US20120290399A1

US20120290399A1 - Web Optimization and Campaign Management in a Syndicated Commerce Environment

Info

Publication number: US20120290399A1
Application number: US13/335,295
Authority: US
Inventors: Aron England; Steven Tedjamulia; Manish C. Mehta; Ronald Vincent Rose
Original assignee: Individual
Current assignee: Dell Products LP
Priority date: 2011-05-13
Filing date: 2011-12-22
Publication date: 2012-11-15
Also published as: US9595068B2; US20120290432A1; US20120290431A1; US20120290433A1; US20120290446A1; US20120290448A1; US8554635B2; US20120290553A1; US10354337B2; US10325325B2; US20120290374A1; US20140040067A1; US9135665B2

Abstract

A method and system are disclosed for optimizing webpages and managing campaigns in a syndicated commerce environment. Social data associated with a first user is used to determine their user characteristics. These characteristics are then used to identify a set of second users that possess the same characteristics. The past online behavior of the set of second users is analyzed to identify online conversion actions they have performed. In turn, the online conversion actions are analyzed to identify campaign data associated with a campaign goal, which is then provided to the first user.

Description

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit under 35 U.S.C. §119(e) of U.S. Provisional Application No. 61/485,367, filed May 13, 2011, entitled “Social Marketplace.” U.S. Provisional Application No. 61/485,767 includes exemplary systems and methods and is incorporated by reference in its entirety.

BACKGROUND OF THE INVENTION

1. Field of the Invention
Embodiments of the invention relate generally to information handling systems. More specifically, embodiments of the invention provide a method and system for optimizing we pages and managing campaigns in a syndicated commerce environment.
2. Description of the Related Art
As the value and use of information continues to increase, individuals and businesses seek additional ways to process and store information. One option available to users is information handling systems. An information handling system generally processes, compiles, stores, and/or communicates information or data for business, personal, or other purposes thereby allowing users to take advantage of the value of the information. Because technology and information handling needs and requirements vary between different users or applications, information handling systems may also vary regarding what information is handled, how the information is handled, how much information is processed, stored, or communicated, and how quickly and efficiently the information may be processed, stored, or communicated. The variations in information handling systems allow for information handling systems to be general or configured for a specific user or specific use such as financial transaction processing, airline reservations, enterprise data storage, or global communications. In addition, information handling systems may include a variety of hardware and software components that may be configured to process, store, and communicate information and may include one or more computer systems, data storage systems, and networking systems.
These same information handling systems have played a key role in the rapid growth of electronic commerce on the Internet. One known aspect of electronic commerce is affiliate networks, which allow online merchants to reach a larger audience through participation in various affiliate programs. Typically, potential customers are referred to the merchant's website from an affiliate's web site, which receives a share of any resulting sale as compensation for the referral. Various affiliate network services and benefits generally include referral tracking, reporting tools, payment processing, and access to a large base of participants. Over time, affiliate networks have made progress in simplifying the process of registering affiliate participants fore or more merchant affiliate programs. However, affiliates still face integration challenges when attempting to provide their users a customized subset of the merchant's website.
In recent years, information handling systems have also been instrumental in the widespread adoption of social media into the mainstream of everyday life. Social media commonly refers to the use of web-based technologies for the creation and exchange of user-generated content for social interaction. As such, it currently accounts for approximately 22% of all time spent on the Internet. More recently, various aspects of social media have become an increasingly popular for enabling customer feedback, and by extension, have likewise evolved into a viable marketing channel for vendors. This new marketing channel, sometimes referred to as “social marketing,” has proven to not only have a higher customer retention rate than traditional marketing channels, but to also provide higher demand generation “lift.”
Other aspects of social marketing include campaign management and website optimization. Campaign management allows organizations and marketers to segment populations of prospects and customers into smaller groups that exhibit the same, or similar, set of characteristics. Interactions with individuals within these segments are then specified to increase the likelihood of a positive outcome (e.g., make a purchase, respond to social advocacy, etc.). Such interactions may be a single point of contact or a series of contacts across one or more mediums over a period of time. Therefore a campaign defines the ‘how, when, and where’ to communicate to a prospect or customer. In contrast, website optimization is the process of matching appropriate interfaces (e.g., a webpage, content, theme, offer, etc.) to a segment within a predetermined interaction (e.g., an email, tweet, wall post, web landing page, etc.) to increase the likelihood of a positive outcome (e.g., a purchase, rating or review, watch a video, a sign-up, etc.). Accordingly, website optimization is the ‘what’ to communicate to the user once you have made contact with them within a specific interaction point. As a result, website optimization can have a direct effect on the success of a campaign.

SUMMARY OF THE INVENTION

A method and system are disclosed for optimizing webpages and managing campaigns in a syndicated commerce environment. In various embodiments, online store owners are provided the opportunity to increase sales by creating, scheduling and executing marketing campaigns that drive traffic to online stores, blogs and widgets. In these and other embodiments, social data associated with a first user is used to determine their user characteristics. These characteristics are then used to identify a set of second users that possess the same characteristics. The past online behavior of the set of second users is analyzed to identify online conversion actions they have performed. In turn, the online conversion actions are analyzed to identify campaign data associated with a campaign goal, which is then provided to the first user.
In various embodiments, the user characteristics may include social graph, demographic and geo-location data, as well prior and current online site behavior, prior conversion history and prior purchase history. In these and other embodiments, the conversion actions may comprise purchasing a product, navigating to a target webpage, downloading predetermined content, performing a user gesture within a user interface (UI), clicking through a hypertext mark-up language (HTML) link, or performing a social media action.
Likewise, the campaign data may comprise a predetermined Uniform Resource Locator (URL) embedded in a personalized email message, a display ad, or other content. In one embodiment, the campaign data comprises a search term that returns a predetermined. URL when used in a search engine. In these and other embodiments, the campaign data is provided to one or more endpoints, including an online store, a widget, a social media site, a mobile device, or a kiosk. In various embodiments, the campaign goal may comprise improvements in email click-through rates, landing page conversion rates, social media actions, registrations and coupon downloads. In these and other embodiments, the campaign goal may likewise include improvements in purchase levels, average order values, and revenues.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention may be better understood, and its numerous objects, features and advantages made apparent to those skilled in the art by referencing the accompanying drawings. The use of the same reference number throughout the several figures designates a like or similar element.

FIG. 1 is a generalized illustration of the components of an information handling system as implemented in the system and method of the present invention;

FIG. 2 is a simplified block diagram showing the implementation of a social commerce marketing system;

FIG. 3 is a simplified block diagram showing a high-level architecture of a social commerce marketplace system;

FIGS. 4 a-c are a simplified block diagram showing a plurality of social commerce modules implemented within a plurality of host environments;

FIG. 5 is a generalized flowchart of social commerce initiation operations performed on behalf of an affiliate;

FIGS. 6 a-d are generalized depictions of social commerce initiation operations performed on behalf of an affiliate within a plurality of user interface windows;

FIG. 7 is a generalized flowchart of the performance of social commerce operations;

FIG. 8 is a generalized flowchart of the performance of social commerce advertising network management operations;

FIGS. 9 a-b show the creation of an affiliate offer within a user interface window;

FIG. 10 shows the display of affiliate offers within a user interface window;

FIG. 11 shows the display of affiliate network feeds and associated offers within a user interface window;

FIG. 12 is a generalized flowchart of the performance of content syndication operations;

FIG. 13 is a generalized flowchart of the performance of billboard management operations;

FIG. 14 is a generalized flowchart of the performance of product categorization operations;

FIG. 15 is a generalized flowchart of the performance of product moderation operations;

FIG. 16 is a generalized flowchart of the performance of campaign management operations;

FIG. 17 is a generalized flowchart of the performance of multiple endpoint publication operations;

FIG. 18 is a generalized flowchart of the performance of landing page optimization operations;

FIG. 19 is a generalized flowchart of the performance of web content targeting operations;

FIG. 20 is a generalized flowchart of the performance of recommended layout operations;

FIG. 21 is a generalized flowchart of the performance of product-to-merchandise operations;

FIG. 22 is a generalized flowchart of the performance of social shopping operations; and

FIG. 23 is a generalized flowchart of the performance of user-generated content (UCG) targeting.

DETAILED DESCRIPTION

A method and system are disclosed for optimizing webpages and managing campaigns in a syndicated commerce environment. For purposes of this disclosure, an information handling system may include any instrumentality or aggregate of instrumentalities operable to compute, classify, process, transmit, receive, retrieve, originate, switch, store, display, manifest, detect, record, reproduce, handle, or utilize a form of information, intelligence, or data for business, scientific, control, or other purposes. For example, an information handling system may be a personal computer, a network storage device, or any other suitable device and may vary in size, shape, performance, functionality, and price. The information handling system may include random access memory (RAM), one or more processing resources such as a central processing unit (CPU) or hardware or software control logic, ROM, and/or other types of nonvolatile memory. Additional components of the information handling system may include one or more disk drives, one or more network ports for communicating with external devices as well as various input and output (I/O) devices, such as a keyboard, a mouse, and a video display. The information handling system may also include one or more buses operable to transmit communications between the various hardware components.
FIG. 1 is a generalized illustration of an information handling system 100 that can be used to implement the system and method of the present invention. The information handling system 100 includes a processor (e.g., central processor unit or “CPU”) 102, input/output (I/O) devices 104, such as a display, a keyboard, a mouse, and associated controllers, a hard drive or disk storage 106, and various other subsystems 108. In various embodiments, the information handling system 100 also includes network port 110 operable to connect to a network 140, which is likewise accessible by a service provider server 142. The information handling system 100 likewise includes system memory 112, which is interconnected to the foregoing via one or more buses 114. System memory 112 further comprises operating system (OS) 116 and in various embodiments may also comprise a social commerce marketplace system 118, a plurality of social commerce affiliate management modules 120, a plurality of merchant/network management modules 122, and a merchant online cart/checkout system 124. In one embodiment, the information handling system 100 is able to download the social commerce marketplace system 118, the plurality of social commerce affiliate management modules 120, the plurality of merchant/network management modules 122, and the merchant online cart/checkout system 124 from the service provider server 142. In another embodiment, the social commerce marketplace system 118, the plurality of social commerce affiliate management modules 120, the plurality of merchant/network management modules 122, and the merchant online cart/checkout system 124 is provided as a service from the service provider server 142.
FIG. 2 is a simplified block diagram showing the implementation of a social commerce marketing system in accordance with an embodiment of the invention. In this embodiment, a social commerce marketplace system 118 is implemented with a plurality of social commerce affiliate management modules 120, a plurality of merchant/network management modules 122, a merchant online cart/checkout system 124. In these and other embodiments, the plurality of social commerce affiliate management modules 120 are accessed and used by a plurality of affiliates 214. Likewise, the plurality of social commerce affiliate management modules 120 comprises a blog/site management module 218, a social network management module 222, and a mobile delivery management module 222. The plurality of social commerce affiliate management modules 120 likewise comprises a hosting management module 224, a social commerce management module 226, and a marketing management module 228.
In one embodiment, the blog/site management module 214 is used by the plurality of affiliates 214 to manage the posting and linking of social commerce content from the affiliate's online blog or website to the social commerce marketplace system 118. In another embodiment, the social network management module 220 is used by the plurality of affiliates 214 to manage the linkages between one or more social media environments and the social commerce marketplace system 118. In yet another embodiment, the mobile delivery management module 222 is used by the plurality of affiliates 214 to manage the delivery of social commerce content to a mobile device. In still another embodiment, the hosting management module 224 is used by the plurality of affiliates 214 to manage the hosting environment(s) of a customized social commerce storefront associated with the affiliate and the merchant. In one embodiment, the social commerce management module 226 is used by the plurality of affiliates 214 to perform social commerce management operations as described in greater detail herein. In yet another embodiment, the marketing management module 228 is used by the plurality of affiliates 214 to perform social commerce marketing operations, as likewise described in greater detail herein.
In various embodiments, the plurality of merchant/network management modules 122 are accessed and used by a plurality of merchant administrators 230. In these and other embodiments, the plurality of merchant/network management modules 122 comprises a merchant/network management module 234, and a social commerce moderation module 236. Likewise, the plurality of merchant/network management modules 122 comprises asocial commerce reporting module 238, a targeting module 240, and an incentives module 242.
In one embodiment, the merchant/network management module is used by is used by the plurality of merchant administrators 230 to manage a plurality of affiliate social commerce storefronts and a plurality of affiliate networks 204. In another embodiment, the moderation management module 236 is used by the plurality of merchant administrators 230 to monitor and moderate social commerce content and associated social media content related to the plurality of affiliates 214. In yet another embodiment, the social commerce reporting module 238 is used by the plurality of merchant administrators 230 to administer and deliver a plurality of social commerce reports as described in greater detail herein. In one embodiment, the targeting module 240 is used by the plurality of merchant administrators 230 to perform targeted advertising and promotion operations familiar to those of skill in the art and described in greater detail herein. In another embodiment, the incentives module 242 is used by the plurality of merchant administrators 230 to manage the accounting and payment of incentives to the plurality of affiliates 214 as compensation for referring customers to the merchant. As described in greater detail herein, the plurality of social commerce affiliate management modules 120 and the plurality of merchant/network management modules 122 may include additional modules and the foregoing is not intended to limit the spirit, scope or intent of the invention.
Referring now to FIG. 2, a plurality of users, such as customers 202, are referred by a plurality of affiliate networks 204 to the social commerce marketplace system 118 as described in greater detail. Once referred, the customers 202 are presented with a customized social commerce storefront that is associated with an individual affiliate of the plurality of affiliates 214 and the merchant. In various embodiments, each of the customized social commerce storefronts comprises a micro catalog 208 of purchasable products, which is a subset of a master catalog 210 comprising a set of available products. In these and other embodiments, and as likewise described in greater detail herein, the customized social commerce storefronts comprise social commerce content related to the purchasable products. In these various embodiments, the customers 202 review the social commerce content and select individual purchasable products for purchase. Once selected, an online purchase transaction familiar to skilled practitioners of the art is completed with the merchant online cart/checkout system 124.
FIG. 3 is a simplified block diagram showing a high-level architecture of a social commerce marketplace system as implemented in accordance with an embodiment of the invention. In this embodiment, the architecture a social commerce marketplace system 118 comprises infrastructure 302, data 304, application 306 and presentation 308 layers. As shown in FIG. 3, the infrastructure 302 layer comprises feeds from affiliate networks 316, as described in greater detail herein, and other networks 318, such as advertising networks known to those of skill in the art. The infrastructure 302 layer likewise comprises a local application fabric 314, as likewise known to those of skill in the art, a plurality of application programming interfaces (APIs) 312, and a plurality of databases 310, as described in greater detail herein. The data 304 layer likewise comprises repository classes 320, which are used for the exchange of data between the data 304 and infrastructure 302 layers.
Likewise, the application 306 layer comprises host environments 322, which in turn comprise a tenancy management module 324, a product catalog management module 326, and a product search module 328. The host environments 322 likewise comprise a stores management module 330, a commission management module 332, and a caching module 334. Likewise, the host environments 322 comprise an auditing module 336, a notifications module 338, a search engine optimization (SEO) module 340, a security management module 342, a moderation management module 344, and other modules 346 as described in greater detail herein.
In one embodiment, the tenancy management module 324 is used by merchant administrators to manage a plurality of affiliate tenancies in a virtual environment. In another embodiment, the product catalog management module 326 is used to manage available products in a master catalog and purchasable products, which are subsets of the available products, in micro catalogs as described in greater detail herein. In yet another embodiment, the product search module 328 is used with various other modules in the initiation, provisioning, and management of affiliate storefronts. In still another embodiment, the commission management module 332 is used to track, account, and pay commissions to affiliates as compensation for referring customers to the merchant. In one embodiment, the caching module 334 is used to cache social commerce content and other data related to conducting social commerce operations.
In another embodiment, the auditing module 336 is used to audit social commerce transactions that are performed within the social commerce marketplace system. In yet another embodiment, the notifications module 338 is use to manage notifications to affiliates as well as users referred by the affiliates to the social commerce marketplace system. In still another embodiment, the SEO module 340 is used to perform SEO operations known to skilled practitioners of the art. In this embodiment, the SEO operations, as described in greater detail herein, are performed to optimize the identification of a purchasable product according to the search terms used by either an affiliate or a user of a social media environment. In one embodiment, the security module is used to maintain the security of the social commerce marketplace system. In another embodiment, the moderation module 344 is used to monitor and moderate social commerce content and associated social media content related to a plurality of affiliates. In yet another embodiment, the other modules 346 comprise additional modules, as described in greater detail herein, that operate within the host environments 322.
In various embodiments, the presentation 308 layer comprises a Representational State Transfer (REST) application program interface (API) 348 known to skilled practitioners of the art. In these and other embodiments, the presentation 308 layer likewise comprises a controller module 350 a presentation model 352, a presentation view 354, and a plurality of administration 356 and affiliate storefront 358 sites. In these various embodiments, the controller module 350 interacts with the presentation model 354 and presentation view 354, which likewise interact with each other, to present different aspects of the plurality of administration 356 and affiliate storefront 358 sites. Likewise, the presentation view 354 module provides feedback to the controller module 350.
Referring now to FIG. 3, the presentation 308 layer comprises manager classes 350 and the application 306 layer comprises domain services. The manager classes 360 provide presentation layer data to the service contracts module 362, which is then used for the management of the domain service 364. In turn, the domain services 364 provide application layer data to the repository contracts module 366, where it is used for the management of the repository classes 320. Likewise, the service contracts module 362 and the repository contracts module 366 are managed and bounded by a dependencies module 368. In turn, the dependencies module 368 is managed with the logging 370, caching 372, and auditing 374 management modules.
FIGS. 4 a-c show a simplified block diagram of a plurality of social commerce modules implemented within a plurality of host environments in accordance with an embodiment of the invention. In this embodiment, the host environments 322 comprise social media store 4001, affiliate storefront 4002, blog 4003, templates 4004, content 4006, notifications 4010, uniform resource locator (URL) 4011, reputation 4012, and search engine optimization (SEO) 4017 management modules. Likewise, the host environments 322 comprise catalog 4026, links 4035, web analytics 4038, fraud 4042, payment 4048, administration 4054, reports 4063, widget 4070, campaign management 4087, and web optimization 4101 management modules.
In one embodiment, the social media store 4001 management module is used to manage a social commerce storefront that is associated with an affiliate's presence and activities within a social media environment. In another embodiment, the affiliate storefront 4002 management module is used to manage a social commerce storefront that is associated with an affiliate's web site or online biog. In yet another embodiment, the blog 4003 management module is used to manage an affiliates blog activities as it relates to social commerce activities, processes and operations as described in greater detail herein. In still another embodiment, as likewise described in greater detail herein, the templates 4004 management module is used for the automated configuration of social commerce storefront pages. In one embodiment, the notifications 4010 management module is used for the management of notifications to affiliates and users associated with affiliates, such as users of an affiliate's online social commerce presence. In various embodiments, the affiliate's online presence may comprise a blog, a website, or a community of interest or conversation thread in a social media environment. In another embodiment, the URL 4011 management module is used to manage URL links between the host environments 322 and the affiliate's various online social commerce presences.
In yet another embodiment, the content 4005 management module further comprises articles 4006, podcast 4007, pictures 4008, and video 4009 management sub-modules. In this and other embodiments, the articles 4006, podcast 4007, pictures 4008, and video 4009 management sub-modules are used by affiliates to manage their respective, associated content as it relates to social commerce operations. In still another embodiment, the reputation 4012 management module comprises points 4013, badges 4014, activity 4015, and score 4016 management sub-modules. In this and other embodiments, the reputation 4012 management module comprises points 4013, badges 4014, activity 4015, and score 4016 management sub-modules are used by the merchant to manage reputation data associated with affiliates. As used herein, reputation data refers to data associated with social commerce activities performed by an affiliate. As an example, an affiliate may receive points from a merchant for each item of social commerce content they product. Likewise, badges may be awarded upon achievement of various point tiers or frequency of activity. Likewise, each social commerce content item may receive a score that is associated with the achievement of the points and badges. It will be appreciated that many such examples are possible and the foregoing is not intended to limit the spirit, scope, or intent of the invention.
In one embodiment, the SEO management 4017 module comprises backlinks 4018, rank 4019, competition 4020, search application program interface (API) 4021, keyword density 4022, keyword placement 4023, keyword insertion 4024, and content comparison 4025 management sub-modules. In this and other embodiments the various sub-modules of the SEO management 4017 module are used by affiliates and the merchants to perform SEO operations familiar to those of skill in the art. As an example, the backlinks 4018 management sub-module may be used to determine prior web site locations that a user has visited prior to being referred to an affiliate's social commerce storefront. Likewise, the rank 4019 management sub-module may be used to determine the search engine rank assigned to the affiliate's social commerce storefront as well as the individual search engine ranking of the search terms that resulted in the referral. As another example, the competition 4020 management sub-module may be used by the merchant to rank the search engine popularity of their competitors, or alternatively, the frequency that a competitor's web site is returned as a result of a search by a user of a social media environment. Likewise, the search API 4021 management sub-module may be used by the merchant and affiliates alike to gain access to various search engines in order to receive search metadata. As yet another example, the keyword density 4022, placement 4023, and insertion 4024 management sub-modules may likewise be used by the merchant and the affiliates to optimize searches through the use of predetermined keywords within related social commerce content. As still another example, the content comparison 4025 sub-module may be used to compare various items of social commerce content to determine which items perform better than others during SEO operations.
In another embodiment, the catalog 4026 management module comprises filter 4027, search 4028, price 4029, taxonomy 4030, import 4031, differential 4032, categories 4033, and deals 4034 management sub-modules. In this and other embodiments, the filter 4027, search 4028, price 4029, taxonomy 4030, import 4031 differential 4032, categories 4033, and deals 4034 management sub-modules are used by the affiliate for managing their social commerce storefronts. For example, the filter 4027, search 4028, price 4029, differential 4032, deals 4034, and import 4031 management sub-modules may be used individually, or in combination, to identify and populate a set of purchasable products within a micro catalog from a set of available products contained in a master catalog. Likewise, the taxonomy 4030 and categories 4033 management sub-modules may be used to understand the interrelationship of various purchasable products and how they are categorized within the affiliate's social commerce storefront. It will be appreciated that many such examples are possible and the foregoing is not intended to limit the spirit, scope, or intent of the invention.
In yet another embodiment, the links 4035 management module comprises network 4036 and system 4037 management sub-modules, which are used to manage the linkages between the various systems, modules, and sub-modules of the social commerce marketplace system and various affiliate and advertising networks. In still another embodiment, the web analytics 4038 module comprises web crawling 4039, listening 4040, and analytics 4041 management sub-modules. In this and other embodiments the web crawling 4039, listening 4040, and analytics 4041 management sub-modules are used by the merchant to perform web analytics operations familiar to skilled practitioners of the art. As an example, the merchant may use the web crawling 4039 management sub-module to perform web crawling operations to discover conversation threads associated with its products. Once discovered, the listening 4040 management sub-module may be used to monitor the conversations threads, which are then analyzed with the analytics 4041 management sub-module to determine their relevance and possible effect on social commerce operations. Those of skill in the art will be knowledgeable of many such examples. Accordingly, the foregoing is not intended to limit the spirit, scope, or intent of the invention.
In one embodiment, the fraud 4042 management module comprises an abuse reporting 4043, traffic 4044, links 4045, Internet Protocol (IP) 4046, and dashboard 4047 management sub-modules. In this and other embodiments, the abuse reporting 4043, traffic 4044, links 4045, Internet Protocol (IP) 4046, and dashboard 4047 management sub-modules are used by the merchant to identify, mitigate, and prevent fraudulent behavior within the social commerce market place system. As an example, the traffic 4044, links 4045, and IP 4046 management sub-modules may be used to identify the source of fraudulent behavior. Once identified, it may be reported by the abuse reporting 4043 management sub-module and then displayed for review within a user interface by the dashboard 4047 sub-module.
in another embodiment, the payment 4048 module comprises a traffic 4049, payment 4050, 1099 Form 4051, buyers 4052, and payment processor 4053 management sub-modules. In this and other embodiments, the traffic 4149, payment 4150, 1099 4151, buyers 4152, and payment processor 4153, management sub-modules are used by the merchant for the management of payment to affiliates. As an example, the buyers 4052 and traffic 4049 management sub-modules may be used to identify individual buyers and the traffic they generate at an affiliate's social commerce storefront. In turn, the payment 4050 and payment processor 4053 sub-modules may be used to track the payments made by the buyers, which are then processed by various payment processors. Likewise, the same sub-modules may be used to track commission payments made by the merchant to individual affiliates. The output of those sub-modules may then be processed by the 1099 Form 4051 sub-module for managing reporting of the commission payments to the affiliate to the Internal Revenue Service (IRS).
In yet another embodiment, the administration 4054 module comprises companies 4055, target 4056, users 4057, roles 4058, deals 4059, moderation 4060, profile 4061, and email 4062 management sub-modules. In this embodiment, the companies 4055, target 4056, users 4057, roles 4058, deals 4059, moderation 4060, profile 4061, and email 4062 management sub-modules are used by the merchant to administer the various users of the social commerce marketplace system. As an example, the target 4056 management sub-module may be used, individually or in conjunction with, the target 4056, users 4057, profile 4061, and roles 4058 management sub-modules to identify specific users of a social media environment. Once identified, their social media interactions may be monitored by the moderation 4060 management sub-module, and in turn the email 4062 and deals 4059 management sub-modules may be used individually, or in combination, to target predetermined users.
In still another embodiment, the reports module 4063 comprises traffic abuse 4064, traffic 4065, search engines 4066, users 4067, content status 4068, and competitors 4069 reporting sub-modules. In this embodiment, the traffic abuse 4064, traffic 4065, search engines 4066, users 4067, content status 4068, and competitors 4069 reporting sub-modules are used by the merchant to generate various reports related to social commerce operations, which in turn may be provided to an affiliate. As an example, the content status 4068 reporting sub-module may report on the status of various items of social commerce content and the search engines 4066 reporting sub-module may report on the search results it generates. In turn, the traffic reporting 4065 sub-module may be used to report on the social commerce traffic resulting from the search results and the users 4067 reporting sub-module may provide reports related to the various users referred to the social commerce site. Likewise, the traffic abuse reporting sub-module 4064 may be used to report on various traffic abuses related to the social commerce marketplace system, while the competitors 4069 reporting sub-module may provide reports related to competitive activity from competitors.
In various embodiments, the widgets module 4070 may comprise web crawling 4071, keyword analysis 4072, analytics 4073, widget manager 4074, data 4075, semantic analysis 4076, catalog management 4077, scoring 4078, hot spots manager 4079, sentiment analysis 4080, keyword widget 4081, social keyword widget 4082, API 4083, recommendations engine 4084, social score widget 4085 and in-line links widget 4086 sub-modules. In one embodiment, the web-crawling 4071 sub-module is implemented to perform web crawling operations to discover keywords within webpages. In another embodiment, the keyword analysis 4072 sub-module is implemented to determine related keywords, competition of keywords, search frequency of keywords, and locality of keywords. In yet another embodiment, the analytics 4073 sub-module is implemented to provide the utilization of widgets by visitors. In still another embodiment, the widget manager 4074 sub-module is implemented to provide a set of user interfaces to configure and publish a widget. In various embodiments, the widget manager 4074 sub-module is implemented to provide templates that comprise user interface (UI) themes and interactions that determine the end-user experience. In these and other embodiments, the widget manager 4074 sub-module comprises a wizard that provides a multi-step process to configure the widget. In one embodiment, the widget manager 4074 sub-module comprises a dashboard providing a UI to access the wizard, embed associated programming code, and generate related reports.
In one embodiment, the data 4075 sub-module is implemented to process social graph, user, and catalog data. In another embodiment, the semantic analysis 4076 sub-module is implemented to semantically extract keywords, topics, people and places from strings of text. In another embodiment, the catalog 4076 sub-module is implemented with a widget to process catalog data. In yet another embodiment, the hot spots manager 4077 sub-module comprises a set of user interfaces to configure and publish images and videos that contain hot spots. In still another embodiment, the sentiment analysis 4078 sub-module is implemented to extract positive, neutral and negative tone from strings of text. In one embodiment, the page keyword widget 4079 sub-module is implemented to provide a widget that automatically matches catalog products to the context of keywords extracted from a webpage. In another embodiment, the social keyword widget 4080 sub-module is implemented to provide a widget that automatically matches catalog products to a user's context by matching keywords and themes from their social graph. In yet another embodiment, the API 4081 sub-module is implemented to provide an API between a widget and various operating environments. In still another embodiment, the recommendation engine 4082 sub-module is implemented to automatically select or recommend objects that best match the user's context based on a set of algorithms. In one embodiment, the social score widget 4083 sub-module is implemented to provide a widget that dynamically presents catalog products and discounts according to a user's social score. In yet another embodiment, the in-line links widget 4084 sub-module is implemented to provide a widget that automatically creates in-line hyperlinks within text strings based on keywords that match objects from a catalog. It will be appreciated that many such embodiments are possible and the foregoing is not intended to limit the spirit, scope or intent of the invention.
In various embodiments, the campaign management module 4087 comprises campaigns 4088, offers 4089, data 4090, catalog 4091, scoring 4092, goals 4093, social graph 4094, recommendations engine 4095, publisher 4096, testing 4097, traffic 4098, scheduler 4099, and interactions management sub-modules.
In one embodiment, the campaigns 4088 management sub-module is used to create, schedule and manage on-line marketing campaigns that drive traffic to an affiliate's websites, blogs and social media networks. In another embodiment, the campaigns 4088 management sub-module is used to create an email marketing campaign. In yet another embodiment, the campaigns 4088 management sub-module is used to create dynamic landing web pages using behavioral targeting approaches to determine which content, layout and calls-to-action to present to each visitor. In still another embodiment, the campaigns 4088 management sub-module is used to post offers to users accounts (e.g., “walls”) within social networking sites such as Facebook®. In another embodiment, the campaigns 4088 management sub-module is used to post content and offers via social sites like Twitter®. In yet another embodiment, the campaigns 4088 management sub-module is used to select the landing page that campaign recipients will visit when they click-through an offer by an email, tweet, Facebook® post, etc.).
In another embodiment, the offers 4089 management sub-module is used to select predetermined products and services from one or more catalogs 4091. The selected products are then presented to a campaign recipient. In yet another embodiment, the data 4090 management sub-module is used to select the delivery medium of a campaign, such as email, search marketing, display ad, or social media. In yet another embodiment, the data 4090 management sub-module is used to select the content and creative assets that are displayed to users that view or otherwise respond to the campaign. In yet another embodiment, the offers 4089 management sub-module is used to select the delivery end-points (e.g., Facebook®, Twitter®, etc.) that the campaign will be transported through to reach the intended user(s).
In yet another embodiment, the data 4090 management sub-module is used to select the list of users that are targeted to receive a predetermined campaign. In still another embodiment, the data 4090 management sub-module is used to select the segment of users that are targeted to receive a targeted campaign. In yet another embodiment, the data 4090 management sub-module is used to store and manage the data collected from the recipients of the campaign via the interactions 4100 management sub-module. In yet another embodiment, the data 4090 management sub-module is used to store and manage the versions of landing pages (e.g., their respective content, layout, style, etc.) that are used presented to users. In yet another embodiment, the data 4090 management sub-module is used to store and manage user's social graph information.
In still another embodiment, the catalog 4091 management sub-module is used to select which products to include in the campaign's offers. In yet another embodiment, the catalog 4091 management sub-module is used to select which merchants to include in the campaign's offers.
In one embodiment, the scoring 4092 management sub-module is used to select a list of targeted campaign recipients by using various algorithms to calculate the economic value of each recipient, which is then ranked. In yet another embodiment the scoring 4092 management sub-module is used to select a list of targeted recipients by using various algorithms to calculate the social influence of each recipient, which is then ranked to determine the prioritized order of which recipients receive the campaign. In still another embodiment, the scoring 4092 management sub-module is used to select a list of targeted recipients by using various algorithms to calculate the conversion uplift of similar segments and similar campaigns within a predetermined group of accounts, which are then rank ordered.
In another embodiment, the goals 4093 management sub-module is used to create the success criteria that campaigns will be measured against. In yet another embodiment, the goals 4093 management sub-module is used to measure the response to the campaign against predefined business goals (e.g., success criteria) to determine the success of the campaign. In still another embodiment, the goals 4093 management sub-module is used to measure the interactions rates and success criteria for a specific channel (e.g., a delivery medium such as Facebook®, email, etc.). In yet another embodiment, the goals 4093 management sub-module is used compare a campaign's success rate(s) against averages across a predetermined group of accounts. In yet another embodiment, the goals 4093 management sub-module is used to suggest combinations of campaigns and offers that are likely to meet the success criteria based upon a predetermined group of accounts. In various embodiments, the business goals and success criteria may include email click through rates, landing page conversion rates, traffic volume to a predetermined landing page, mentions and re-tweets, purchases, average order value, revenue, leads, and so forth. It will be appreciated that many such business goals and success criteria are possible and the foregoing is not intended to limit the spirit or scope of the invention.
In yet another embodiment, the social graph 4094 management sub-module is used to pull a campaign recipient's data from one or more social networks (e.g., Facebook®, Twitter®, Google+®) via APIs such that it can be used by the scoring 4092 management sub-module to determine which users should receive the campaign.
In still another embodiment, the recommendation engine 4095 sub-module utilizes algorithms to dynamically select which offers provided by the offers 4089 management sub-module (e.g., products, etc.) each campaign recipient should be presented within their respective landing pages to increase the likelihood of uplift. In yet another embodiment, the recommendation engine 4095 sub-module utilizes algorithms to select which data provided by the data 4090 management sub-module (e.g., landing page layout, content, etc.) each campaign recipient should be presented within their respective landing pages to increase the likelihood of uplift. In still another embodiment, the recommendation engine 4095 sub-module is used to suggest what campaigns and offers an account should consider using to meet success criteria goals provided by the goals 4093 management sub-module.
In one embodiment, the publisher 4096 management sub-module is used to select when campaign interactions provided by the interactions 4100 management sub-module are to be made available to campaign recipients. In yet another embodiment, the publisher 4096 management sub-module is used with the recommendation engine 4095 management sub-module to select a publishing schedule of interactions provided by the interactions 4100 management sub-module that would result in a higher likelihood of meeting or exceeding goals success criteria goals provided by the goals 4093 management sub-module. In yet another embodiment, the publisher 4096 management sub-module is used to select which landing pages will be published to the Internet and made available to campaign recipients according to a schedule provided by the scheduler 4099 management sub-module.
In another embodiment, the testing 4097 management sub-module is used to randomly present different combinations of interactions, offers and data to campaign recipients in order to determine which combination results in the highest attainment of predetermined goals. In yet another embodiment, the testing 4097 management sub-module is used to present different combinations of interactions, offers and data to predetermined segments of campaign recipients to determine which combination results in the highest attainment of predetermined goals. In another embodiment, the testing 4097 management sub-module provides users the ability to change or select predetermined versions of content elements within regions of published webpage to test from within the context of the webpage using standard web browsers. In yet another embodiment, the testing 4097 management sub-module is used to select a statistical confidence interval used by the social commerce marketplace system to determine a winning combination of interactions, offers and data.
In yet another embodiment, the traffic 4098 management sub-module is used in conjunction with the testing 4097 management sub-module to select the amount and frequency of traffic to direct to a predetermined combination of interactions, offers and data to control the presentation of campaigns to visitors.
In still another embodiment, the scheduler 4099 management sub-module is used to determine when recipients will be exposed to a campaign and the frequency of follow up interactions. In another embodiment, the scheduler 4099 management sub-module is used to determine when landing pages will be published and unpublished for a predetermined campaign. In yet another embodiment, the scheduler 4099 management sub-module is used to determine when offers will be published and unpublished for a predetermined campaign.
In one embodiment, the interactions 4100 management sub-module is implemented as an instance of the user interface (UI) within a predetermined channel for a predetermined campaign recipient (e.g., an offer in combination with an end point). In one embodiment, the interactions 4100 management sub-module is used to record individual recipient engagements with a campaign to capture data (e.g., when, which end-point, did the user click through an offer, did the user convert, etc.) that the goals 4093 management sub-module uses to determine whether the business goals have been met or exceeded.
In various embodiments, the web optimization module 4101 comprises web crawling 4102, analytics 4103, data 4104, catalog 4105, scoring 4106, sentiment analysis 4107, social graph 4108, recommendations engine 4109, offers 4110, publisher 4111, testing 4112, ratings 4113, reviews 4114, social shopping 4115, listening 4116, and traffic management sub-modules.
In one embodiment, the web crawling 4102 management sub-module is used to capture a website's page content, layouts, creative assets and calls-to-action. In another embodiment, the web crawling 4102 management sub-module is used with the recommendation engine 4109 and testing 4112 management sub-modules to recommend content to use within webpages that have been statistically proven to increase uplift for the source site. In yet another embodiment, the web crawling 4102 management sub-module is used to crawl the Internet and social network sites for product ratings and reviews.
In another embodiment, the analytics 4103 management sub-module is used to capture the visitor click-stream data that is used by the recommendations engine 4109 management sub-module to determine which content, layout, and calls-to-action are most likely to result in increased visitor engagement (e.g., amount of time on-site, page views, etc.) and conversions.
In yet another embodiment, the data 4101 management sub-module is used to retrieve prior purchase history information for a visitor, which in turn is used by the recommendation engine 4109 management sub-module to determine which content, layout, and calls-to-action are most likely to result in increased visitor engagement (e.g., time on-site, page views, etc.) and a conversion event for the current site visitor or other visitors with similar characteristics. In yet another embodiment, the data 4101 management sub-module is used to retrieve current temporal information and end user device information for a visitor, which in turn is used by the recommendation engine 4109 management sub-module to determine which content, layout, and calls-to-action are most likely to result in increased visitor engagement e.g., time on-site, page views, etc.) and a conversion event for the current site visitor or other visitors with similar characteristics.
In still another embodiment, the catalog 4105 management sub-module is used to select which products and offers to present to a visitor as determined by the recommendation engine 4109 management sub-module that are most likely to result in increased visitor engagement (e.g., time on-site, page views, etc.) or a conversion event.
In one embodiment, the scoring 4106 management sub-module is used to determine the social influence of a visitor such that the recommendations engine 4109 management sub-module is able to determine which content (e.g., products and offers), layouts, and calls-to-action to present to the user that is most likely to result in increased visitor engagement (time on site, page views), or a conversion event for a calculated social influence score. In yet another embodiment, the scoring 4106 management sub-module is used with the sentiment analysis 4107 management sub-module to calculate trends of topics and products that are then provided as recommendations to users to include within their websites to capitalize upon the trend.
In another embodiment, the sentiment analysis 4107 management sub-module is used to analyze the tone of a website, page, blog, content or social post to determine the positive, neutral or negative tonality about the topics within the content. The sentiment analysis 4107 management sub-module then aggregates tonality analysis across multiple sites, social posts, social networks, etc. to identify market trends for products and services. In turn, the recommendation engine 4109 management sub-module recommends products to merchandize in order to capitalize upon the market trends. In another embodiment, the sentiment analysis 4107 management sub-module is used to identify customer support, product and service satisfaction issues for the website owner to remedy. In another embodiment, the sentiment analysis 4107 management sub-module is used with the social graph 4108 management sub-module to determine positive topics of interest for a user and their network of users, which are then used to target predetermined content and products that match the users' topics of interest.
In yet another embodiment, the social graph 4108 management sub-module is used to retrieve visitor social data and social relationship data such that the recommendation engine 4109 management sub-module can determine which content (e.g., products and offers), layouts, and calls-to-action to present to the user that are most likely to result in increased visitor engagement (e.g., time on-site, page views, etc.) or a conversion event based upon the users social graph information. It another embodiment, the social graph 4108 management sub-module processes a user's social graph data to match it with other users that have similar social graph characteristics.
In still another embodiment, the recommendation engine 4109 management sub-module is used to determine which content (e.g., products and offers), layouts, and calls-to-action to present to the user that are most likely to result in increased visitor engagement (e.g., time on-site, page views, etc.) or a conversion event. In this and other embodiments, the recommendation engine 4109 management sub-module uses inputs from the analytics 4013, data 4104, catalog 4105, social graph 4108, and scoring 4106 management sub-modules as inputs into an algorithm for the afore-mentioned operations.
In one embodiment, the offers 4110 management sub-module is used to select which products and offers to present to a user that are most likely to result in increased engagement (e.g., time on-site and conversions). In another embodiment, the offers 4110 management sub-module is used to match the highest rated products as found by the web crawler 4102 management sub-module with products from the catalog 4105 management sub-module to present to a user.
In another embodiment, the publisher 4111 management sub-module is used to schedule the automatic publishing and un-publishing of web pages. In yet another embodiment, the publisher 4111 management sub-module is used to schedule the automatic publishing and un-publishing of products provided by the catalog 4105 management sub-module within predetermined web pages. In still another embodiment, the publisher 4111 management sub-module is used to schedule the automatic publishing and un-publishing of content variations within predetermined webpages to keep the website fresh.
In yet another embodiment, the testing 4112 management sub-module is used to randomly present different combinations of data, page layout, content and visual style within webpages to visitors to determine which combination results in the highest level of engagement (e.g., time on-site or conversions). In another embodiment, the testing 4112 management sub-module provides users the ability to change or select predetermined versions of content elements within regions of a published webpage in order to use standard web browsers to directly test from within the context of the webpage. In yet another embodiment, the testing 4112 management sub-module is used to select the statistical confidence interval the social commerce marketplace system used to determine a winning combination of data, page layout, content and visual style to automatically publish the winning version to the webpage.
In still another embodiment, the ratings 4113 management sub-module is used to dynamically select which product ratings format to display to the user that is most likely to result in higher engagement for the visitor. In another embodiment, the ratings 4113 management sub-module is used to select which products to display within a web page based upon the correlation between the products ratings and user click-through rates. In yet another embodiment, the ratings 4113 management sub-module is used to identify trends in product ratings to recommend when to add or remove a predetermined product or class of products from a website to optimize engagement.
In one embodiment, the reviews 4114 management sub-module is used to dynamically select which product reviews format to display to the user that is most likely to result in higher engagement for the visitor. In another embodiment, the reviews 4114 management sub-module is used to select which products to display within a web page based upon the correlation between the products reviews and user click-through rates. In yet another embodiment, the reviews 4114 management sub-module is used to identify trends in product reviews to recommend when to add or remove a predetermined product or class of products from a website to optimize engagement. The reviews 4114 management sub-module uses the sentiment analysis 4107 management sub-module to determine positive, neutral or negative sentiment towards a specific product. In still another embodiment, the reviews 4114 management sub-module uses the predictive scoring 4106 management sub-module to deter the direction and magnitude of market trends for each product managed by the catalog 4105 management sub-module.
In another embodiment, the social shopping 4115 management sub-module implements JavaScript® into third party website pages to display user interface (UI) controls next to products that list users in their social network that have indicated in their social network (e.g., Facebook®) that they own the product. In another embodiment, the social shopping 4115 management sub-module shows which users within their social network have visited the webpage and when. In another embodiment, the social shopping 4115 management sub-module displays a list of individuals within their social network that have used the same referring search keyword term that the user used to reach the webpage. In yet another embodiment, the social shopping 4115 management sub-module lists individuals, and their associated content, that have expressed an opinion about the product within their social networks (e.g., ratings, reviews, mentions, etc.).
In yet another embodiment, the listening 4116 management sub-module is used with the crawling 4102 management sub-module to find specific information on websites and within social network sites matching products with a catalog managed by the catalog 4105 management sub-module. In another embodiment, the listening 4116 management sub-module provides users with content ideas, content fragments, and user interface designs to consider using within their websites, based upon content collected by the web crawler 4102 management sub-module. In yet another embodiment, the listening 4116 management sub-module searches for social network mentions (e.g., Twitter® tweets or Facebook® wall posts) and provides them to the sentiment analysis 4107 management sub-module to identify positive content that the recommendations engine 4109 management sub-module uses to recommend which products to merchandize.
In still another embodiment, the traffic 4098 management sub-module is used in conjunction with testing 4112 management sub-module to select the amount and frequency of traffic to direct to a specific combination of data, layout, content and calls-to-action to control the presentation of webpage user interfaces to visitors.
FIG. 5 is a generalized flowchart of social commerce initiation operations performed on behalf of an affiliate in accordance with an embodiment of the invention. In this embodiment, affiliate social commerce operations are begun in step 502, followed by a candidate affiliate providing information to a merchant in step 504 to register as an affiliate. The merchant then uses the provided information to create a social commerce storefront for the affiliate in step 506. The affiliate then proceeds to select a product to add to their social commerce storefront in step 508. In various embodiments, the product is selected from a plurality of available products contained in a master catalog. The selected product is then added to the affiliate's social commerce storefront in step 510. In various embodiments, a selected product becomes a purchasable product once it is added to the affiliate's social commerce storefront.
The affiliate then views their social commerce storefront in step 512, followed by a determination being made in step 514 whether to add an additional product. If so, then the process is continued, proceeding with step 508. Otherwise, an article related to one or more of the purchasable products is written in step 516 and then posted to the affiliate's social commerce storefront. The ongoing sales results of the affiliate's social commerce storefront is then tracked in step 518, as well as the ongoing ranking of its performance relative to other affiliate social commerce storefronts in step 520. Ongoing conversion of organic searches resulting in sale is likewise tracked in step 522, followed by affiliate social commerce initiation operations being ended in step 524.
FIGS. 6 a-d are generalized depictions of social commerce initiation operations performed on behalf of an affiliate within a plurality of user interface windows in accordance with an embodiment of the invention. In this embodiment, a social commerce storefront management module, as described in greater detail herein, is implemented within a window 604 of a user interface (UI) 602. As shown in FIG. 6 a, the UI window 604 comprises data entry fields 606 for a candidate affiliate to provide information to initiate the creation of a social commerce storefront. Referring now to FIG. 6 b, the affiliate then provides additional information 610 associated with their social commerce storefront. As shown in FIG. 6 c, the affiliate selects the category 614 of their social commerce storefront, and as likewise shown in FIG. 6 d, selects products 614 to be displayed for purchase within their social commerce storefront.
FIG. 7 is a generalized flowchart of the performance of social commerce operations as implemented in accordance with an embodiment of the invention. In this embodiment, social commerce operations are begun in step 702, followed by the affiliate managing the integration of the social commerce storefront in step 704 with a social media environment, such as asocial network. In various embodiments, the integration may be with an affiliate web site or biog. The affiliate then launches the social commerce storefront in step 706, followed by the importation of friends, family, and associates from one or more social media environments (e.g., a social network) in step 708. The friends, family and associates are then matched with products that are appropriate to their interests in step 710, followed by a determination in step 712 whether to create a promotional offer for them. If so, then a promotional offer is created in step 714 and the process is continued, proceeding with step 712. Otherwise, a determination is made in step 716 whether the affiliate will offer to provide an offer to pay a commission to the friends, family or associates in return for referrals. If so, then a commission offer is prepared in step 718 and the process is continued, proceeding with step 716. Otherwise the promotion offers(s), the commission offer(s), or both, are displayed to the friends, family and associates in-line within the social media environment in step 720. Ongoing activity at the affiliate's social commerce storefront, and the corresponding success of the offer(s), is tracked in step 722 and social commerce operations are then ended in step 724.
FIG. 8 is a generalized flowchart of the performance of social commerce advertising network management operations as implemented in accordance with an embodiment of the invention. In this embodiment, advertising network operations are begun in step 802, followed by ongoing operations in step 804 for affiliate and enterprise channels to manage their online advertisements. In step 806 the affiliate and enterprise channels perform ongoing operations to select online advertisements for purchase, followed by corresponding ongoing operations in step 808 to place the purchased online advertisements in predetermined online locations. In steps 810, 812, 814, 816, and 818, the affiliate and enterprise channels perform ongoing operations respectively display the online advertisements in social commerce storefronts, online newsletters, social media channels, online billboards, and enterprise sites. Ongoing operations are then performed in step 820 to correlate sales to the placement of the online advertisements, followed by advertising network operations being ended in step 822.
FIGS. 9 a-b show the creation of an affiliate offer within a user interface window in accordance with an embodiment of the invention. In this embodiment, a social commerce storefront deals management module, as described in greater detail herein, is implemented within a window 904 of a user interface (UI) 902. As shown in FIG. 9 a, the UI window 904 comprises an ‘Offers’ tab 906, a data entry field 908 for information related to the merchant and the affiliate making the offer, and associated data entry fields 910 corresponding to details of the purchasable product. Likewise, the UI window 904 comprises data entry fields 912 corresponding to details of the offer, as well as an offer display window 914 that provides a preview of the offer as it will appear when presented within a social media environment. As likewise shown in FIG. 9 a, data display field 916 displays summary information corresponding to a related offer, and as shown in FIG. 9 b, a corresponding offer display window 918 providing a preview of the related offer, as well as data display fields 920 displaying summary information corresponding to other offers.
FIG. 10 shows the display of affiliate offers within a user interface window implemented in accordance with an embodiment of the invention. In this embodiment, a social commerce storefront deals management module, as described in greater detail herein, is implemented within a window 1004 of a user interface (UI) 1002. As shown in FIG. 10, the UI window 1004 comprises an ‘Offers’ tab 1006, as well a listing 1008 of a plurality of offers and related information 1010.
FIG. 11 shows the display of affiliate network feeds and associated offers within a user interface window implemented in accordance with an embodiment of the invention. In this embodiment, a social commerce storefront deals management module, as described in greater detail herein, is implemented within a window 1104 of a user interface (UI) 1102. As shown in FIG. 11, the UI window 1104 comprises an ‘Offers’ tab 906, as well a listing 1108 of a plurality of advertising network feeds corresponding to referrals resulting from associated offers, and related information 1110.
FIG. 12 is a generalized flowchart of the performance of content syndication operations as implemented in accordance with an embodiment of the invention. In this embodiment, content syndication operations are begun in step 1202, followed by the ongoing generation of search engine optimization (SEO) content by an affiliate, a professional author, or both, in step 1204. Ongoing syndication operations are then performed in step 1206 to syndicate the SEO content other sites and establish corresponding links. Then, in step 1208, ongoing operations are performed to post the SEO content to other content marketplaces known to skilled practitioners of the art. A determination is then made in step 1210 whether enterprises (e.g., corporations) elect to accept the SEO content. If so, then ongoing operations are performed by the enterprises in step 1212 to accept the SEO content for online publication. As an example, a corporation may elect to post predetermined SEO content on their internal web site for review by employees.
However, if it is determined in step 1210 that enterprises do not elect to accept the SEO content, or if they do so in step 1212, then a determination is made in step 1214 whether other affiliates elect to accept the SEO content for online publication. If so, then ongoing operations are performed by affiliates in step 1216 to accept the SEO content for publication in step 1217. For example, another affiliate may elect to publish SEO content that is complementary to content they generate themselves. However, if it is determined in step 1214 that other affiliates do not elect to accept the SEO content, or if they do so in step 1216, then ongoing operations are performed in step 1218 for enterprises, affiliates, or both, to post a “bounty” (i.e., an offer for compensation) for content creation. Thereafter, ongoing operation are performed in step 1220 to track authors, the content they generate, their corresponding reputation ratings, and the monetary value they receive as compensation for providing the content. Content syndication operations are then ended in step 1222.
FIG. 13 is a generalized flowchart of the performance of billboard management operations as implemented in accordance with an embodiment of the invention. In this embodiment, online billboard management operations familiar to those of skill in the art are begun in step 1320, followed by the ongoing aggregation of the most popular product content in step 1304. A micro site, such as a small, specialized web site, is then created in step 1306, followed by ongoing operations in step 1309 to determine high rankings for challenging key words used in searches for product information. Ongoing operations are then performed in step 1310 to drive traffic to affiliate social commerce storefronts, such as using the high ranking challenging key words in search engine optimization (SEO) operations known to skilled practitioners of the art. Thereafter, ongoing operations are performed in step 1312 to determine high ranking niche focus key words, followed by ongoing operations being performed by affiliates in step 1314 to drive traffic to their storefronts, and accordingly, receive compensation from a merchant for doing so. In step 1316, ongoing operations are performed by the merchant to challenge small affiliates to challenge the sales performance of larger affiliates. Online billboard management operations are then ended in step 1318.
FIG. 14 is a generalized flowchart of the performance of product categorization operations as implemented in accordance with an embodiment of the invention. In this embodiment, product categorization operations are begun in step 1402, followed by the receipt of recurring data feeds of catalog data from a vendor, merchant or other product source in step 1404. The catalog data is then processed in step 1406 to acquire stock keeping units (SKUs) related to an individual vendor, merchant or other product source, their corresponding merchant category pairs, Global Trade Item Numbers (GTINs), and manufacturer part numbers (MPNs). As used herein, a merchant category pair refers to a pairing of an individual vendor, merchant or other product source and a predetermined product category.
A SKU categorization file is then generated in step 1408, followed by the addition of a SKU category column to the SKU categorization file in step 1410. Then, in step 1412, target product catalog data feeds are consolidated into batches for processing. The consolidated product catalog data is processed to identify products that have neither a MTN nor a GTIN (MPN|GTIN). Catalog product data is then selected for processing in step 1412, followed by a determination being made in step 1414 whether the selected catalog product data comprises MPN|GTIN data. If so, then a products crawler system, such as a web crawler system familiar to those of skill in the art, is accessed and the selected catalog product data is inputted in step 1418. The products crawler then performs a search in step 1420 for the MPN|GTIN associated with the selected product data. It will be appreciated by those of skill in the art that in various embodiments the product crawler may be implemented to crawl webpages, sites, and other data repositories residing on the Internet at-large, private and proprietary data repositories, or both.
A determination is then made in step 1422 whether the product crawler has identified additional product data corresponding to the MPN|GTIN associated with the selected product data. If so, then a determination is made in step 1424 whether only one product category is listed for the MPN|GTIN. If not, then a determination is made in step 1426 whether the product category is listed within the master product catalog. If so, then the product crawler selects the first product category out of a set of listed categories in step 1428. Thereafter, or if it was determined in step 1424 that only one product category was listed, the product crawler selects the first search result. Then, in step 1432, the product crawler captures all required details from product content associated with the link to the first search result. The product crawler then matches the captured MPN|GTIN to the MPN|GTIN returned in the product crawler search in step 1434, followed by making a determination in step 1436 whether the product details between the two MPN|GTIN are similar. If not, or if it was determined in step 1416 that the MPN|GTIN was not available, or in step 1422 that the product crawler did not find a MPN|GTIN, or in step 1426 that a product category was not listed, then the product data is sorted on the basis of merchant category and product brand. Then, in step 1440, a merchant category is selected, followed by selecting the first product brand in the selected merchant category.
A determination is then made in step 1442 whether the product brand in the selected merchant category is “blank,” (e.g., “generic,” not specified, etc.). If so, then a check is performed in step 1444 with the associated product image specifications and product details to ascertain a product brand for each SKU with a “blank” product brand. A determination is then made in step 1446 whether the product brand can be verified. If not, then the SKU category within the SKU categorization file is assigned a value of “uncategorized” and the process is continued, proceeding with step 1440. Otherwise, or if it was determined in step 1442 that the product brand was not “blank,” then for each product brand under the merchant category, a category assigned by an automated process is used as a benchmark and to initialize manual categorization for unmapped products in step 1450. The benchmark category for the product brand is then assigned in step 1452 as the category for SKUs in the SKU categorization file.
However, if it was determined in step 1436 that the product details between the two MPN|GTIN are not similar, then the product crawler sets the first category as the category for the GTIN in the SKU categorization file. Thereafter, or after the benchmark category for the product brand has been assigned in step 1452, then the product data is sorted, based on merchant category, in step 1454. Then, in step 1456, one merchant category at a time is selected, with a final merchant category being assigned within the master catalog, based on the least common category applicable for all SKUs within that merchant and merchant category pair. The product data is then populated in the master catalog, followed by a determination in step 1460 whether to end product categorization operations. If not, then the process is continued, proceeding with step 1412. Otherwise, product categorization operations are ended in step 1462.
FIG. 15 is a generalized flowchart of the performance of product moderation operations in accordance with an embodiment of the invention. In this embodiment, product moderation operations are begun in step 1502, followed by the receipt of a recurring data feed of “product offers” in step 1504. As used herein, “product offers” refer to product data associated with a product being offered for sale, or resale, by a merchant, vendor, manufacturer or other product source. The product offer data feeds are then processed by various systems associated with the product moderation process in step 1506 and an automated product crawler system, such as a web crawler system familiar to those of skill in the art, is run on the URL of a selected product offer in step 1508.
A determination is then made in step 1510 whether the URL associated with the selected product offer is broken. If so, then the product offer is automatically or manually rejected in step 1516 and the process is continued, proceeding with step 1506. Otherwise, a determination is made in step 1514 whether all MPN|GTIN fields in the product over are blank. If so, then the product over is automatically or manually rejected in step 1516 and the process is continued, proceeding with step 1506. Otherwise, in step 1518, the product offer is entered into a work scheduler, a master catalog URL is created, the product offer is assigned to a moderator for auditing, and a moderation page is opened in a separate browser window for the assigned moderator.
The assigned moderator then initiates the audit of an assigned product offer in step 1520, followed by a determination being made in step 1522 whether the title, brand, manufacturer, or MPN|GTIN fields contain profanity. If so, then the product offer is automatically or manually rejected in step 1516 and the process is continued, proceeding with step 1506. Otherwise, a determination is made in step 1524 whether the brand in the product over title is different than the brand referenced within the product offer itself. If so, then the product offer is automatically or manually rejected in step 1516 and the process is continued, proceeding with step 1506. Otherwise, a determination is made in step 1526 whether the manufacturer in the product offer title is different than the brand referenced within the product offer itself. If so, then the product offer is automatically or manually rejected in step 1516 and the process is continued, proceeding with step 1506.
Otherwise, a determination is made in step 1528 whether the product image associated with the product offer passes image checks. As an example, the product image may not pass the image check if it contains pornography, nudity or profanity. As another example, the product image may not pass the image check if shows a product that is different than a product described within the title of the product offer or within the product offer itself. If it is determined in step 1528 that the product offer image does not pass the image checks, then the product offer image is marked as “not passed” in step 1530. Thereafter, or if it was determined in step 1528 that the product offer image passed the image checks, then a determination is made in step 1532 whether the GTIN of the product offer is different than the GTIN of the product itself. If so, then a search is performed in step 1534, using GTIN, MPN, and manufacturer name as search criteria to perform the search in the master catalog.
A determination is then made in step 1536 whether the search yielded an applicable product. If so, then the product data associated with the applicable product is used in step 1538 to replace (i.e., “switch”) the product data associated with the product offer. The process is then continued, proceeding with step 1532. However, if it was determined in step 1538 that the search did not yield an applicable product, then a search is performed in step 1540 using the MPN, GTIN, manufacturer name, and the title of the product offer as search criteria. A determination is then made in step 1542 whether the search yielded an applicable product. If not, the product offer data feed is queried in step 1544 to determine the Global Unique Identifier (GUID) associated with the product offer. The GUID is then used to perform a search of the master product catalog and the process is then continued, proceeding with step 1536.
However, if it is determined in step 1542 that the search yields an applicable product, then a new product is created in the master catalog in step 1548 and populated with the details associated with the product offer. Any information specific to the merchant, vendor or other source of the product offer is then removed from the new product listing in step 1550. A determination is then made in step 152 whether the product image associated with the new product listing is specific to the merchant, vendor or other source of the product offer. If so, then the product image associated with the product offer is marked as “unavailable” in step 1554. Thereafter, or if the product image associated with the new product listing is not specific to the merchant, vendor or other source of the product offer, the process is continued, proceeding with step 1532.
However, if it is determined in step 1532 that the GTIN of the product offer is not different from the GTIN of the product itself, then a determination is made in step 1556 whether the product GTIN contains profanity. If so, then the process is continued, proceeding with step 1534. Otherwise, a determination is made in step 1558 whether the MPN, manufacturer name, or product brand in the product offer is the same as the product itself. If not, then a determination is made in step 1560 whether any related product offers are mapped to the product itself. If so, then a determination is made in step 1562 whether the MPN, manufacturer name, or product brand in the product offer contains profanity. If so, then the process is continued, proceeding with step 1534. Otherwise, an edit function is implemented in step 1564 to manually or automatically delete the profanity from MPN, manufacturer name, or product brand in the product offer and the process is continued, proceeding with step 1534. However, if it is determined in step 1560 that no other product offers are mapped to the product itself, then the product detail is manually or automatically edited in step 1566 to have the same MPN, manufacturer name, or product brand as the other product offer.
Thereafter, or if it is determined in step 1558 that the MPN, manufacturer name, or product brand in the product offer is the same as the product itself, a determination is made in step 1568 whether the manufacturer name or product brand contains profanity. If so, then the product offer is either manually or automatically edited in step 1570 to have the same product brand and manufacturer name as in the related product offer or any identified profanity is deleted. Thereafter, or if is determined in step 1568 that there is no profanity in the manufacturer name or product brand, then a determination is made in step 1572 whether the product image associated with the product offer is marked “not passed.” If so, then an “unavailable image” is selected in step 1574 as the product image. Otherwise, a determination is made in step 1576 whether the product image passes image checks. If not, then a product offer image is selected in step 1578 as the product image in the master catalog, or alternatively, an “unavailable image” is selected if the product offer image has merchant-related text. Otherwise, or once the product offer images have respectively selected in steps 1574 or 1578, the product offer is approved in step 1580. A determination is then made in step 1582 whether to end product moderation operations. If not, then the process is continued, proceeding with step 1506. Otherwise, product moderation operations are ended in step 1584.
FIG. 16 is a generalized flowchart of the performance of campaign management operations as implemented in accordance with an embodiment of the invention. In various embodiments, online store owners are provided the opportunity to increase sales by creating, scheduling and executing marketing campaigns that drive traffic to online stores, blogs and widgets. In this embodiment, campaign management operations are begun in step 1602, followed by the selection of a campaign type in step 1606. In various embodiments the selection of campaign types may include:

- email marketing, which includes sending personalized emails to a list of contacts that drive traffic to a predetermined URL (e.g., a landing page)
- search marketing, which includes purchasing and managing search terms that drive traffic to a predetermined URL (e.g., a landing page)
- display ads, which includes creating and publishing display ads to ad networks to drive traffic to a predetermined landing page
- social media marketing, which includes creating and publishing predetermined content to a target social media destination (e.g., tweet, blog, event, etc.) or to a predetermined user to drive traffic to a predetermined URL
- integrated marketing, which includes a combination of the above campaign types working together to drive interactions

In these and other embodiments, the selected campaign type is then used to automatically recommend templates, content, schedules, touch points, etc. to improve the effectiveness of the marketing campaign. In certain of these embodiments, the campaigns may be targeted, or executed, within one or more social network or syndicated commerce environments.
In step 1606, the campaign is named and details of the campaign are defined, such as the interactions (e.g., touch points) and offers that will be presented to targeted members of one or more segments. In various embodiments, offers may be predetermined products or specific calls to action, such as “generate a post recommending the online store to two friends in your social network and receive 20% off your next purchase.” It will be appreciated by those of skill in the art that an offer does not necessarily have to be ‘sent’ to an individual. For instance, an offer can be dynamically presented to visitors within existing sites via widgets and billboards.
In various embodiments, multiple offers are defined for the campaign. In these and other embodiments, offers may be explicitly assigned to predetermined user segments or to steps in the campaign interactions. In certain of these embodiments, the offers may be automatically assigned by using a predictive algorithm that matches offers to users to improve conversion rates. In these and other embodiments, the social commerce marketplace system determines the optimal product offer to present to a target member of a segment based upon recommendations generated by an SEO algorithm. Such product offers may include the combination of:

- product
- price
- discount
- time limit of the offer
- supporting content (e.g., ratings, reviews, videos, specifications, etc.)
- presentation UI

The creative content (e.g., email content, ‘tweet’ text, etc.) that is associated with an offer is then defined in step 1608. In various embodiments, the definition may include the creation of the content within the social commerce marketplace system through the use of a “what you see is what you get” (WYSIWYG) editor. In these and other embodiments, externally-created content may be ingested and included within the offer as well as internally-created, previously-used, and dynamically-created and recommended content. In certain of these embodiments, user are provided the option of executing workflows that route the creative content to other user to edit or approve before it is published and used within a campaign. Then, in step 1610, goals and key performance indicators are established to determine whether the campaign is reaching its objectives. In various embodiments, reaching such goals may include attaining predetermined:

- email click-through rates
- landing page conversion rates (clicking on/through specific buttons and links)
- traffic to a landing page/destination site
- mentions or ‘re-tweets’
- purchase levels
- average order values
- revenues
- registrations
- coupon downloads
- foot traffic to off-line locations

In these and other embodiments, the goals may be associated with a predetermined campaign step, such as an interaction point, or at the overall campaign level. In step 1612, various options are used for defining the target audience for the offer, including:

- uploading lists of contacts
- creating a contact list using API calls from external systems such as a customer relationship management system (CRM)
- selecting users captured from sources such as previous visitors sessions, third party listening platforms, social graph data, and users capture by a web crawler
- using system-recommended contacts or target individuals

Then, in step 1614, delivery endpoints are defined. As used herein a delivery endpoint refers to the user interface, or environment, through which a customer, or prospective customer, receives an offer associated with a campaign. In various embodiments, such delivery endpoints include:

- email
- Twitter tweets
- Facebook “wall posts,” “friend requests,” “applications,” etc.
- landing pages
- widgets
- friend requests
- blogs and associated posts

In these and other embodiments, the delivery endpoints may be managed or controlled by the social commerce marketplace system (e.g., send an email, etc.) or may be executed by one or more external systems. In certain of these embodiments, a user defines the delivery end points for each target in a list of contacts for the campaign, or alternatively, initiates the use a predictive algorithm to select the best end point and offer combination for each user to increase conversion rates.
A landing page familiar to those of skill in the art is then generated in step 1616. In various embodiments, the social commerce marketplace system provides users the ability to:

- specify an existing internal page (e.g., a product details page, blog post, etc.)
- create a new internal landing page, whether built “from scratch” or from predefined templates comprising page designs and layouts
- specify an external page

In various embodiments, pages are automatically instrumented to track campaign-driven visitor traffic and click stream activities. In these and other embodiments, the ability is provided to create vanity URLs for each landing page so they can be used as the hyperlinks within offers and interactions that drive the user back to a landing page. In certain embodiments, JavaScript tags are generated and then placed within an external site to track visitor activities. In yet other embodiments, vanity URLs are created that redirect users to an external target page.
Interactions (e.g., offers and endpoint combinations), and the frequency of follow up activities are then scheduled in step 1918. In various embodiments, the social commerce marketplace system automatically executes and publishes the interactions once the specified dates and times have passed. In these and other embodiments, the system uses a predictive algorithm to build a recommended schedule of interactions based on the attributes and characteristics of the contacts described in greater detail herein.
Then, in step 1620, the campaign is begun. In various embodiments, the start and end dates for each campaign are defined. In these and other embodiments, the campaigns are configured to either automatically or manually begin executing (e.g., presenting offers and interactions) on the start date. The results of the campaign are then measured in step 1622. In one embodiment, the results are presented within the UI of a performance dashboard familiar to skilled practitioners of the art. In this embodiment, the results include analytic reports, which include key performance indicators indicating how well defined goats are being achieved. Modifications to the campaign are then performed in step 1624. In one embodiment, the modifications are performed automatically according to the results of a predictive algorithm. A determination is then made in step 1626 whether to end campaign management operations. If not, then the process is continued, proceeding with step 1604. Otherwise, campaign management operations are ended in step 1628.
FIG. 17 is a generalized flowchart of the performance of multiple endpoint publication operations as implemented in accordance with an embodiment of the invention. In various embodiments, when a store catalog is created from one or more merchant catalogs by a user, they have the ability to publish the catalog and custom content within multiple distributed end points, such as various user interfaces on the web described in greater detail herein. In these and other embodiments, any changes (e.g., add, change, delete, etc.) to the store catalog or store content are automatically reflected across multiple locations on the internet.
In this embodiment, multiple endpoint publishing operations are begun in step 1702, followed by the creation of an online store or online store content in step 1704. In various embodiments, a user can create multiple stores, which in turn comprise their own store catalog, or additional content associated with an online store. In these and other embodiments, the social commerce marketplace system maintains a central reference of the store, and its associated content, so it can be reused across multiple publishing end points. Then, in step 1706, the user (e.g., the online store owner) selects one or more end points to publish the storefront, which is a UI representation of a store catalog, and may include:

- a storefront within the social commerce marketplace system
- online store within a social network environment
- a widget embedded in third party site
- a storefront embedded within another merchant's website
- a mobile device application
- HTML-email-embedded store
- an API or catalog feed
- a kiosk
- a personal video recorder (PVR)-embedded store

Endpoints are then automatically updated or published in step 1708. In one embodiment, all endpoints are automatically updated. In another embodiment only predetermined endpoints are updated. It will be appreciated that the ability to selectively choose which content to present on each end point is advantageous to the user (e.g., an online store owner). In various embodiments, the social commerce marketplace system provides users the ability to manually publish or push changes to each end point, or alternatively, configure the system to automatically publish the updates. In view of the foregoing, those of skill in the art will recognize that multiple versions of the content can be published to an end point. In various embodiments, the user configures the default version to display and the social commerce marketplace system selects the appropriate alternative version to display based on the user's intent and context. For example, a single store can have two notions of the catalog; one for men and one for women. If the visitor is identified as a female, the system obfuscates the products specific to the male gender and only presents products made thr the female gender.
Once a publishing event has been completed, the store or content changes are updated in step 1710 and made available to visitors within each respective end point. A determination is then made in step 1712 whether to end multiple endpoint publishing operations. If not, then the process is continued, proceeding with step 1704. Otherwise, endpoint publishing operations are ended in step 1714.
FIG. 18 is a generalized flowchart of the performance of landing page optimization operations as implemented in accordance with an embodiment of the invention. As used herein, a landing page refers to a predetermined page (e.g., a URL) that is presented to campaign contacts after they accept a call to action within a campaign offer (e.g., a button or link in an email, a link within a ‘tweet,’ etc.). Skilled practitioners of the art will be aware that one aspect of increasing the success, or conversion rates, of a campaign is to present page layouts and content that have be statistically proven to increase conversions to visitors of the landing page. In various embodiments, online store owners are provided the ability to create multiple variants of their content and associated landing pages. In these and other embodiments, users are afforded the additional ability to test various landing pages to determine which versions result in the highest visitor conversion rates. In certain of these embodiments, landing page optimization is provided to affiliates and multi-level channels. In various embodiments, social graph data is likewise used to segment users and to predict which versions of the landing page will provide higher conversion rates.
In this embodiment, landing page optimization operations are begun in step 1802, followed by the generation of a landing page in step 1804. In various embodiments, a product details page is automatically created when a user (e.g., an owner of an online store) adds an associate product to the online store from the store's catalog. Skilled practitioners of the art will be aware that such product detail pages are often used as landing pages for marketing campaigns. Accordingly, these same skilled practitioners will recognize that this same approach can be utilized to create and test other content such as emails, widget content, or calls to action. For example, in one embodiment, an existing page (e.g., a product details page, a blog post, etc.) is automatically specified as the landing page. In another embodiment, a new landing page is generated through the use of a WYSIWYG editor. In this and other embodiments, the user has the ability to build a page from scratch or select page designs and layouts from predefined templates.
In various embodiments, the landing page is automatically instrumented to allow users (e.g., the online store owner) to generate variants of the page as well as include them in A|B or multivariate tests (MVT). Accordingly, the type of test is defined in step 1806. In one embodiment, the test is an A|B test, which refers to comparing conversion rates and other success metrics between an existing page or content (i.e., version ‘A’), and an alternate version (i.e., version ‘B’). As an example, version ‘B’ may be used for all visitors or there may be multiple versions of test ‘B’ for one or more segments of visitors. In another embodiment, the test is a MVT, which refers to simultaneously comparing multiple combinations of elements (e.g., header, image, buttons, colors, etc.), and versions of elements, to determine which combination of elements leads to higher conversion rates for specific segments of users.
A determination is then made in step 1808 whether the test is an ‘A|B’ test or a ‘MVT.’ If it is determined in step 1808 that the test is an ‘A|B’ test, then the user generates one or more alternative versions of the webpage or content in step 1810. In one embodiment, the user (e.g., the online store owner) is provided the ability to create an alternative version by selecting an existing template or version. In another embodiment, the user is provided the ability to upload a new template or version to test. In yet another embodiment, the user is provided the ability to create a new template or version using a WYSIWYG editor within the system. In this embodiment, the user may have the ability to build a new version from scratch, load an existing page or content and select an element to change, or load an existing page or content and rearrange the existing elements to create a new version. Then, in step 1812, the user can select the template or version an associate it with the ‘A|B’ test. In various embodiments, the user may select more than one alternate version and test each of them for predetermined segments of users for more complex tests. It will be appreciated that while each alternate version used is actually a separate ‘A|B’ test, it will appear to the user that they are conducting a single test. In these and other embodiments, the segmentation of traffic is managed to ensure a valid test is completed for each version.
However, if it is determined in step 1808 that the test is a ‘MVT’ test, then one or more elements are created or selected in step 1814 to test with at least two versions of each page or content. For example, a page header image and a button label may be selected, or multiple versions of a single element (e.g., two or more versions of a header image). In various embodiments, users are provided the ability to create multiple alternative versions of elements and to select multiple elements to test, such as:

- selecting more than one existing template or version
- uploading new versions of elements or templates to test
- creating new templates or element versions using a WYSIWYG editor
- loading the page, selecting the elements (e.g., regions of the page, etc.), and then defining alternate versions of each element by creating new versions or selecting from existing versions

Once the respective selection operations are completed in steps 1812 or 1814, the amount of traffic sent to the incumbent version of the page, or content, and the amount sent to a challenger version is defined in step 1816. Thereafter, a statistical confidence interval is determined in step 1818 to predict an optimum selection of the set of versions being tested. In one embodiment, visitor's social graph data is used as decision inputs to segment users and to predict an optimum version. As an example, visitors with social scores greater than ‘30’ are more likely to convert using version ‘X.’
Then, in step 1820, the test and the alternate versions of the test are published, followed by a winning version being predicted in step 1822 once a predetermined confidence interval value defined by the user (e.g., the online store owner) is reached. In various embodiments, depending upon the user segments identified and tested, there may be multiple optimum versions. In certain of these embodiments, it may be determined that each segment requires a different version.
Users (e.g., online store owners) then have the option to manually publish the optimum version(s) or configure the social commerce marketplace system to automatically replace the incumbent version with the optimum version in step 1824. In various embodiments, multiple versions may be published so that they can be dynamically rendered for a specific visitor segment. As an example, the system may have determined that female users with a social score greater than ‘30’ have higher conversion rates when they are presented a header image of a ‘ball’ and a button label of “Save Now,” whereas male users with a social score greater than ‘30’ have higher conversion rates when presented a header image of a ‘bat’ and a button label of “Get it Now.” A determination is then made in step 1826 whether to end landing page optimization operations. If not, then the process is continued, proceeding with 1804. Otherwise, landing page optimization operations are ended in step 1828.
FIG. 19 is a generalized flowchart of the performance of web content targeting operations as implemented in accordance with an embodiment of the invention. Skilled practitioners of the art will be familiar with web experience optimization, which is the process of matching a visitor to predetermined content that will increase their likelihood to convert. A conversion may have multiple meanings and definitions across organizations based upon their business model and active marketing initiatives, including:

- purchasing a product
- navigating to a target webpage
- downloading a white paper
- clicking on a button within a webpage or HTML email
- clicking through a link within a webpage or HTML email
- preforming a “re-tweet”
- “liking” an “item”
- posting an item to their “wall”
- providing their contact information
- providing authentication privileges to their social account and graph

Ideally, an organization will dynamically generate webpages or content that contextually matches the need or intent of a visitor when they request a predetermined URL. It will be appreciated that being responsive to a visitor's needs or intents increases the likelihood that the visitor will complete a desired conversion event. It will likewise be appreciated that many factors may influence a visitor's likelihood to convert, including:

- the user's intent or need
- page layouts proven to increase conversions for other visitors with the same intent or need
- content proven to increase conversions for other visitors with the same intent or need

In various embodiments, each visitor is automatically evaluated, characteristics (e.g., behavior and historical data) analyzed, and prediction operations performed to determine which content is most likely to induce a conversion event. In these and other embodiments, various process flows are self-optimizing and self-tuning, with the result that the content rendered to each user is continually adjusted to improve conversion rates. When a visitor requests a page, a process is initiated to analyze the visitor's behavior and historical data to select the best version of the webpage layout, content, etc.) that will increase the likelihood of the visitor completing one or more conversion events. In various embodiments, various characteristics of the visitor are analyzed to match them with a user segment that has the same or similar characteristics. The segment information is then used to determine which version of the page to render for the visitor. In one embodiment, a predictive algorithm is used to determine which layouts and content to present by comparing the visitor to prior visitors with similar characteristics. In these various embodiments, the similar characteristics may include:

- historical visit behaviors (e.g., frequency of visits, click path, time on content, search activity, etc.)
- current session visit behaviors (e.g., prior click action, referring context, click path, time on content, search activity, etc.)
- social graph data (e.g., relationships, interests, relevancy to current website, social score, etc.)
- demographics
- geo-location
- prior conversion history
- prior purchase history

Then, in step 1908, a page layout is selected for the visitor that has been statistically proven to increase conversion actions when presented to visitors with the same or similar characteristics. Once the page layout is selected, it is populated in step 1910 with content that has likewise been statistically proven to increase conversion actions when presented to users with the same or similar characteristics. The combined layout and content is then rendered in step 1912 and the visitor's behaviors within the page, as well as their next set of click actions, is monitored and measured to determine whether or not a conversion event action was completed.
The resulting user data and associated outcomes are then used in step 1914 to adjust the algorithm for the next visitor, or set of visitors, with the same or similar characteristics. In various embodiments, layout and content combinations that result in higher frequency conversion events are prioritized and those combinations with lower conversion rates are deprioritized. A determination is then made in step 1918 whether to end web content optimization operations. If not, the process is continued, proceeding with step 1904. Otherwise, web content optimization operations are ended in step 1920.
FIG. 20 is a generalized flowchart of the performance of recommended layout operations as implemented in accordance with an embodiment of the invention. Those of skill in the art will be aware that when an organization creates a new web presence within a specific end point (e.g., asocial site, community site, third party site, etc.), or creates content for a new market segment, they may not have the experience or information available to determine what types of layouts and content will best resonate with their customer or visitor population. The absence of this information may either increase the risk of initial failure to reach their organization goals or result in a protracted amount of time to test and optimize designs that hone in on the interest of the population.
In various embodiments, these issues are addressed by recommending a layout that increases the likelihood of improving visitor engagement (e.g., time on-site, number of pages visited, frequency of visits, etc.) and conversion rates. In this embodiment, recommended layout operations are begun in step 2002, followed by the creation of a new online store, webpage or content (e.g., campaign offer, product description, etc.) in step 2004. Then, in step 2006, the online store's published catalog, the number and location of defined publishing end-points, and user-generated content (e.g., blogs, product descriptions, reviews, images, videos, etc.) are analyzed between the online store and other online stores to create a list of online stores with similar characteristics.
The resulting list of similar stores is then scored according to a variety of factors (e.g., traffic quality, visitor engagement, conversion rates, etc.) to recommend optimum layouts in step 2008. One of the recommended layouts is then selected in step 2010, followed by the publishing of the selected layout in step 2012. In various embodiments, the online store's performance (e.g., traffic quality, visitor engagement, conversion rates, etc.) is measured over time to automatically adjust the algorithm for the next recommendation with similar characteristics. A determination is then made in step 2014 whether to end recommended layout operations. If so, then the process is continued, proceeding with step 2004. Otherwise, recommended layout operations are ended in 2016.
FIG. 21 is a generalized flowchart of the performance of product-to-merchandise operations as implemented in accordance with an embodiment of the invention. Skilled practitioners of the art will recognize that the timely identification of market trends allows organizations to maximize profits by being either an early entrant into an emerging opportunity or an early defector of a market that is about to decline. In various embodiments, market trends for individual products within an online store are identified as they emerge. In this embodiment, product-to-merchandise operations are begun in step 2102, followed by the creation of a list of products in step 2104 that will be analyzed to determine whether there are market, trends that should capitalized upon. In various embodiments, users (e.g., online store owners) have the ability to either select products from the master or online store catalog or select keyword phrases that represent the product's name, description, characteristics or functionality. In these and other embodiments, the Internet in general and social networking sites in particular, are crawled in step 2106 to continually discover and collect information related to the products in the list by:

- searching or crawling various search engine indexes
- searching or crawling comparison shopping sites
- searching or crawling blog sites
- search ‘tweets’ for mentions
- pulling visitors social graphs data
- search crawling social network sites
- purchasing sales data from third party service providers

The collected information is then analyzed on step 2108 to generate market trend data, which in various embodiments includes:

- market interest
- sale volumes
- demand volumes
- price direction
- competitiveness

The resulting market trend data is then scored in step 2110 for each product within the list. In one embodiment, the scoring is performed by a predictive algorithm to determine the direction and magnitude of the market trends for each product. In this and other embodiments, the list of products is ranked by score to create a priority of opportunity. In turn, associated analytics and predictive data are presented for each product in step 2112 to illustrate the relative opportunity between the various products in the list. In various embodiments, the resulting scores are used to recommend products to merchandize that have a higher relative opportunity, and to recommend the removal of products that have weakening or low relative opportunity. In these and other embodiments, products may be recommended by end point, geography or region, and user segment such that targeted campaigns can be defined to capitalize on each identified opportunity. A determination is then made in step 2114 whether to end product-to-merchandise operations. If not, then the process is continued, proceeding with step 2104. Otherwise, product-to-merchandise operations are ended in step 2116.
FIG. 22 is a generalized flowchart of the performance of social shopping operations as implemented in accordance with an embodiment of the invention. Those of skill in the art will be aware that various influences on purchasing behavior include peer feedback and pressure to like or dislike a brand or product. While some shoppers may seek products that provide them with the sense of uniqueness and individuality, others want products that provide them with a sense of inclusion and similarity within a peer group. In various embodiments, users are provided the ability to make purchasing decisions based on feedback and influence from their social circles and social peer group. For example, a teenage girl buying a prom dress may want to know if anyone within her school is considering to purchase, or has already purchased, a predetermined dress to ensure that she can select a unique dress.
In this embodiment, social shopping operations are begun in step 2202, followed by the instrumentation of a webpage in step 2204. In various embodiments, tags (e.g., JavaScript tags) are automatically inserted in the webpage to identify visitors, dynamically interact with users, and to present predetermined social shopping features based upon their intent and need. In these and other embodiments, the webpages are instrumented such that they can be embedded into external commerce sites to likewise enable them with social shopping capabilities.
In step 2206, a social-shopping-enabled webpage is accessed by a visitor and social context information associated with the visitor is retrieved, including:

- identity for specific social sites (e.g., Facebook ID and username)
- social site authorization token, or other authorization permissions, for each discovered social site
- previous purchase history
- previous visit history

Once the visitor's identity and authorization permissions have been obtained, their social graph is obtained in step 2208 and then analyzed in step 2210. In various embodiments, the resulting analysis of the visitor's social graph information is used to identify relationships within their social network and relevant relationships to people within the social graphs of other visitors. In these and other embodiments, the relationships may include those that have:

- purchased a product within the online store's catalog
- positively or negatively reviewed, commented or mentioned the product within the online store's catalog
- expressed a desire to own a product within the online store's catalog

In one embodiment, the visitor's influence on other visitors is scored and rated. Then, in step 2212, social shopping user controls are rendered for the visitor. In various embodiments, the visitor is allowed to choose which social shopping features they wish to use. In another embodiment, predetermined social shopping control are dynamically rendered that will result in higher conversion rates. In yet another embodiment, a control that contains a list of relationships is sorted and then prioritized by social score and influence. In these and other embodiments, shopping controls include:

- “Friends Who Have Purchased,” which lists individuals or relationships within the visitor's social network or groups such as schools, associations, employer, etc. that have purchased the product or offer presented on the webpage
- “Social Footprints.” which lists individuals or relationships within the visitor's social network or groups that have viewed the webpage, and the associated date and time of each visit
- “Social Search,” which lists individuals or relationships within the visitor's social network or groups that have used the same referring search term that lead to the visitor's visit to the webpage.
- “Friend's Opinion,” which lists individuals or relationships that have expressed an opinion about the product, including ratings, reviews, comments, “wall posts,” ‘tweets,’ etc. in addition to listing what the individual stated, their sentiment, and tone
- “Ask a Friend to Recommend,” which provides the ability for the visitor to ask individuals within their social network to vote or make a purchase recommendation regarding whether they should purchase the product or not as well as recommending other or similar products in the store catalog for consideration
- “Invite a Friend to Shop,” which provides the ability for the visitor to obtain the presence awareness of their friends, whether or not they are currently on-line, and the ability to invite them into their shopping experience, which may include:
  - a shared session
  - a shared session plus a video chat (including mobile chat)
  - a shared session plus a text chat (including mobile chat)
- “Invite Friends to Purchase,” which provides the ability to send or post offers to friends, including the ability to unlock additional offers or discounts

A determination is then made in step 2114 whether to end product-to-merchandise operations. If not, then the process is continued, proceeding with step 2104. Otherwise, product-to-merchandise operations are ended in step 2116.
FIG. 23 is a generalized flowchart of the performance of user-generated content (UCG) targeting operations as implemented in accordance with an embodiment of the invention. Skilled practitioners of the art will be aware that user purchase decisions are often influenced by the opinion of others. Common venues for such opinions are product ratings and reviews. However, the context, tone and length of a rating or review may not affect all user purchase decisions the same way. Accordingly, it will be appreciated that product purchases or other types of conversions may be optimized by providing ratings and reviews that are targeted to the needs or intent of each visitor.
In this embodiment, UCG targeting operations are begun in step 2302, followed by a visitor requesting a predetermined webpage in step 2304. In various embodiments, a visitor's past behaviors and historical data is analyzed in step 2306 and the optimum subset of a target product's rating is selected to increase the likelihood of the visitor purchasing the product. In various embodiments, multiple characteristics about the visitor are analyzed in order to match them with a segment of users that has similar characteristics, which may include:

- historical visit behaviors (e.g., frequency of visits, click path, time on content, search activity, etc)
- current session visit behaviors (e.g., prior click action, referring context, click path, time on content, search activity, etc.)
- social graph data (e.g., relationships, interests, relevancy to current website, social score, etc.)
- demographics
- geo-location
- prior conversion history
- prior purchase history

The resulting segment information is then used to determine which version of the page to render for the visitor. In various embodiments, a predictive algorithm is used to compare the visitor to prior visitors with similar characteristics that resulted in conversion events in order to determine which product ratings and reviews were present and viewed.
Then, in step 2308, a list of ratings and reviews is selected, sorted, prioritized and rendered to the visitor, followed by the monitoring and measurement of the visitor's actions, behaviors within the webpage, and their next set of click actions in step 2310 to determine whether or not a conversion event action was completed. The algorithm is then adjusted in step 2312 for the next visitor, or set of visitors, with the same or similar characteristics. In various embodiments, combinations of reviews and presentation order that have a higher conversions rate frequency are prioritized, while those that have lower conversion rate frequency are deprioritized. A determination is then made in step 2314 whether to end UCG targeting operations. If not, then the process is continued, proceeding with step 2304. Otherwise, UCG targeting operations are ended in step 2316.
The present invention is well adapted to attain the advantages mentioned as well as others inherent therein. While the present invention has been depicted, described, and is defined by reference to particular embodiments of the invention, such references do not imply a limitation on the invention, and no such limitation is to be inferred. The invention is capable of considerable modification, alteration, and equivalents in form and function, as will occur to those ordinarily skilled in the pertinent arts. The depicted and described embodiments are examples only, and are not exhaustive of the scope of the invention.
For example, the above-discussed embodiments include software modules that perform certain tasks. The software modules discussed herein may include script, batch, or other executable files. The software modules may be stored on a machine-readable or computer-readable storage medium such as a disk drive. Storage devices used for storing software modules in accordance with an embodiment of the invention may be magnetic floppy disks, hard disks, or optical discs such as CD-ROMs or CD-Rs, for example. A storage device used for storing firmware or hardware modules in accordance with an embodiment of the invention may also include a semiconductor-based memory, which may be permanently, removably or remotely coupled to a microprocessor/memory system. Thus, the modules may be stored within a computer system memory to configure the computer system to perform the functions of the module. Other new and various types of computer-readable storage media may be used to store the modules discussed herein. Additionally, those skilled in the art will recognize that the separation of functionality into modules is for illustrative purposes. Alternative embodiments may merge the functionality of multiple modules into a single module or may impose an alternate decomposition of functionality of modules. For example, a software module for calling sub-modules may be decomposed so that each sub-module performs its function and passes control directly to another sub-module.
Consequently, the invention is intended to be limited only by the spirit and scope of the appended claims, giving full cognizance to equivalents in all respects.

Claims

1. A computer-implementable method for managing syndicated commerce campaigns, comprising:

processing social data associated with a first user to generate market segment data;

processing the market segment data to identify a set of second users corresponding to a set of campaign data associated with a campaign goal; and

providing the set of campaign data to the first user.

2. The computer-implementable method of claim 1, wherein the market segment data comprises at least one of the set of:

users of a syndicated commerce environment;

a list of users provided by a third party;

a list of users generated from Application Programming Interface (API) calls from and external source;

previous visitors to an online site;

a third party listening platform;

social graph data; and

users captured by a web crawler.

3. The computer-implementable method of claim 1, wherein the campaign data comprises at least one of the set of:

a personalized electronic mail (email) message comprising a predetermined Uniform Resource Locator (URL), the personalized email message provided to a list of target contacts;

a search term provided to a search engine, the use of the search term resulting in the provision of a predetermined URL;

a display ad comprising a predetermined URL, the display ad provided to an ad network;

content data comprising a predetermined URL, the content data provided to a predetermined social media site; and

content data comprising a predetermined URL, the content data provided to a predetermined user.

4. The computer-implementable method of claim 1, wherein the set of campaign data is provided to an endpoint, the endpoint comprising at least one of the set of:

an online store;

a widget embedded in an online site;

an online store embedded in a third party site;

a widget embedded in a third party site;

a store embedded in a personal video recorder (PVR);

a store embedded in a hypertext mark-up language (HTML) electronic mail (email) message;

an Application Programming Interface (API);

an application executing in a mobile device; and

an application executing in a kiosk.

5. The computer-implementable method of claim 1, wherein the attainment of the campaign goal comprises at least one of the set of attaining predetermined:

electronic mail (email) click-through rates;

landing page conversion rates;

traffic to a predetermined site;

social medial actions performed by the user;

purchase levels;

average order values;

revenues;

registrations;

coupon downloads; and

foot traffic to off-line locations.

6. The computer-implementable method of claim 1, wherein the campaign data is provided in a predetermined format generated by at least one of the set of:

selecting an existing campaign data template;

creating a new campaign data template; and

loading a webpage, selecting the elements of the webpage, and then defining alternate versions of each element to create a new version of the webpage.

7. A system comprising:

a processor;

a data bus coupled to the processor; and

a computer-usable medium embodying computer program code, the computer-usable medium being coupled to the data bus, the computer program code interacting with a plurality of computer operations and comprising instructions executable by the processor and configured for:

providing the set of campaign data to the first user.

8. The system of claim 7, wherein the market segment data comprises at least one of the set of:

users of a syndicated commerce environment;

a list of users provided by a third party;

previous visitors to an online site;

a third party listening platform;

social graph data; and

users captured by a web crawler.

9. The system of claim 7, wherein the campaign data comprises at least one of the set of:

10. The system of claim 9, wherein the set of campaign data is provided to an endpoint, the endpoint comprising at least one of the set of:

an online store;

a widget embedded in an online site;

an online store embedded in a third party site;

a widget embedded in a third party site;

a store embedded in a personal video recorder (PVR)

an Application Programming Interface (API);

an application executing in a mobile device; and

an application executing in a kiosk.

11. The system of claim 7, wherein the attainment of the campaign goal comprises at least one of the set of attaining predetermined:

electronic mail (email) click-through rates;

landing page conversion rates;

traffic to a predetermined site;

social medial actions performed by the user;

purchase levels;

average order values;

revenues;

registrations;

coupon downloads; and

foot traffic to off-line locations.

12. The system of claim 7, wherein the campaign data is provided in a predetermined format generated by at least one of the set of:

selecting an existing campaign data template;

creating a new campaign data template; and

13. A computer-usable medium embodying computer program code, the computer program code comprising computer executable instructions configured for:

providing the set of campaign data to the first user.

14. The computer usable medium of claim 13, wherein the market segment data comprises at least one of the set of:

users of a syndicated commerce environment;

a list of users provided by a third party;

previous visitors to an online site;

a third party listening platform;

social graph data; and

users captured by a web crawler.

15. The computer usable medium of claim 13, wherein the campaign data comprises at least one of the set of:

16. The computer usable medium of claim 15, wherein the set of campaign data is provided to an endpoint, the endpoint comprising at least one of the set of:

an online store;

a widget embedded in an online site;

an online store embedded in a third party site;

a widget embedded in a third party site;

a store embedded in a personal video recorder (PVR);

an Application Programming Interface (API);

an application executing in a mobile device; and

an application executing in a kiosk.

17. The computer usable medium of claim 13, wherein the attainment ref the campaign goal comprises at least one of the set of attaining predetermined:

electronic mail (email) click-through rates;

landing page conversion rates;

traffic to a predetermined site;

social medial actions performed by the user;

purchase levels;

average order values;

revenues;

registrations;

coupon downloads; and

foot traffic to off-line locations.

18. The computer usable medium of claim 13, wherein the campaign data is provided in a predetermined format generated by at least one of the set of:

selecting an existing campaign data template;

creating a new campaign data template; and

19. The computer usable medium of claim 13, wherein the computer executable instructions are deployable to a client computer from a server at a remote location.

20. The computer usable medium of claim 13, wherein the computer executable instructions are provided by a service provider to a customer on an on-demand basis.