Search Engine Optimization for Social Marketplace
A method and system are disclosed for optimizing search engine operations to increase the likelihood of attaining financial goals in a syndicated commerce environment. An SEO algorithm is implemented to determine keyword options for a predetermined product based upon the product's description, its web page content, and other related information. The SEO algorithm is then used to determine the product's associated search traffic and rank-per-keyword from various search engines. This information, in addition to sales conversion rate information, is then used to estimate the likelihood of monetization for one or more keyword. The product web page content is then automatically revised with an optimized combination of keywords. Once optimized, various search engines are automatically notified of the revisions to the web content pages to improve organic search rankings.
This application claims the benefit under 35 U.S.C. §119(e) of U.S. Provisional Application No. 61/485,767, 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 INVENTION1. 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 search engine operations to increase the likelihood of attaining financial goals 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.”
Another aspect of social marketing that is gaining popularity is syndicated commerce, where a scaled-down version of a merchant's online storefront is embedded in an affiliate's web page or social media site. Such syndicated commerce sites provide the opportunity to increase sales, a portion of which is typically provided to the affiliate. However, these embedded storefronts often fail to attain their financial goals due to insufficient traffic and low sales conversion rates. It is not uncommon for such failure to be attributed to either a lack, misapplication, or misunderstanding of various search engine optimization (SEO) approaches. As an example, one or more keywords used in an SEO approach may not prove effective in driving traffic due to low search engine ranking. As another example, the keywords may be associated with content generated by a social network user whose social graph is insufficient to drive significant traffic. Conversely, the keywords may be selected from non-authoritative sites, resulting in low conversion rates from sufficient, yet inappropriate traffic.
SUMMARY OF THE INVENTIONA method and system are disclosed for optimizing search engine operations to increase the likelihood of attaining financial goals in a syndicated commerce environment. In various embodiments, a SEO algorithm is implemented in a syndicated commerce environment to predict the amount of financial compensation an individual or social commerce marketplace entity can receive from the sale of a predetermined product. In certain embodiments, the SEO algorithm is further implemented to optimize their associated web pages to increase site traffic, and as a result, the likelihood of reaching their financial goals.
In these and other embodiments, the SEO algorithm determines keyword options for a predetermined product based upon the product's description, its web page content, and other related information. The social commerce marketplace system then uses the SEO algorithm to determine the product's associated search traffic and rank-per-keyword from various search engines. This information, in addition to sales conversion rate information, is used to estimate the likelihood of monetization for a single keyword or a group of keywords. In certain embodiments, the SEO algorithm refines its estimates by tracking and analyzing historical purchase records for a given path and visitor segment. The system then automatically modifies the website pages with optimal combinations of keywords. Once optimized, various search engines are automatically notified of the changes to the web pages to improve organic search rankings.
In various embodiments, the SEO algorithm is implemented to determine the likelihood of a relationship or visitor associated with the user's social graph to purchase a predetermined product. Once the likelihood is determined, the social commerce marketplace system creates tasks for the user, monitors the progress of their completion, and makes ongoing recommendations to assist the user in reaching their revenue goals. In one embodiment, a crawler sub-module is implemented with the SEO algorithm to crawl a predetermined domain or website to analyze the market opportunity or financial value of the site. In this embodiment, the output of the analysis is a list of recommendations and tasks to complete to capitalize on each opportunity.
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.
A method and system are disclosed for optimizing search engine operations to increase the likelihood of attaining financial goals 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 any 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.
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 a social 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
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
In one embodiment, the social media store 401 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 402 management module is used to manage a social commerce storefront that is associated with an affiliate's web site or online blog. In yet another embodiment, the blog 403 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 404 management module is used for the automated configuration of social commerce storefront pages. In one embodiment, the notifications 410 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 411 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 405 management module further comprises articles 406, podcast 407, pictures 408, and video 409 management sub-modules. In this and other embodiments, the articles 406, podcast 407, pictures 408, and video 409 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 412 management module comprises points 413, badges 414, activity 415, and score 416 management sub-modules. In this and other embodiments, the reputation 412 management module comprises points 413, badges 414, activity 415, and score 416 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 417 module comprises backlinks 418, rank 419, competition 420, search application program interface (API) 421, keyword density 422, keyword placement 423, keyword insertion 424, and content comparison 425 management sub-modules. In this and other embodiments the various sub-modules of the SEO management 417 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 418 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 419 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 420 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 421 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 422, placement 423, and insertion 424 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 425 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 426 management module comprises filter 427, search 428, price 429, taxonomy 430, import 431, differential 432, categories 433, and deals 434 management sub-modules. In this and other embodiments, the filter 427, search 428, price 429, taxonomy 430, import 431, differential 432, categories 433, and deals 434 management sub-modules are used by the affiliate for managing their social commerce storefronts. For example, the filter 427, search 428, price 429, differential 432, deals 434, and import 431 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 430 and categories 433 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 435 management module comprises network 436 and system 437 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 438 module comprises web crawling 439, listening 440, and analytics 441 management sub-modules. In this and other embodiments the web crawling 439, listening 440, and analytics 441 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 439 management sub-module to perform web crawling operations to discover conversation threads associated with its products. Once discovered, the listening 440 management sub-module may be used to monitor the conversations threads, which are then analyzed with the analytics 441 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 442 management module comprises an abuse reporting 443, traffic 444, links 445, Internet Protocol (IP) 446, and dashboard 447 management sub-modules. In this and other embodiments, the abuse reporting 443, traffic 444, links 445, Internet Protocol (IP) 446, and dashboard 447 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 444, links 445, and IP 446 management sub-modules may be used to identify the source of fraudulent behavior. Once identified, it may be reported by the abuse reporting 443 management sub-module and then displayed for review within a user interface by the dashboard 447 sub-module.
In another embodiment, the payment 448 module comprises a traffic 449, payment 450, 1099 Form 451, buyers 452, and payment processor 453, management sub-modules. In this and other embodiments, the traffic 449, payment 450, 1099 451, buyers 452, and payment processor 453, management sub-modules are used by the merchant for the management of payment to affiliates. As an example, the buyers 452 and traffic 449 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 450 and payment processor 453 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 451 sub-module for managing reporting of the commission payments to the affiliate to the Internal Revenue Service (IRS).
In yet another embodiment, the administration 454 module comprises companies 455, target 456, users 457, roles 458, deals 459, moderation 460, profile 461, and email 462 management sub-modules. In this embodiment, the companies 455, target 456, users 457, roles 458, deals 459, moderation 460, profile 461, and email 462 management sub-modules are used by the merchant to administer the various users of the social commerce marketplace system. As an example, the target 456 management sub-module may be used, individually or in conjunction with, the target 456, users 457, profile 461, and roles 458 management sub-modules to identify specific users of a social media environment. Once identified, their social media interactions may be monitored by the moderation 460 management sub-module, and in turn the email 462 and deals 459 management sub-modules may be used individually, or in combination, to target predetermined users.
In still another embodiment, the reports module 463 comprises traffic abuse 463, traffic 465, search engines 466, users 467, content status 468, and competitors 469 reporting sub-modules. In this embodiment, the traffic abuse 463, traffic 465, search engines 466, users 467, content status 468, and competitors 469 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 468 reporting sub-module may report on the status of various items of social commerce content and the search engines 466 reporting sub-module may report on the search results it generates. In turn, the traffic reporting 465 sub-module may be used to report on the social commerce traffic resulting from the search results and the users 467 reporting sub-module may provide reports related to the various users referred to the social commerce site. Likewise, the traffic abuse reporting sub-module 464 may be used to report on various traffic abuses related to the social commerce marketplace system, while the competitors 469 reporting sub-module may provide reports related to competitive activity from competitors.
In various embodiments, the widgets module 470 may comprise web crawling 471, keyword analysis 472, analytics 473, widget manager 474, data 475, semantic analysis 476, catalog management 477, scoring 478, hot spots manager 479, sentiment analysis 480, keyword widget 481, social keyword widget 482, API 483, recommendations engine 484, social score widget 485 and in-line links widget 486 sub-modules. In one embodiment, the web-crawling 471 sub-module is implemented to perform web crawling operations to discover keywords within web pages. In another embodiment, the keyword analysis 472 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 473 sub-module is implemented to provide the utilization of widgets by visitors. In still another embodiment, the widget manager 474 sub-module is implemented to provide a set of user interfaces to configure and publish a widget. In various embodiments, the widget manager 474 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 474 sub-module comprises a wizard that provides a multi-step process to configure the widget. In one embodiment, the widget manager 474 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 475 sub-module is implemented to process social graph, user, and catalog data. In another embodiment, the semantic analysis 476 sub-module is implemented to semantically extract keywords, topics, people and places from strings of text. In another embodiment, the catalog 476 sub-module is implemented with a widget to process catalog data. In yet another embodiment, the hot spots manager 477 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 478 sub-module is implemented to extract positive, neutral and negative tone from strings of text. In one embodiment, the page keyword widget 479 sub-module is implemented to provide a widget that automatically matches catalog products to the context of keywords extracted from a web page. In another embodiment, the social keyword widget 480 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 481 sub-module is implemented to provide an API between a widget and various operating environments. In still another embodiment, the recommendation engine 482 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 783 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 784 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.
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.
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.
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 web pages, 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.
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 offer are blank. If so, then the product offer 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 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 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.
In these and other embodiments, the SEO algorithm determines keyword options for a predetermined product based upon the product's description, its web page content, and other related information. The social commerce marketplace system then uses the SEO algorithm to determine the product's associated search traffic and rank-per-keyword from various search engines. This information, in addition to sales conversion rate information, is used to estimate the likelihood of monetization for a single keyword or a group of keywords. In certain embodiments, the SEO algorithm refines its estimates by tracking and analyzing historical purchase records for a given path and visitor segment. The system then automatically modifies the website pages with optimal combinations of keywords. Once optimized, various search engines are automatically notified of the changes to the web pages to improve organic search rankings.
In various embodiments, the SEO algorithm determines the competitiveness for a predetermined keyword and then assigns it a “level of difficulty” for a user to succeed in organic search optimization. Likewise, the “level of difficulty” is used by the SEO algorithm to determine how much money the user could potentially earn selling products that correspond to a given level of difficulty. In these and other embodiments, the “level of difficulty” is further refined according to analysis of the user's generated content and any additional data the social commerce marketplace system can capture from a visitor to the user's website. The SEO algorithm then determines the likelihood of a relationship or visitor associated with the user's social graph to purchase a predetermined product. Once the likelihood is determined, the social commerce marketplace system creates tasks for the user, monitors the progress of their completion, and makes ongoing recommendations to assist the user in reaching their revenue goals. In one embodiment, a crawler sub-module is implemented with the SEO algorithm to crawl a predetermined domain or website to analyze the market opportunity or financial value of the site. In this embodiment, the output of the analysis is a list of markets to target, and a list of recommendations and tasks to complete, to capitalize on each opportunity.
Referring now to
Title
Short Description
Long Description
Friendly (vanity) URLs
Keywords
Specifications
Ratings
Reviews
Product Blog
Posts to third party social sites about the product
Then, in step 1606, manufacturer links, such as Uniform Resource Locators (URLs), provided in the catalog feeds described in greater detail herein are used by the social commerce marketplace system as primary sources to crawl for product content. In one embodiment, the social commerce marketplace system submits a search request to a search engine to obtain links to crawl if the manufacturer links are not included in the feed. In various embodiments, the crawled content is indexed and used by other process steps described in greater detail herein to identify keywords and high value content.
The social commerce marketplace system then acquires the domains included in the merchant's catalog feed(s) as well as the highest ranked pages within predetermined search engines in step 1608. Then, in step 1610, the acquired domains and website URLs (i.e., backlinks) are submitted to predetermined search engines, as well as other data service providers, to retrieve the number, quality, trust, and other information about the inbound links to each domain. In various embodiments, this information is stored within the social commerce marketplace system and is subsequently used to determine the relative competitiveness of other vendors in the market as well as sources to crawl for recommended content and keywords for use in various SEO operations.
Then, in step 1612, social graph information and social site history from predetermined social network sites for the store's social accounts (e.g., store entity, store owner users, etc.) is retrieved. The retrieved information is then analyzed by the SEO algorithm in step 1614 to identify high-value keywords, content, backlinks and influencers for the product within the social graph(s). In various embodiments, the retrieved product information may be contained in social objects such as “wall posts,” comments, “tweets,” profiles, stores, events, etc. In various embodiments, the retrieved content is semantically analyzed to determine the sentiment (i.e., the “tone” of the content) for each extracted element. In certain of these various embodiments, the social commerce marketplace system scores the retrieved keywords and content according to the source's authoritative value and the content creator's social influence (e.g., their digital worth score).
As used herein, authoritative value broadly refers to the contextual relationship of a keyword to the overall theme of its associated content source. As an example, the search term “Lincoln automobile” may return the phrase “the Lincoln automobile is named after President Abraham Lincoln,” where the content source is a first web page primarily oriented to the history of President Lincoln. In this example, authoritative value is low. As another example, the same search term may return the same phrase, but from a second web page primarily oriented to the history of the Lincoln automobile. In this example, the authoritative value is high.
As likewise used herein, social influence broadly refers to the level of influence a user of a social networking environment is capable of exerting upon a predetermined market segment. In various embodiments, a digital worth score is derived from a user's social influence. As used herein, a digital worth score refers to a numeric value, or set of values, associated with a predetermined user's social influence. As an example, a user may write a blog extolling the virtues of a product, with the result that a high percentage of the readers of the blog purchase the product. In this example, the writer of the blog would have a high digital worth score. In these and other embodiments, the financial value of the associated purchase(s) of the referenced product is used to determine the digital worth score.
In various embodiments, the SEO algorithm uses additional information associated with the content authors and influencers that is stored within the social commerce marketplace system, including their:
name
email addresses
IP Address
geographic location
preferences
The social commerce marketplace system then retrieves available historical clickstream web analytics information in step 1616. In various embodiments, the analytics information is retrieved from corporate web sites associated with the store owner that contain product or product related information. The analytics information is then processed to generate inputs for the SEO algorithm in step 1618. In various embodiments, the retrieved analytics information includes:
Web Analytics Data
-
- Visitor personal information (e.g., name, demographics, prior purchase history, etc.)
- Referring keywords (e.g., associated with source, visitor, geo-location, temporal information, etc.)
- Conversion Data
Listening Platform Data
-
- Content
- Source (e.g., person or entity)
- Sentiment
- Media (e.g., web, television, radio, etc.)
- Location
One or more authoritative sites are then crawled in step 1620 to determine keywords and content related to the product, which may include:
titles
product name
descriptions
ratings
reviews
pricing
discounts
offers
location(s)
As used herein, an authoritative site broadly refers to the contextual relationship of individual content elements within a content source.
To extend the previously-used example, the phrase “the Lincoln automobile is named after President Abraham Lincoln,” in a first web page primarily oriented to the history of President Lincoln may not be considered to be an authoritative site on the Lincoln automobile. Conversely, the same phrase in a second web page primarily oriented to the history of the Lincoln automobile may be considered to be an authoritative site on the Lincoln automobile.
Once the product has been added to the on-line store, the social commerce marketplace system semantically extracts topics, themes and keywords from the product's content and associated metadata in step 1622. In various embodiments, such content and associated metadata comprises:
-
- merchant or manufacturer-defined content (e.g., titles, descriptions, promotion, pricing, etc.)
- store owner-defined content
- content defined by other store owners
- visitor-generated content
- third party content and data sources (e.g., backlinks)
In various embodiments, additional third party data related to the product is extracted and stored within the social commerce marketplace system, including:
sales information, such as:
-
- number of units manufactured and sold
- average sales price
- sales location
ratings and reviews
demographics related to owners of the product
A list of keywords, themes and topics from the previous process steps, along with any additional keywords that were extracted for the same catalog product when it was last added or analyzed for other stores is then generated in step 1624. The resulting list is then submitted to various search engines as well as other data service providers to retrieve additional information in step 1626. Search results corresponding to each element of the submitted list is then received in step 1628. In various embodiments, the search results include:
-
- keyword ideas, referring to additional sets of keywords that are related to the submitted keyword
- local search traffic, referring to the number of searches submitted to the search engine for a predetermined geographic region
- global search traffic, referring to the number of searches submitted to the search engine by all Internet users
- mobile search traffic, referring to the number of searches submitted to the search engine via mobile devices
- frequency, referring to the frequency that the keyword is searched
- competition, referring to the relative frequency of bids combined with the value and associated ad price of each keyword within various advertising networks
- traffic estimation, referring to the estimated traffic, the estimated number of paid visits, the estimated paid search rank, and the estimated paid search cost per day
- category, referring to various businesses, industries, genera's, etc. that the search engine has determined that the keyword is most closely associated with
- domains and websites, referring to a list of the highest-ranked domain or website for a predetermined keyword
- demographics, referring to the demographics corresponding to a set of users that used the keyword
- purchase conversion information, referring to a list of products and prices that a user purchased after searching with a keyword combined with the corresponding site where the purchase was made
- ad competition, referring to the relative market competitiveness of the keyword for a paid search within a commercial search engine service or within an advertising network
- vendors competitive pricing information, referring to a list of top-performing vendors selling a product associated with a predetermined product, combined with its current price
The keyword search results received in step 1628 are then analyzed by the SEO algorithm in step 1630 to generate a keyword score corresponding to each keyword's estimated effect on inbound traffic, conversion rate, competiveness, competitive pricing, and other factors. Then, in step 1632, the SEO algorithm uses a variety of SEO formulas and optimization best practices to process the keyword scores generated in step 1630 to generate a ranked list of keywords predicted to result in the highest amount of traffic and conversion rates.
In step 1634, the user (e.g., an online store owner) uses various financial goal information to set financial goals for the product before it is published to the online store. In various embodiments, the financial goal information may include:
-
- commissions, referring to the amount of monthly commission revenue the store owner would like to generate for the product
- ad revenue, referring to the amount of monthly ad revenue the store owner would like to generate for the product's associated product detail page
- quantity, referring to the number of product units the store owner would like to sell on a monthly basis
A series of market opportunity (i.e., market penetration) scores are then generated in step 1636 from the data collected and analyzed in the previous process steps to identify areas that the product may perform well in (e.g., low competition, high demand, etc.). In various opportunities, these areas may include
-
- local market, referring to one or more local geographic areas
- social network, referring to one or more social networks or populations (i.e., segments) of users
- geo-location/region, referring to a state, country, or other geographic region
- search marketing, referring to a paid search market for a predetermined commercial search engine
- market segment, referring to a group of individuals with similar characteristics
The social commerce marketplace system then uses the preceding goals, selected list of keywords, and opportunity scores in step 1638 to determine the estimated traffic and related SEO elements (e.g., the number of backlinks links, etc.) required to reach the financial goals of the online store. Then, in step 1640, the social commerce marketplace system calculates the estimated difficulty of achieving the financial goals, which provides the store owner the information required to make a decision if they should include the product within their online store. In one embodiment, the financial goal information provided in step 1634 is presented to the online store owner to show the potential financial opportunity by market segment. It will be appreciated that such information would assist the online store owner in focusing and aligning their marketing efforts to those market segments that represent the greatest financial opportunities.
The product is then saved to the online store and its corresponding product details page is published to the online store's website in step 1642, followed by a determination being made in step 1644 whether to continue SEO goal attainment operations. If so, then the process is continued, proceeding with step 1604. Otherwise, SEO goal attainment operations are ended in step 1644.
In one embodiment, the financial goal window 1806 comprises a financial goal amount data entry field 1808 and a ‘Calculate’ command button 1810. In this embodiment, a user enters a financial goal amount in the financial goal amount data entry field 1808 and then selects the ‘Calculate’ command button 1810. The estimated traffic and SEO elements required to reach the financial goal are calculated and then displayed in the requirements window 1812.
In this embodiment, keyword submission optimization operations are begun in step 1902 to automatically insert the keywords generated in step 1624 and selected in step 1632 of the process described in the descriptive text of
Then, in step 1906, the social commerce marketplace system automatically inserts the product title provided by the merchant or a manufacturer into the webpage's HTML title tag. In one embodiment, a user (e.g. the online store owner) can manually update the title tag at any time through a UI. The social commerce marketplace system then automatically inserts the product title provided by the merchant or a manufacturer into the alt image tag for the product image's URL in step 1908. In one embodiment, a user (e.g. the online store owner) can manually update the alt image tag at any time through a UI.
A friendly URL that contains text elements from the product's title is then automatically created by the social commerce marketplace system in step 1910. As used herein, a friendly URL refers to a URL pointing to a location that references a topic or subject that is indicated in the name of the URL. As an example, the URL may contain the name of a product that is promoted within the URL's associated page or site. Then, in step 1912, the social commerce marketplace system automatically inserts the product title from the merchant/manufacture into the webpage's HTML H1 heading tag. In various embodiments, a user (e.g., the online store owner) can manually update any of the H1 through H6 HTML heading tags at any time through a UI. In various embodiments, the social commerce marketplace system also automatically updates other HTML elements expected by commercial search engines such as:
meta content language
meta content type
meta language
meta author
meta copyright
robots meta tag
The target web page is then published to a production instance of the online store in step 1914. Once the target web page is published, the social commerce marketplace system automatically creates an HTML site map for the online store in step 1916 and keeps it updated thereafter. In various embodiments, the web page's index within the site map is updated whenever a material change (e.g., in its page name, title, URL, etc.) occurs.
Skilled practitioners of the art will be aware that it is common for search engine crawlers to use sitemap.xml files to help them index a target website. To assist such search engine crawlers the sitemap.xml file for the online store is automatically updated by the social commerce marketplace system in step 1918 whenever there is a material change (e.g., new page, URL name change, etc.). Those of skill in the art will likewise be aware that it is also common for search engine crawlers to use robots.txt files to help them understand which areas of the site to index. To assist such search engine crawlers the robots.txt file for the online store is automatically updated by the social commerce marketplace system in step 1920 whenever there is a material change (e.g., new page, URL name change, etc.).
Then, in step 1922, the social commerce marketplace system automatically submits the page to various search engines to notify them if there was a change to the online store, such as in the page's HTML elements, its URL, or if the page was newly created or deleted. The social commerce marketplace system then automatically identifies potential issues and creates tasks for the user to remedy them in step 1924. In various embodiments, such issues may include:
not enough keywords
too many keywords
recommended product description text
add keywords to URLs
A determination is then made is step 1926 whether to end keyword submission optimization operations. If not, then the process is continued, proceeding with step 1904. Otherwise, keyword submission optimization operations are ended in step 1928.
In various embodiments, an SEO algorithm is implemented to mitigate the potential deterioration of the earning value of a product promoted by similar online stores by:
-
- generating recommendations for changes to online store of product content, including the generation of content ideas for a user
- generating recommendations regarding where their marketing messages should be syndicated, including automated processed to efficiently perform the syndication
- analyzing the online store and store users' social network to determine its value and then making recommendations to capitalize on the network's potential
- generating recommendations and associated tasks to capitalize on market opportunities
In these and other embodiments, the social commerce marketplace system evaluates the online store's SEO status, individual product detail page SEO status, and other factors to generate tasks for a user (e.g., the store owner) to complete to improve the likelihood of achieving their revenue goals.
In this embodiment, product and store performance optimization operations are begun in step 2102. Then, in step 2104, the social commerce marketplace system retrieves social graph information associated with a user (e.g., an online store owner), which is then used to determine their relationships, influence, reach within their network, and the corresponding influence and reach of each of those relationships for the online store and each store user. Based upon each store owner's digital worth and social graph, the social commerce marketplace system then generates recommended tasks in step 2106 to improve the likelihood of the online store reaching its financial goals. In various embodiments, these tasks include:
-
- comment and posting tasks, including a list of content sources within each social network that should have a comment or response posted by the user
- syndication tasks, including a list of people and the creation of backlinks to the online store
- friend request tasks, including a list of users within each social network the user should build a relationship with due to the user's influence and digital worth score
- store creation, including recommendations on the type of online store to create within each social network and any maintenance the user should complete to keep the store interesting and current
- ads, including recommendations on the type of ads to place within a specific social network
Then, in step 2108 the social commerce marketplace system recommends social networking tasks for the user (e.g., the online store owner), including the creation of new types of blog posts and changes to make to existing blog posts. In one embodiment, the social commerce marketplace system identifies other online store blogs within a social network site that the user should consider building backlinks with to mutually benefit each party. In another embodiment, the social commerce marketplace systems recommends specific blog sites that have the highest market opportunity to attract powerful influencers, who in turn will create backlinks to the on line store. In this and other embodiments, the backlinks are created directly through page links, or indirectly through re-tweets, wall posts, etc. to drive organic traffic to the online store. In yet another embodiment, the social commerce marketplace system recommends the frequency of updates, content to post, and the types of offers to make within the blogs.
In step 2110, the social commerce marketplace system identifies third party influencers and creates recommended tasks that provide both ideas and instructions to obtain backlinks from each target. In one embodiment, in addition to existing targets based on their individual value, the social commerce marketplace system also recommends targets based upon the aggregate long-term value of the market opportunity associated with the target. In this embodiment, a determination is made regarding how valuable the target's social graph and influence are within a market segment and the likelihood that they will generate additional relationships that the online store can capitalize upon in the future.
The social commerce marketplace system then automatically generates recommended tasks in step 2112 to make changes to product detail pages, widgets, store blogs, or the online store's home page to improve the online store's SEO performance and subsequent traffic. In various embodiments, these recommendations are based upon visitor activity within a social network environment, their associated purchase activity, online store content changes, and other information. Then, in step 2114, the social commerce marketplace system generates recommend lead generation tasks, including:
-
- list of contacts to target
- type of communication to use (e.g., tweets, email, posts, etc.)
- type and structure of offers to make (e.g., packaging, bundling, pricing, etc.)
- time and date to send communications
- frequency of re-marketing activities
- recommended campaigns, including outlines of markets to target, the type of campaign to run, and the duration of the campaign.
The social commerce marketplace system then generates recommended search engine tasks in step 2116. In one embodiment, the social commerce marketplace system analyzes the competition and then recommends search engine keyword bidding activities for each identified marketing opportunity. In another embodiment, a recommendation is generated to determine marketing spend allocation to optimize various Search Engine Marketing (SEM) programs. Then, in step 2118, the social commerce marketplace system generates recommended pricing and offers that should be created and presented to each market segment or individual visitor. In various embodiments, the social commerce marketplace system analyzes competitive factors for each market opportunity segment and recommends optimized pricing and discount structures to optimize conversion rates, revenue and margins for each segment.
In step 2120, the social commerce marketplace system analyzes competitive factors (e.g., lower competition, more demand, etc.) for each market opportunity segment and then recommends specific ratings and reviews to associate with each product to optimize conversation and revenue uplift. Then, in step 2122, the social commerce marketplace system identifies markets and market segments that have arbitrage opportunity, which are then used to generate a list of recommended market tasks that capitalize on the arbitrage opportunities. Examples of market arbitrage opportunities include:
high demand|low adoption rates
high demand|low sales penetration rates
high demand|lack of competitive vendor pricing
shifts in buying patterns
high demand|low inventory availability
time-of-product in a market
In one embodiment, the social commerce marketplace system performs an analysis to determine if there is an opportunity (e.g., based upon projected revenue or margin) to liquidate products to a specific market. In another embodiment, the social commerce marketplace system performs an analysis to recommend the type of marketing campaigns to execute within a specific market. In yet another embodiment, the social commerce marketplace system performs an analysis to recommend whether or not the online store should create a micro-site store for a specific market opportunity. In still another embodiment, the social commerce marketplace system performs an analysis to recommend ‘local’ physical locations to open a Flash ‘pop-up’ store based upon a local market opportunity.
Then, in step 2124, the social commerce marketplace system tracks ad spend and response rates across radio, television, and web media to generate recommendations for the optimal allocation of ad spend. In one embodiment, the social commerce marketplace system monitors ad spends for a specific product or product category to determine on-line marketing and merchandising tasks to capitalize on the ad influence to determine which products to sell, where (e.g., region, location, etc.) to sell them, and at what pricing point. In step 2126, the social commerce marketplace system then generated recommendations regarding what types of product to stock according to their anticipated sales rate such that various online stores can optimize their inventory levels to achieve higher net margins for a given market segment or opportunity.
The social commerce marketplace system then generates recommendations in step 2128 regarding which products to sell, which products to bundle, the price of a product, and the discount to apply, based upon the visitor's context or intent and the market opportunity analysis. In one embodiment, the social commerce marketplace system recommends which products to market to a specific market segment. In another embodiment, the social commerce marketplace system recommends which related products to offer or present to visitors based on the current product they are viewing and their market opportunity context. For example, a particular type of hair product may be presented, according to the user's local market trends and demand.
Then, in step 2130, the social commerce marketplace system analyzes procurement demand within predetermined markets and market segments to determine areas of opportunity. Based upon each opportunity, the social commerce marketplace system generates recommendations regarding which products to bid, pricing and packaging. After the recommended tasks have been completed, the social commerce marketplace system re-executes the SEO algorithm in step 2132 to update financial prediction as well as recommended store tasks. A determination is then made in step 2134 whether to end product and store performance optimization operations. If not, then the process is continued, proceeding with step 2104. Otherwise, product and store performance optimization operations are ended in step 2136.
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 optimizing search engine operations, comprising:
- processing a first set of product data to generate a set of candidate keywords, the first set of product data corresponding to a product;
- submitting the set of candidate keywords to a search engine;
- receiving a set of candidate keyword performance values corresponding to the set of candidate keywords, the set of candidate keyword performance values provided by the search engine; and
- processing the set of candidate keyword performance values, search traffic data corresponding to the product data, and sales conversion data corresponding to the product to generate a monetization value for the set of candidate keywords.
2. The computer-implementable method of claim 1, wherein the first set of product data comprises at least one of the set of:
- product title data;
- product description data;
- product promotion data;
- product pricing data;
- associated content data; and
- backlink data.
3. The computer-implementable method of claim 1, wherein:
- a set of alternative keywords and a set of alternative keyword performance values is received from the search engine, the set of alternative keywords corresponding to the set of candidate keywords and the set of alternative keyword performance values corresponding to the set of alternative keywords;
- the set of candidate keyword performance values and the set of alternative keyword performance values is processed to generate a set of optimized keywords;
- the first set of product data and the set of optimized keywords is processed to generate a second set of product data.
4. The computer-implementable method of claim 3, wherein the second set of product data comprises at least one of the set of:
- a keyword meta tag;
- a title tag;
- H1 Hypertext Markup Language (HTML) heading text;
- an alt tag;
- a site map;
- a sitemap.xml file; and
- a robots.txt file.
5. The computer-implementable method of claim 3, wherein the search engine is notified of the generation of the second set of product data.
6. The computer-implementable method of claim 1, wherein the sales conversion data comprises historical purchase data associated with the keywords.
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: processing a first set of product data to generate a set of candidate keywords, the first set of product data corresponding to a product; submitting the set of candidate keywords to a search engine; receiving a set of candidate keyword performance values corresponding to the set of candidate keywords, the set of candidate keyword performance values provided by the search engine; and processing the set of candidate keyword performance values, search traffic data corresponding to the product data, and sales conversion data corresponding to the product to generate a monetization value for the set of candidate keywords.
8. The system of claim 7, wherein the first set of product data comprises at least one of the set of:
- product title data;
- product description data;
- product promotion data;
- product pricing data;
- associated content data; and
- backlink data.
9. The system of claim 7, wherein:
- a set of alternative keywords and a set of alternative keyword performance values is received from the search engine, the set of alternative keywords corresponding to the set of candidate keywords and the set of alternative keyword performance values corresponding to the set of alternative keywords;
- the set of candidate keyword performance values and the set of alternative keyword performance values is processed to generate a set of optimized keywords;
- the first set of product data and the set of optimized keywords is processed to generate a second set of product data.
10. The system of claim 9, wherein the second set of product data comprises at least one of the set of:
- a keyword meta tag;
- a title tag;
- H1 Hypertext Markup Language (HTML) heading text;
- an alt tag;
- a site map;
- a sitemap.xml file; and
- a robots.txt file.
11. The system of claim 9, wherein the search engine is notified of the generation of the second set of product data.
12. The system of claim 7, wherein the sales conversion data comprises historical purchase data associated with the keywords.
13. A computer-usable medium embodying computer program code, the computer program code comprising computer executable instructions configured for:
- processing a first set of product data to generate a set of candidate keywords, the first set of product data corresponding to a product;
- submitting the set of candidate keywords to a search engine;
- receiving a set of candidate keyword performance values corresponding to the set of candidate keywords, the set of candidate keyword performance values provided by the search engine; and
- processing the set of candidate keyword performance values, search traffic data corresponding to the product data, and sales conversion data corresponding to the product to generate a monetization value for the set of candidate keywords.
14. The computer usable medium of claim 13, wherein the first set of product data comprises at least one of the set of:
- product title data;
- product description data;
- product promotion data;
- product pricing data;
- associated content data; and
- backlink data.
15. The computer usable medium of claim 14, wherein:
- a set of alternative keywords and a set of alternative keyword performance values is received from the search engine, the set of alternative keywords corresponding to the set of candidate keywords and the set of alternative keyword performance values corresponding to the set of alternative keywords;
- the set of candidate keyword performance values and the set of alternative keyword performance values is processed to generate a set of optimized keywords;
- the first set of product data and the set of optimized keywords is processed to generate a second set of product data.
16. The computer usable medium of claim 15, wherein the second set of product data comprises at least one of the set of:
- a keyword meta tag;
- a title tag;
- H1 Hypertext Markup Language (HTML) heading text;
- an alt tag;
- a site map;
- a sitemap.xml file; and
- a robots.txt file.
17. The computer usable medium of claim 15, wherein the search engine is notified of the generation of the second set of product data.
18. The computer usable medium of claim 13, wherein the sales conversion data comprises historical purchase data associated with the keywords.
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.
Type: Application
Filed: Oct 12, 2011
Publication Date: Nov 15, 2012
Inventors: Aron England (Austin, TX), Steven Tedjamulia (Austin, TX), Manish C. Mehta (Austin, TX), Ronald Vincent Rose (Austin, TX)
Application Number: 13/271,568
International Classification: G06F 17/30 (20060101);
