatically determining the recipient's location |
US20090048904A1 (en) * | 2007-08-16 | 2009-02-19 | Christopher Daniel Newton | Method and system for determining topical on-line influence of an entity |
US20090055435A1 (en) * | 2004-10-12 | 2009-02-26 | Kimmo Kiviluoto | Analyzer, a system and a method for defining a preferred group of users |
US20090063284A1 (en) * | 2007-02-01 | 2009-03-05 | Enliven Marketing Technologies Corporation | System and method for implementing advertising in an online social network |
US20090119173A1 (en) * | 2006-02-28 | 2009-05-07 | Buzzlogic, Inc. | System and Method For Advertisement Targeting of Conversations in Social Media |
US20090174551A1 (en) * | 2008-01-07 | 2009-07-09 | William Vincent Quinn | Internet activity evaluation system |
US20090204482A1 (en) * | 2008-02-13 | 2009-08-13 | Eran Reshef | System and method for streamlining social media marketing |
US20090217178A1 (en) * | 2008-02-26 | 2009-08-27 | Social Media Networks, Inc. | Ranking interactions between users on the internet |
US20090222313A1 (en) * | 2006-02-22 | 2009-09-03 | Kannan Pallipuram V | Apparatus and method for predicting customer behavior |
US20090249451A1 (en) * | 2008-03-31 | 2009-10-01 | Yahoo!, Inc. | Access to Trusted User-Generated Content Using Social Networks |
US20090281851A1 (en) * | 2008-05-07 | 2009-11-12 | Christopher Daniel Newton | Method and system for determining on-line influence in social media |
US20090307073A1 (en) * | 2008-06-10 | 2009-12-10 | Microsoft Corporation | Social marketing |
US20090319351A1 (en) * | 2008-06-18 | 2009-12-24 | Vyrl Mkt, Inc. | Measuring the effectiveness of a person testimonial promotion |
US20090319359A1 (en) * | 2008-06-18 | 2009-12-24 | Vyrl Mkt, Inc. | Social behavioral targeting based on influence in a social network |
US20100005152A1 (en) * | 2008-07-01 | 2010-01-07 | General Motors Corporation | Interactive information dissemination and retrieval system and method for generating action items |
US20100027778A1 (en) * | 2008-07-30 | 2010-02-04 | Cisco Technology, Inc. | Method and apparatus for maintaining dynamic queues in call centers using social network information |
US20100036690A1 (en) * | 2008-08-05 | 2010-02-11 | International Business Machines Corporation | Service scheduling |
US20100088130A1 (en) * | 2008-10-07 | 2010-04-08 | Yahoo! Inc. | Discovering Leaders in a Social Network |
US20100121849A1 (en) * | 2008-11-13 | 2010-05-13 | Buzzient, Inc. | Modeling social networks using analytic measurements of online social media content |
US20100132049A1 (en) * | 2008-11-26 | 2010-05-27 | Facebook, Inc. | Leveraging a social graph from a social network for social context in other systems |
US20100145771A1 (en) * | 2007-03-15 | 2010-06-10 | Ariel Fligler | System and method for providing service or adding benefit to social networks |
US20100153175A1 (en) * | 2008-12-12 | 2010-06-17 | At&T Intellectual Property I, L.P. | Correlation of Psycho-Demographic Data and Social Network Data to Initiate an Action |
US20100169159A1 (en) * | 2008-12-30 | 2010-07-01 | Nicholas Rose | Media for Service and Marketing |
US20100223581A1 (en) * | 2009-02-27 | 2010-09-02 | Microsoft Corporation | Visualization of participant relationships and sentiment for electronic messaging |
US20100223212A1 (en) * | 2009-02-27 | 2010-09-02 | Microsoft Corporation | Task-related electronic coaching |
US20100223341A1 (en) * | 2009-02-27 | 2010-09-02 | Microsoft Corporation | Electronic messaging tailored to user interest |
US20100228614A1 (en) * | 2009-03-03 | 2010-09-09 | Google Inc. | AdHeat Advertisement Model for Social Network |
US20100287281A1 (en) * | 2009-05-11 | 2010-11-11 | Motorola, Inc. | Telecommunication network resource management based on social network characteristics |
US7881924B2 (en) * | 2001-01-24 | 2011-02-01 | Shaw Stroz Llc | System and method for computer analysis of computer generated communications to produce indications and warning of dangerous behavior |
US20110047117A1 (en) * | 2009-08-21 | 2011-02-24 | Avaya Inc. | Selective content block of posts to social network |
US20110054992A1 (en) * | 2009-07-31 | 2011-03-03 | Liberty Michael A | Communicating price discounts |
US7930762B1 (en) * | 2006-09-11 | 2011-04-19 | Avaya Inc. | Systems and methods for automated media filtering |
US7930302B2 (en) * | 2006-11-22 | 2011-04-19 | Intuit Inc. | Method and system for analyzing user-generated content |
US20110125744A1 (en) * | 2009-11-23 | 2011-05-26 | Nokia Corporation | Method and apparatus for creating a contextual model based on offline user context data |
US8032622B2 (en) * | 2007-03-20 | 2011-10-04 | Siemens Enterprise Communications, Inc. | System and method for social-networking based presence |
US8108414B2 (en) * | 2006-11-29 | 2012-01-31 | David Stackpole | Dynamic location-based social networking |
US8271509B2 (en) * | 2008-11-20 | 2012-09-18 | Bank Of America Corporation | Search and chat integration system |
US8386564B2 (en) * | 2006-11-30 | 2013-02-26 | Red Hat, Inc. | Methods for determining a reputation score for a user of a social network |
US8396741B2 (en) * | 2006-02-22 | 2013-03-12 | 24/7 Customer, Inc. | Mining interactions to manage customer experience throughout a customer service lifecycle |
US8532280B2 (en) * | 2011-08-25 | 2013-09-10 | Bank Of America Corporation | Network value determination for call center communications |
US8792631B2 (en) * | 2009-04-07 | 2014-07-29 | Echostar Technologies L.L.C. | System and method for matching service representatives with customers |
Family Cites Families (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6205215B1 (en) * | 1998-07-01 | 2001-03-20 | Mci Communications Corporation | Method of and system for providing network-initiated multilingual operator assistance |
US6847714B2 (en) * | 2002-11-19 | 2005-01-25 | Avaya Technology Corp. | Accent-based matching of a communicant with a call-center agent |
US9208245B2 (en) * | 2007-06-21 | 2015-12-08 | Oracle International Corporation | System and method for compending blogs |
CA2705133C (en) * | 2007-12-05 | 2014-09-23 | Facebook, Inc. | Community translation on a social network |
US8296278B2 (en) * | 2008-09-17 | 2012-10-23 | Microsoft Corporation | Identifying product issues using forum data |
-
2010
- 2010-02-11 US US12/704,244 patent/US20110125697A1/en not_active Abandoned
- 2010-02-19 US US12/709,135 patent/US8331550B2/en active Active
- 2010-04-19 US US12/762,854 patent/US20110125550A1/en not_active Abandoned
- 2010-04-19 US US12/762,856 patent/US20110125580A1/en not_active Abandoned
-
2011
- 2011-04-18 DE DE102011017442A patent/DE102011017442A1/en not_active Ceased
- 2011-04-19 GB GB1106592A patent/GB2479825A/en not_active Withdrawn
Patent Citations (89)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US4866707A (en) * | 1987-03-03 | 1989-09-12 | Hewlett-Packard Company | Secure messaging systems |
US6183362B1 (en) * | 1996-05-24 | 2001-02-06 | Harrah's Operating Co. | National customer recognition system and method |
US20030135512A1 (en) * | 1997-07-29 | 2003-07-17 | Morgan Charles D. | Data linking system and method using encoded links |
US6064973A (en) * | 1998-04-17 | 2000-05-16 | Andersen Consulting Llp | Context manager and method for a virtual sales and service center |
US6356633B1 (en) * | 1999-08-19 | 2002-03-12 | Mci Worldcom, Inc. | Electronic mail message processing and routing for call center response to same |
US7050567B1 (en) * | 2000-01-27 | 2006-05-23 | Avaya Technology Corp. | Call management system using dynamic queue position |
US6859529B2 (en) * | 2000-04-12 | 2005-02-22 | Austin Logistics Incorporated | Method and system for self-service scheduling of inbound inquiries |
US7353182B1 (en) * | 2000-06-30 | 2008-04-01 | Accenture Llp | System and method for providing a multi-channel customer interaction center |
US7197470B1 (en) * | 2000-10-11 | 2007-03-27 | Buzzmetrics, Ltd. | System and method for collection analysis of electronic discussion methods |
US20020059166A1 (en) * | 2000-11-02 | 2002-05-16 | Waytech Development Inc | Method and system for extracting contents of web pages |
US8000990B2 (en) * | 2000-11-03 | 2011-08-16 | Caesars Entertainment Operating Company, Inc. | Automated service scheduling system based on customer value |
US6962531B2 (en) * | 2000-11-03 | 2005-11-08 | Harrah's Operating Company, Inc. | Automated service scheduling system |
US7765121B2 (en) * | 2000-11-03 | 2010-07-27 | Harrah's Operating Company, Inc. | Automated service scheduling system based on customer value |
US20020107715A1 (en) * | 2000-11-03 | 2002-08-08 | Pace Mark C. | Automated service scheduling system based on customer value |
US7881924B2 (en) * | 2001-01-24 | 2011-02-01 | Shaw Stroz Llc | System and method for computer analysis of computer generated communications to produce indications and warning of dangerous behavior |
US6712698B2 (en) * | 2001-09-20 | 2004-03-30 | Igt | Game service interfaces for player tracking touch screen display |
US20030188037A1 (en) * | 2002-03-28 | 2003-10-02 | Arik Elberse | Using existing web-based information to generate responses to user queries |
US7330873B2 (en) * | 2002-08-23 | 2008-02-12 | International Buisness Machines Corporation | Method and apparatus for routing call agents to website customers based on customer activities |
US20040062383A1 (en) * | 2002-10-01 | 2004-04-01 | Nortel Networks Limited | Presence information for telephony users |
US20040107246A1 (en) * | 2002-12-02 | 2004-06-03 | Sony Corporation | Control system and control method, method and apparatus for processing information, information processing terminal and method thereof, storage medium, and program |
US20050154556A1 (en) * | 2004-01-13 | 2005-07-14 | Keller Edward B. | System and method of identifying individuals of influence |
US7266537B2 (en) * | 2004-01-14 | 2007-09-04 | Intelligent Results | Predictive selection of content transformation in predictive modeling systems |
US20050160083A1 (en) * | 2004-01-16 | 2005-07-21 | Yahoo! Inc. | User-specific vertical search |
US20050177414A1 (en) * | 2004-02-11 | 2005-08-11 | Sigma Dynamics, Inc. | Method and apparatus for automatically and continuously pruning prediction models in real time based on data mining |
US20050216550A1 (en) * | 2004-03-26 | 2005-09-29 | Paseman William G | Communication mode and group integration for social networks |
US20060009994A1 (en) * | 2004-07-07 | 2006-01-12 | Tad Hogg | System and method for reputation rating |
US20060042483A1 (en) * | 2004-09-02 | 2006-03-02 | Work James D | Method and system for reputation evaluation of online users in a social networking scheme |
US20090055435A1 (en) * | 2004-10-12 | 2009-02-26 | Kimmo Kiviluoto | Analyzer, a system and a method for defining a preferred group of users |
US7941339B2 (en) * | 2004-12-23 | 2011-05-10 | International Business Machines Corporation | Method and system for managing customer network value |
US20060143081A1 (en) * | 2004-12-23 | 2006-06-29 | International Business Machines Corporation | Method and system for managing customer network value |
US20060218225A1 (en) * | 2005-03-28 | 2006-09-28 | Hee Voon George H | Device for sharing social network information among users over a network |
US20070121843A1 (en) * | 2005-09-02 | 2007-05-31 | Ron Atazky | Advertising and incentives over a social network |
US20080109419A1 (en) * | 2006-01-22 | 2008-05-08 | Akiko Murakami | Computer apparatus, computer program and method, for calculating importance of electronic document on computer network, based on comments on electronic document included in another electronic document associated with former electronic document |
US20070198510A1 (en) * | 2006-02-03 | 2007-08-23 | Customerforce.Com | Method and system for assigning customer influence ranking scores to internet users |
US8396741B2 (en) * | 2006-02-22 | 2013-03-12 | 24/7 Customer, Inc. | Mining interactions to manage customer experience throughout a customer service lifecycle |
US20090222313A1 (en) * | 2006-02-22 | 2009-09-03 | Kannan Pallipuram V | Apparatus and method for predicting customer behavior |
US20100070485A1 (en) * | 2006-02-28 | 2010-03-18 | Parsons Todd A | Social Analytics System and Method For Analyzing Conversations in Social Media |
US8682723B2 (en) * | 2006-02-28 | 2014-03-25 | Twelvefold Media Inc. | Social analytics system and method for analyzing conversations in social media |
US20070214097A1 (en) * | 2006-02-28 | 2007-09-13 | Todd Parsons | Social analytics system and method for analyzing conversations in social media |
US20090119173A1 (en) * | 2006-02-28 | 2009-05-07 | Buzzlogic, Inc. | System and Method For Advertisement Targeting of Conversations in Social Media |
US20070294281A1 (en) * | 2006-05-05 | 2007-12-20 | Miles Ward | Systems and methods for consumer-generated media reputation management |
US20070269783A1 (en) * | 2006-05-05 | 2007-11-22 | Mcculler Patrick | Determining social activity profile of a participant in a communication network |
US20070293238A1 (en) * | 2006-06-20 | 2007-12-20 | Seven Networks, Inc. | Location-based operations and messaging |
US7930762B1 (en) * | 2006-09-11 | 2011-04-19 | Avaya Inc. | Systems and methods for automated media filtering |
US20080147487A1 (en) * | 2006-10-06 | 2008-06-19 | Technorati Inc. | Methods and apparatus for conversational advertising |
US20080109491A1 (en) * | 2006-11-03 | 2008-05-08 | Sezwho Inc. | Method and system for managing reputation profile on online communities |
US20080120261A1 (en) * | 2006-11-16 | 2008-05-22 | Avaya Technology Llc | Cohesive Team Selection Based on a Social Network Model |
US7930302B2 (en) * | 2006-11-22 | 2011-04-19 | Intuit Inc. | Method and system for analyzing user-generated content |
US8108414B2 (en) * | 2006-11-29 | 2012-01-31 | David Stackpole | Dynamic location-based social networking |
US8386564B2 (en) * | 2006-11-30 | 2013-02-26 | Red Hat, Inc. | Methods for determining a reputation score for a user of a social network |
US20080162260A1 (en) * | 2006-12-29 | 2008-07-03 | Google Inc. | Network node ad targeting |
US20090063284A1 (en) * | 2007-02-01 | 2009-03-05 | Enliven Marketing Technologies Corporation | System and method for implementing advertising in an online social network |
US20080215607A1 (en) * | 2007-03-02 | 2008-09-04 | Umbria, Inc. | Tribe or group-based analysis of social media including generating intelligence from a tribe's weblogs or blogs |
US20100145771A1 (en) * | 2007-03-15 | 2010-06-10 | Ariel Fligler | System and method for providing service or adding benefit to social networks |
US8032622B2 (en) * | 2007-03-20 | 2011-10-04 | Siemens Enterprise Communications, Inc. | System and method for social-networking based presence |
US20080318592A1 (en) * | 2007-06-22 | 2008-12-25 | International Business Machines Corporation | Delivering telephony communications to devices proximate to a recipient after automatically determining the recipient's location |
US20090048904A1 (en) * | 2007-08-16 | 2009-02-19 | Christopher Daniel Newton | Method and system for determining topical on-line influence of an entity |
US20090174551A1 (en) * | 2008-01-07 | 2009-07-09 | William Vincent Quinn | Internet activity evaluation system |
US20090204482A1 (en) * | 2008-02-13 | 2009-08-13 | Eran Reshef | System and method for streamlining social media marketing |
US20090217178A1 (en) * | 2008-02-26 | 2009-08-27 | Social Media Networks, Inc. | Ranking interactions between users on the internet |
US8499247B2 (en) * | 2008-02-26 | 2013-07-30 | Livingsocial, Inc. | Ranking interactions between users on the internet |
US20090249451A1 (en) * | 2008-03-31 | 2009-10-01 | Yahoo!, Inc. | Access to Trusted User-Generated Content Using Social Networks |
US20090281851A1 (en) * | 2008-05-07 | 2009-11-12 | Christopher Daniel Newton | Method and system for determining on-line influence in social media |
US20090307073A1 (en) * | 2008-06-10 | 2009-12-10 | Microsoft Corporation | Social marketing |
US20090319351A1 (en) * | 2008-06-18 | 2009-12-24 | Vyrl Mkt, Inc. | Measuring the effectiveness of a person testimonial promotion |
US20090319359A1 (en) * | 2008-06-18 | 2009-12-24 | Vyrl Mkt, Inc. | Social behavioral targeting based on influence in a social network |
US20100005152A1 (en) * | 2008-07-01 | 2010-01-07 | General Motors Corporation | Interactive information dissemination and retrieval system and method for generating action items |
US20100027778A1 (en) * | 2008-07-30 | 2010-02-04 | Cisco Technology, Inc. | Method and apparatus for maintaining dynamic queues in call centers using social network information |
US20100036690A1 (en) * | 2008-08-05 | 2010-02-11 | International Business Machines Corporation | Service scheduling |
US8458002B2 (en) * | 2008-08-05 | 2013-06-04 | International Business Machines Corporation | Service scheduling |
US20100088130A1 (en) * | 2008-10-07 | 2010-04-08 | Yahoo! Inc. | Discovering Leaders in a Social Network |
US8375024B2 (en) * | 2008-11-13 | 2013-02-12 | Buzzient, Inc. | Modeling social networks using analytic measurements of online social media content |
US20100119053A1 (en) * | 2008-11-13 | 2010-05-13 | Buzzient, Inc. | Analytic measurement of online social media content |
US20100121849A1 (en) * | 2008-11-13 | 2010-05-13 | Buzzient, Inc. | Modeling social networks using analytic measurements of online social media content |
US8271509B2 (en) * | 2008-11-20 | 2012-09-18 | Bank Of America Corporation | Search and chat integration system |
US20100132049A1 (en) * | 2008-11-26 | 2010-05-27 | Facebook, Inc. | Leveraging a social graph from a social network for social context in other systems |
US20100153175A1 (en) * | 2008-12-12 | 2010-06-17 | At&T Intellectual Property I, L.P. | Correlation of Psycho-Demographic Data and Social Network Data to Initiate an Action |
US20100169159A1 (en) * | 2008-12-30 | 2010-07-01 | Nicholas Rose | Media for Service and Marketing |
US20100223212A1 (en) * | 2009-02-27 | 2010-09-02 | Microsoft Corporation | Task-related electronic coaching |
US20100223341A1 (en) * | 2009-02-27 | 2010-09-02 | Microsoft Corporation | Electronic messaging tailored to user interest |
US20100223581A1 (en) * | 2009-02-27 | 2010-09-02 | Microsoft Corporation | Visualization of participant relationships and sentiment for electronic messaging |
US20100228614A1 (en) * | 2009-03-03 | 2010-09-09 | Google Inc. | AdHeat Advertisement Model for Social Network |
US20130103503A1 (en) * | 2009-03-03 | 2013-04-25 | Google Inc. | AdHeat Advertisement Model for Social Network |
US8792631B2 (en) * | 2009-04-07 | 2014-07-29 | Echostar Technologies L.L.C. | System and method for matching service representatives with customers |
US20100287281A1 (en) * | 2009-05-11 | 2010-11-11 | Motorola, Inc. | Telecommunication network resource management based on social network characteristics |
US20110054992A1 (en) * | 2009-07-31 | 2011-03-03 | Liberty Michael A | Communicating price discounts |
US20110047117A1 (en) * | 2009-08-21 | 2011-02-24 | Avaya Inc. | Selective content block of posts to social network |
US20110125744A1 (en) * | 2009-11-23 | 2011-05-26 | Nokia Corporation | Method and apparatus for creating a contextual model based on offline user context data |
US8532280B2 (en) * | 2011-08-25 | 2013-09-10 | Bank Of America Corporation | Network value determination for call center communications |
Cited By (83)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20210090187A1 (en) * | 2002-02-06 | 2021-03-25 | Konrad Hernblad | Customer-based wireless food ordering and payment system and method |
US11816745B2 (en) * | 2002-02-06 | 2023-11-14 | Konrad Hernblad | Customer-based wireless food ordering and payment system and method |
US9213961B2 (en) | 2008-09-21 | 2015-12-15 | Oracle International Corporation | Systems and methods for generating social index scores for key term analysis and comparisons |
US9633399B2 (en) * | 2009-08-19 | 2017-04-25 | Oracle International Corporation | Method and system for implementing a cloud-based social media marketing method and system |
US11483265B2 (en) | 2009-08-19 | 2022-10-25 | Oracle International Corporation | Systems and methods for associating social media systems and web pages |
US11620660B2 (en) | 2009-08-19 | 2023-04-04 | Oracle International Corporation | Systems and methods for creating and inserting application media content into social media system displays |
US10339541B2 (en) | 2009-08-19 | 2019-07-02 | Oracle International Corporation | Systems and methods for creating and inserting application media content into social media system displays |
US20140180788A1 (en) * | 2009-08-19 | 2014-06-26 | Oracle International Corporation | Method and system for implementing a cloud-based social media marketing method and system |
US20110246578A1 (en) * | 2010-03-31 | 2011-10-06 | Technische Universitat Berlin | Method and system for analyzing messages |
US9704165B2 (en) * | 2010-05-11 | 2017-07-11 | Oracle International Corporation | Systems and methods for determining value of social media pages |
US20110282943A1 (en) * | 2010-05-11 | 2011-11-17 | Vitrue, Inc. | Systems and methods for determining value of social media pages |
US8478826B2 (en) * | 2010-07-09 | 2013-07-02 | Avaya Inc. | Conditioning responses to emotions of text communications |
US20120011208A1 (en) * | 2010-07-09 | 2012-01-12 | Avaya Inc. | Conditioning responses to emotions of text communications |
US9805391B2 (en) * | 2010-09-15 | 2017-10-31 | Excalibur Ip, Llc | Determining whether to provide an advertisement to a user of a social network |
US20130275212A1 (en) * | 2010-09-15 | 2013-10-17 | Deepak K. Agarwal | Determining whether to provide an advertisement to a user of a social network |
US20120215590A1 (en) * | 2010-10-19 | 2012-08-23 | International Business Machines Corporation | Defining Marketing Strategies Through Derived E-Commerce Patterns |
US9047615B2 (en) * | 2010-10-19 | 2015-06-02 | International Business Machines Corporation | Defining marketing strategies through derived E-commerce patterns |
US9043220B2 (en) * | 2010-10-19 | 2015-05-26 | International Business Machines Corporation | Defining marketing strategies through derived E-commerce patterns |
US20120095770A1 (en) * | 2010-10-19 | 2012-04-19 | International Business Machines Corporation | Defining Marketing Strategies Through Derived E-Commerce Patterns |
US20120166348A1 (en) * | 2010-12-26 | 2012-06-28 | International Business Machines Corporation | Statistical analysis of data records for automatic determination of activity of non-customers |
US20120185892A1 (en) * | 2011-01-19 | 2012-07-19 | Fliptop, Inc., a corporation of CA | System and method for managing multiple content channels and engagement scoring |
US20120203584A1 (en) * | 2011-02-07 | 2012-08-09 | Amnon Mishor | System and method for identifying potential customers |
US11049084B2 (en) | 2011-05-10 | 2021-06-29 | Rrt Holdings, Llc | Systems and methods for take-out order management |
US20220335398A1 (en) * | 2011-05-10 | 2022-10-20 | Rrt Holdings, Llc | Systems and methods for take-out order management |
US10679278B2 (en) * | 2011-05-10 | 2020-06-09 | Rrt Holdings, Llc | Systems and methods for take-out order analytics |
US10083455B2 (en) * | 2011-05-10 | 2018-09-25 | Restaurant Revolution Technologies, Inc. | Systems and methods for take-out order analytics |
US10096057B2 (en) * | 2011-05-10 | 2018-10-09 | Restaurant Revolution Technologies, Inc. | Systems and methods for take-out order analytics |
US20150348064A1 (en) * | 2011-05-10 | 2015-12-03 | Restaurant Revolution Technologies, Inc. | Systems and methods for take-out order analytics |
US11379811B2 (en) | 2011-05-10 | 2022-07-05 | Rrt Holdings, Llc | Systems and methods for take-out order management |
US20160092586A1 (en) * | 2011-05-12 | 2016-03-31 | Mircosoft Technology Licensing, LLC | Identifying and recommending experts using shared posts and interactions |
US20140006372A1 (en) * | 2011-05-12 | 2014-01-02 | Microsoft Corporation | Identifying and recommending experts using shared posts and interactions |
US9953087B2 (en) * | 2011-05-12 | 2018-04-24 | Mircosoft Technology Licensing, LLC | Identifying and recommending experts using shared posts and interactions |
US9230031B2 (en) * | 2011-05-12 | 2016-01-05 | Microsoft Technology Licensing, Llc | Identifying and recommending experts using shared posts and interactions |
US8538742B2 (en) * | 2011-05-20 | 2013-09-17 | Google Inc. | Feed translation for a social network |
US8412512B1 (en) * | 2011-05-20 | 2013-04-02 | Google Inc. | Feed translation for a social network |
US9519638B2 (en) | 2011-05-20 | 2016-12-13 | Google Inc. | Feed translation for a social network |
US9152681B2 (en) | 2011-05-24 | 2015-10-06 | Avaya Inc. | Social media identity discovery and mapping for banking and government |
US9092492B2 (en) | 2011-05-24 | 2015-07-28 | Avaya Inc. | Social media identity discovery and mapping |
WO2012177787A1 (en) * | 2011-06-20 | 2012-12-27 | Myspace, Llc. | System and method for determining the relative ranking of a network resource |
US20120324007A1 (en) * | 2011-06-20 | 2012-12-20 | Myspace Llc | System and method for determining the relative ranking of a network resource |
WO2013019363A1 (en) * | 2011-08-04 | 2013-02-07 | Pitney Bowes Inc. | Method and system for creating targeted advertising utilizing social media activity |
US20160164927A1 (en) * | 2011-08-25 | 2016-06-09 | Google Inc. | Social media session access |
US20130054480A1 (en) * | 2011-08-25 | 2013-02-28 | Bank Of America Corporation | Determining network value of customer |
US20130117281A1 (en) * | 2011-11-03 | 2013-05-09 | Cgi Technologies And Solutions Inc. | Method and apparatus for social media advisor for retention and treatment (smart) |
US9292830B2 (en) * | 2011-11-03 | 2016-03-22 | Cgi Technologies And Solutions Inc. | Method and apparatus for social media advisor for retention and treatment (SMART) |
US20130132202A1 (en) * | 2011-11-23 | 2013-05-23 | Disney Enterprises, Inc. | Awarding achievements |
US8719178B2 (en) * | 2011-12-28 | 2014-05-06 | Sap Ag | Prioritizing social activity postings |
US20130173333A1 (en) * | 2011-12-28 | 2013-07-04 | Sap Ag | Prioritizing social activity postings |
US20130231975A1 (en) * | 2012-03-02 | 2013-09-05 | Elizabeth Ann High | Product cycle analysis using social media data |
US11201931B1 (en) * | 2012-03-14 | 2021-12-14 | Liferay, Inc. | Managing social equity in a portal platform |
US20170366828A1 (en) * | 2012-04-27 | 2017-12-21 | Comcast Cable Communications, Llc | Processing and delivery of segmented video |
US20140310616A1 (en) * | 2012-05-18 | 2014-10-16 | Artashes Valeryevich Ikonomov | System for interactive communication |
US9357022B1 (en) * | 2012-06-28 | 2016-05-31 | Google Inc. | Measuring effectiveness of social networking activity |
US20160321370A1 (en) * | 2012-07-09 | 2016-11-03 | Facebook, Inc. | Acquiring structured user data using composer interface having input fields corresponding to acquired structured data |
US10534821B2 (en) * | 2012-07-09 | 2020-01-14 | Facebook, Inc. | Acquiring structured user data using composer interface having input fields corresponding to acquired structured data |
US9727925B2 (en) | 2012-09-09 | 2017-08-08 | Oracle International Corporation | Method and system for implementing semantic analysis of internal social network content |
US10552921B2 (en) | 2012-09-09 | 2020-02-04 | Oracle International Corporation | Method and system for implementing semantic analysis of internal social network content |
US10134391B2 (en) | 2012-09-15 | 2018-11-20 | Avaya Inc. | System and method for dynamic ASR based on social media |
US9213996B2 (en) | 2012-11-19 | 2015-12-15 | Wal-Mart Stores, Inc. | System and method for analyzing social media trends |
US20140149422A1 (en) * | 2012-11-28 | 2014-05-29 | Dell Products L.P. | Automating Management of Social Media Data |
US20140156538A1 (en) * | 2012-12-05 | 2014-06-05 | At&T Intellectual Property I, L.P. | Customer Contact Management |
US9247061B2 (en) | 2013-03-15 | 2016-01-26 | Avaya Inc. | Answer based agent routing and display method |
US9852477B2 (en) * | 2013-03-20 | 2017-12-26 | Kaptivating Technology Llc | Method and system for social media sales |
US20140289006A1 (en) * | 2013-03-20 | 2014-09-25 | Kaptivating Hospitality LLC | Method and System For Social Media Sales |
US20160055543A1 (en) * | 2013-03-20 | 2016-02-25 | Kaptivating Hospitality LLC | Method and system for social media sales |
US10521807B2 (en) | 2013-09-05 | 2019-12-31 | TSG Technologies, LLC | Methods and systems for determining a risk of an emotional response of an audience |
US11055728B2 (en) | 2013-09-05 | 2021-07-06 | TSG Technologies, LLC | Methods and systems for determining a risk of an emotional response of an audience |
US20150379647A1 (en) * | 2014-06-30 | 2015-12-31 | Linkedln Corporation | Suggested accounts or leads |
US10922657B2 (en) | 2014-08-26 | 2021-02-16 | Oracle International Corporation | Using an employee database with social media connections to calculate job candidate reputation scores |
US20180255046A1 (en) * | 2015-02-24 | 2018-09-06 | Nelson A. Cicchitto | Method and apparatus for a social network score system communicably connected to an id-less and password-less authentication system |
US11811750B2 (en) | 2015-02-24 | 2023-11-07 | Nelson A. Cicchitto | Mobile device enabled desktop tethered and tetherless authentication |
US11122034B2 (en) | 2015-02-24 | 2021-09-14 | Nelson A. Cicchitto | Method and apparatus for an identity assurance score with ties to an ID-less and password-less authentication system |
US11171941B2 (en) | 2015-02-24 | 2021-11-09 | Nelson A. Cicchitto | Mobile device enabled desktop tethered and tetherless authentication |
US10848485B2 (en) * | 2015-02-24 | 2020-11-24 | Nelson Cicchitto | Method and apparatus for a social network score system communicably connected to an ID-less and password-less authentication system |
US20170064759A1 (en) * | 2015-05-28 | 2017-03-02 | Andrew Egendorf | Communication method and apparatus |
US10380535B1 (en) * | 2015-12-07 | 2019-08-13 | Amazon Technologies, Inc. | Creating group orders through geofencing |
US10318914B1 (en) | 2015-12-07 | 2019-06-11 | Amazon Technologies, Inc. | Creating group orders |
US11093557B2 (en) * | 2016-08-29 | 2021-08-17 | Zoominfo Apollo Llc | Keyword and business tag extraction |
US11790395B2 (en) * | 2017-04-07 | 2023-10-17 | Kimberly-Clark Worldwide, Inc. | Methods and systems for allocating resources in response to social media conversations |
CN110431576A (en) * | 2017-04-07 | 2019-11-08 | 金伯利-克拉克环球有限公司 | The method and system of resource is distributed for talking in response to social media |
KR20190128704A (en) * | 2017-04-07 | 2019-11-18 | 킴벌리-클라크 월드와이드, 인크. | Method and system for allocating resources in response to social media conversations |
KR102611085B1 (en) * | 2017-04-07 | 2023-12-08 | 킴벌리-클라크 월드와이드, 인크. | Methods and systems for allocating resources in response to social media conversations |
US20220374813A1 (en) * | 2021-05-19 | 2022-11-24 | Mitel Networks Corporation | Customer request routing based on social media clout of customers and agents |
Also Published As
Publication number | Publication date |
---|---|
GB201106592D0 (en) | 2011-06-01 |
US8331550B2 (en) | 2012-12-11 |
DE102011017442A1 (en) | 2011-11-24 |
US20110125697A1 (en) | 2011-05-26 |
GB2479825A (en) | 2011-10-26 |
US20110125580A1 (en) | 2011-05-26 |
US20110123015A1 (en) | 2011-05-26 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US20110125550A1 (en) | Method for determining customer value and potential from social media and other public data sources | |
EP2328328B1 (en) | Method for determining response channel for a contact center from historic social media | |
US9247061B2 (en) | Answer based agent routing and display method | |
US10497069B2 (en) | System and method for providing a social customer care system | |
US20110125826A1 (en) | Stalking social media users to maximize the likelihood of immediate engagement | |
US9432325B2 (en) | Automatic negative question handling | |
US20170262529A1 (en) | Sponsor answers and user-approved, system-suggested links in a social search engine | |
US20110276513A1 (en) | Method of automatic customer satisfaction monitoring through social media | |
US20110288897A1 (en) | Method of agent assisted response to social media interactions | |
US20170372329A1 (en) | Predictive analytics for providing targeted information | |
KR101793663B1 (en) | Conversational question and answer | |
US20120016948A1 (en) | Social network activity monitoring and automated reaction | |
US20150170152A1 (en) | System and method for providing actionable input based on social media | |
US20170046627A1 (en) | Using machine learning techniques to determine propensities of entities identified in a social graph | |
US20130317808A1 (en) | System for and method of analyzing and responding to user generated content | |
US20130262320A1 (en) | Systems and methods for customer relationship management | |
US20140365327A1 (en) | Reverse auction for real-time services | |
EP3465586A1 (en) | Providing travel or promotion based recommendation associated with social graph | |
US11842372B2 (en) | Systems and methods for real-time processing of audio feedback | |
Ramaul | Role of AI in marketing through CRM integration with specific reference to chatbots | |
GB2477839A (en) | A text mining and analysis system for contact centres to receive and return messages from different social networking sites for data collection, analysis and |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
AS | Assignment |
Owner name: AVAYA INC., NEW JERSEY Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:ERHART, GEORGE;SKIBA, DAVID;MATULA, VALENTINE C.;SIGNING DATES FROM 20100415 TO 20100505;REEL/FRAME:024375/0919 |
|
AS | Assignment |
Owner name: BANK OF NEW YORK MELLON TRUST, NA, AS NOTES COLLATERAL AGENT, THE, PENNSYLVANIA Free format text: SECURITY AGREEMENT;ASSIGNOR:AVAYA INC., A DELAWARE CORPORATION;REEL/FRAME:025863/0535 Effective date: 20110211 Owner name: BANK OF NEW YORK MELLON TRUST, NA, AS NOTES COLLAT Free format text: SECURITY AGREEMENT;ASSIGNOR:AVAYA INC., A DELAWARE CORPORATION;REEL/FRAME:025863/0535 Effective date: 20110211 |
|
AS | Assignment |
Owner name: THE BANK OF NEW YORK MELLON TRUST COMPANY, N.A., PENNSYLVANIA Free format text: SECURITY AGREEMENT;ASSIGNOR:AVAYA, INC.;REEL/FRAME:029608/0256 Effective date: 20121221 Owner name: THE BANK OF NEW YORK MELLON TRUST COMPANY, N.A., P Free format text: SECURITY AGREEMENT;ASSIGNOR:AVAYA, INC.;REEL/FRAME:029608/0256 Effective date: 20121221 |
|
AS | Assignment |
Owner name: BANK OF NEW YORK MELLON TRUST COMPANY, N.A., THE, PENNSYLVANIA Free format text: SECURITY AGREEMENT;ASSIGNOR:AVAYA, INC.;REEL/FRAME:030083/0639 Effective date: 20130307 Owner name: BANK OF NEW YORK MELLON TRUST COMPANY, N.A., THE, Free format text: SECURITY AGREEMENT;ASSIGNOR:AVAYA, INC.;REEL/FRAME:030083/0639 Effective date: 20130307 |
|
STCB | Information on status: application discontinuation |
Free format text: ABANDONED -- FAILURE TO RESPOND TO AN OFFICE ACTION |
|
AS | Assignment |
Owner name: AVAYA INC., CALIFORNIA Free format text: BANKRUPTCY COURT ORDER RELEASING ALL LIENS INCLUDING THE SECURITY INTEREST RECORDED AT REEL/FRAME 025863/0535;ASSIGNOR:THE BANK OF NEW YORK MELLON TRUST, NA;REEL/FRAME:044892/0001 Effective date: 20171128 Owner name: AVAYA INC., CALIFORNIA Free format text: BANKRUPTCY COURT ORDER RELEASING ALL LIENS INCLUDING THE SECURITY INTEREST RECORDED AT REEL/FRAME 029608/0256;ASSIGNOR:THE BANK OF NEW YORK MELLON TRUST COMPANY, N.A.;REEL/FRAME:044891/0801 Effective date: 20171128 Owner name: AVAYA INC., CALIFORNIA Free format text: BANKRUPTCY COURT ORDER RELEASING ALL LIENS INCLUDING THE SECURITY INTEREST RECORDED AT REEL/FRAME 030083/0639;ASSIGNOR:THE BANK OF NEW YORK MELLON TRUST COMPANY, N.A.;REEL/FRAME:045012/0666 Effective date: 20171128 |