WO2015027223A1

WO2015027223A1 - Page reporting and content performance analytics

Info

Publication number: WO2015027223A1
Application number: PCT/US2014/052411
Authority: WO
Inventors: Jimmy Yu; Thomas J. Ziola; Lemuel S. Park; Sammy Yu; Lennon LIAO
Original assignee: BrightEdge Technologies Inc
Current assignee: BrightEdge Technologies Inc
Priority date: 2013-08-22
Filing date: 2014-08-22
Publication date: 2015-02-26
Anticipated expiration: 2016-02-22

Abstract

A method of determining revenue related to content of referring digital content is disclosed. The method includes receiving a request for accessing a digital content from a referring digital content and determining the referring digital content. The method also includes determining a content category for the referring digital content based on content associated with the referring digital content and determining conversions on the digital content based on the request referred from the referring digital content. The method also includes grouping the conversions associated with the referring digital content with conversions for other referring digital content in a same content category.

Description

PAGE REPORTING AND CONTENT PERFORMANCE ANALYTICS

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This patent application claims priority to U.S. Provisional Patent Application No. 61/869,034, filed August 22, 2013, which is incorporated herein by reference.

FIELD

[0002] The embodiments discussed herein are related to page reporting and content performance.

BACKGROUND

[0003] Companies and individuals may desire to improve the volume and/or quality of traffic to a given webpage or other Internet site to increase sales, brand recognition, dissemination of their product, advertising, or for any other purpose.

[0004] The subject matter claimed herein is not limited to embodiments that solve any disadvantages or that operate only in environments such as those described above. Rather, this background is only provided to illustrate one example technology area where some embodiments described herein may be practiced.

SUMMARY

[0005] According to an aspect of an embodiment, a method of determining revenue related to content of referring digital content is disclosed. The method includes receiving a request for accessing a digital content from a referring digital content and determining the referring digital content. The method also includes determining a content category for the referring digital content based on content associated with the referring digital content and determining conversions on the digital content based on the request referred from the referring digital content. The method also includes grouping the conversions associated with the referring digital content with conversions for other referring digital content in a same content category.

[0006] The object and advantages of the embodiments will be realized and achieved at least by the elements, features, and combinations particularly pointed out in the claims.

[0007] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are not restrictive of the invention, as claimed. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Example embodiments will be described and explained with additional specificity and detail through the use of the accompanying drawings in which:

[0009] Figure 1 illustrates an example embodiment of a content system;

[0010] Figure 2 A illustrates a graph that may be generated by the content system;

[0011] Figure 2B illustrates another graph that may be generated by the content system;

[0012] Figure 2C illustrates another graph that may be generated by the content system;

[0013] Figure 2D illustrates another graph that may be generated by the content system;

[0014] Figure 2E illustrates another graph that may be generated by the content system;

[0015] Figure 2F illustrates another graph that may be generated by the content system;

[0016] Figure 2G illustrates another graph that may be generated by the content system;

[0017] Figure 2H illustrates another graph that may be generated by the content system;

[0018] Figure 21 illustrates another graph that may be generated by the content system;

[0019] Figure 2 J illustrates another graph that may be generated by the content system;

[0020] Figure 3 is a flow chart of an example method of determining revenue related to content of referring digital content; and

[0021] Figure 4 illustrates an example computing device that is arranged to perform any of the computing methods described herein.

DETAILED DESCRIPTION

[0022] In the following detailed description, reference is made to the accompanying drawings, which form a part hereof. In the drawings, similar symbols typically identify similar components, unless context dictates otherwise. The illustrative embodiments described in the detailed description, drawings, and claims are not meant to be limiting. Other embodiments may be utilized, and other changes may be made, without departing from the spirit or scope of the subject matter presented herein. It will be readily understood that the aspects of the present disclosure, as generally described herein, and illustrated in the figures, may be arranged, substituted, combined, separated, and designed in a wide variety of different configurations, all of which are explicitly contemplated herein.

[0023] Examples of various technologies that may be used to measure search performance at the keyword level are described in the following patents and patent applications that are incorporated herein by reference in their entireties: co-pending U.S. Patent Application Serial No. 12/574,069, filed October 6, 2009 entitled CORRELATING WEBPAGE VISITS AND CONVERSIONS WITH EXTERNAL REFERENCES, U.S. Patent No. 8, 190,594 entitled COLLECTING AND SCOPJNG ONLINE REFERENCES, issued May 29, 2012; U.S. Patent No. 8,478,746 entitled OPERATIONALIZING SEARCH ENGINE OPTIMIZATION, issued on July 2, 2013; U.S. Patent No. 8,478,700 OPPORTUNITY IDENTIFICATION AND FORECASTING FOR SEARCH ENGINE OPTIMIZATION, issued on July 2, 2013; co-pending U.S. Patent Application Serial No. 12/853,884, filed on August 10, 2010 entitled SEARCH ENGINE OPTIMIZATION AT SCALE; co-pending U.S. Patent Application Serial No. 13/369,552, filed on February 9, 2012 entitled OPPORTUNITY IDENTIFICATION FOR SEARCH ENGINE OPTIMIZATION; and co-pending U.S. Patent Application Serial No. 13/345,543, filed on January 6, 2012, entitled SYSTEM AND METHOD FOR ESTIMATING ORGANIC WEB TRAFFIC FROM A SECURED SOURCE.

[0024] Search engine marketing performance measurement and optimization systems today commonly define and use key marketing metrics that are based on search engine keywords (i.e., "search terms"). For example, Search Engine Optimization (SEO) systems and/or platforms may typically measure or estimate keyword search engine rank, search engine volumes for specific keywords, and website conversions associated with specific keywords. Among other things, such systems include methods and mechanisms to estimate and track keyword search rank, search traffic, conversions, and revenues resulting from organic search visits to websites. Digital marketing analysis at the keyword-level may be important to the programming logic of virtually all search engine optimization (SEO) reporting, recommendation, and analytic systems. Accordingly, numerous technologies have been developed and are used by marketing professionals to measure search performance at the keyword level. Such systems are widely used by digital agencies, content authors, website designers, application developers, website operators, webmasters, and others to measure search performance and to identify ways to enhance their online marketing efforts and compete more effectively in the digital marketplace.

[0025] However, recent trends in what types of data and how much data search engines pass to websites, if not addressed in an automated search marketing measurement system, may fail to capture important information about keyword and webpage performance. In particular, the rapid rise of "Keyword not provided," otherwise known as encrypted search or secure search, hides important search header information from website measurement systems. In general keyword not provided searches results from search engines that have introduced privacy policies and processing controls on data they pass (search referral header data) to websites. As a result, today many popular websites are experiencing a substantial percentage of organic search traffic that does not pass keyword information from search engines in the search header for visitors who link to their sites. These privacy- protected searches are called "Keyword-Not-Provided" or "encrypted" searches. Indeed, it is common to find 30-60% or more of organic searches on popular keywords encrypted by referring search engines and the percentages may continue to increase. As this percentage continues to climb, web analytics systems and search optimization systems face significant technical challenges in providing accurate reporting and analysis for online marketers. For example, a web analytics system that tracks only referral header information for organic searches may easily under-report the traffic volumes for individual keywords that are driving traffic to a site. Indeed, in the case of lower-volume, long-tailed keywords, it is possible that the traffic from some such keywords could be essentially invisible to the web analytics system, at least at the individual keyword level. In a similar but parallel manner, SEO systems that do not account for encrypted searches, i.e. secured searches, when they extract data about conversion events from web analytics systems run the risk of underreporting individual keyword-driven conversion events and visitor metrics.

[0026] In addition, multiple new content types and many non-search engine marketing channels have emerged that influence visitor traffic, keyword search ranking metrics, website conversion events, among other metrics. A description concerning the new content types and channels is discussed in co-pending U.S. Patent Application Serial No. 13/648,962, filed October 10, 2012, entitled AUDITING OF WEBPAGES, which is incorporated herein by reference in its entirety. Social media sites like Facebook, Twitter, Pinterest, Linkedln, Google+, Quora, dedicated mobile apps for websites, among others as well as new media types such as Youtube, streaming audio, end-user content & reviews, blog posts, and others may influence customer engagement on webpages and traffic to and conversion events on those webpages. They also impact search engine keyword volumes, search ranks, and conversion events on webpages associated with keywords that drive traffic to webpages. A description of how the social media sites may impact or influence customer engagement on websites and traffic to and conversion events on those sites is discussed in co-pending U.S. Patent Application Serial No. 13/270,917, filed October 11, 2011 entitled SEARCH ENGINE OPTIMIZATION RECOMMENDATIONS BASED ON SOCIAL SIGNALS; co-pending U.S. Patent Application Serial No. 13/476,893, filed May 21, 2012, entitled OPTIMIZATION OF SOCIAL MEDIA ENGAGEMENT; and copending U.S. Patent Application Serial No. 13/409,730, filed March 1, 2012, entitled OPTIMIZATION OF SOCIAL MEDIA ENGAGEMENT, which are incorporated herein by reference in their entireties.

[0027] Not only are these other non-search engine channels growing quickly, but data about activity inside these channels by visitors may not be captured or directly measured in web analytics systems that measure activity only once visitors come to a webpage. Indeed, separate and distinct social media management and analytics systems have evolved to monitor these other marketing channels, each using their own types of metrics and reporting methodologies. In addition, it should be noted that many of these new content types and marketing channels might have complex cross-channel effects where activity in one channel may influence activity in other channels. Such interactions are technically difficult to detect in systems that only analyze website traffic performance once a visitor has landed on a webpage.

[0028] In addition, the same or similar content may appear in or be utilized in multiple different channels creating challenges for content authors to measure the impact of their content in different channels. For example, a blog post may include essentially the same information as a Facebook post on a company's Facebook page, but not be recorded as being the same when traffic is directed from the blog and from the company's Facebook page. A video in Youtube could be the same video that appears on a company's website, but perhaps may have different metatags.

[0029] With respect to social media channels, a variety of attribution systems have attempted to capture the impact of activity in specific social media channels by adding "pixels" to user data to track user behavior site. However, only individually tagged users may be tracked in this manner since non-tagged user data is unavailable.

[0030] To help address and understand activities that are happening off a company's main web presence, there are some common approaches by which attribution is handled. The company may first identify the activities happening off a company's main web presence. For example, a company may invest in a Youtube channel or series of Youtube videos. The company would want to understand how that video or groups of videos impact revenue, visits, or conversions on their webpages. The same applies to other channels such as Twitter tweets, Facebook posts, Pinterest pins, and so forth.

[0031] Another method to capture attribution is through query strings. For example, as a company launches a campaign on Twitter, they may want to identify a tweet or series of tweets and see whether it affects activity on their webpages. They may, in the morning send tweets with www.abc.com/promotion.html?campaign=l and then later in the evening send tweets to the same page, but mention the URL as www.abc.com/promotion.html?campaign=2. There are many other tactics deployed by companies, but in each case, the tactics will be deployed to help understand which campaign or which external asset drives performance on my webpages.

[0032] These industry developments taken together magnify the technical challenges that search engine measurement & optimization systems face when attempting to analyze the factors that drive search engine marketing performance and make recommendations for optimizing that performance. Also, the emergence of diverse new content types - ubiquitous and heterogeneous content - and new marketing channels means that there are constantly changes ways for how visitors are influenced or may be directed to visit webpages. In this context, it may be increasingly important to be able to identify, to report on, and to make recommendations regarding the types of content and elements of content that may be contributing to a site's online business performance.

[0033] Search marketing optimization systems may thus be designed to accommodate encrypted search, i.e. secured searches, and evaluate performance at the webpage level, but may also be designed to quickly and efficiently absorb diverse data types from a systems reporting standpoint, and may be able to analyze diverse content to determine the impact such content may have on webpage performance. Such systems may be flexible enough to incorporate information about many different data and content types, and may allow users to quickly & efficiently select different data and content types to analyze and optimize around. Alternately or additionally, such system may be able to suggest relevant factors that appear to be correlated with webpage performance that the user might not have been aware of previously.

[0034] This patent application describes technical solutions that may address these challenges in ways that are highly scalable and describes solutions that may make new data processing models feasible that may enhance the effectiveness, accuracy, and comprehensiveness of marketing performance measurement and optimization systems.

[0035] This patent application may further describe technical solutions that address the growth of encrypted search, i.e. secured searches, that results in reducing the amount of information available to webpages about which organic keywords are delivering visitors to webpages, making it more difficult to determine traffic volumes by individual keywords.

[0036] Further difficulties exist with respect to web analytic systems that need to be addressed. For example, web analytics systems keep detailed logs of web traffic visits and visitor activity across website pages. There may be millions of pages tracked in a web analytics system for a large website. However, these systems do not always report conversion metrics for pages in a way that may be readily mapped back to the actual URLs as they appear in search engine result pages ("SERP"). Failure to resolve the discrepancies between how web analytics systems organize data and how search engines treat webpages and display SERP results may make it difficult to accurately analyze page performance at the page level, especially where organic search is a key parameter being analyzed.

[0037] A number of other things may make it technically difficult to translate web analytics page ids and structures to pages as they are described and listed in search engine SERP results. Three examples of difficulties include: (a) page naming conventions in a web analytics system (such as SiteCatalyst) which utilize page names rather than URLs for identifying and organizing pages; (2) 301 and 302 re-directs which redirect visitors to different pages than the ones they are initially directed to by a search engine and (3) "mayonical" conventions that webmaster use to tell search engine bots which webpages should be considered mayonical webpages for the traffic coming to their sites. A mayonical webpage may be a webpage with a preferred version of a set of webpages with highly similar content. There are often changing dynamic parameters in a URL, even though the content on the webpages associated with those dynamic parameters may be essentially the same webpage.

[0038] What is more, further problems may be encountered by enterprise websites. Enterprise websites typically consist of large numbers of webpages that describe product offerings to visitors. These systems are constructed and instrumented with web analytic systems that track visitors. For example, the system could track what referred the visitors to a webpage (if the data is available), what they do on webpages in the website, where and how they navigate webpages & content, and various "conversion events" associated with their visit to the websites. In some cases, tracking cookies and other tags are used to identify users and enable tracking of users while they access various other web services. Naming conventions are commonly used in web analytics systems to organize pages on large websites. In one specific example, the web analytics system SiteCatalyst, webpages are assigned "page names" that usually, but not always, correspond to how companies are organized or report on key business metrics broadly outside the web analytics team itself. Indeed, as most enterprises grow, it becomes increasingly difficult for web analytics systems to rapidly adapt to new organizational structures and meet reporting needs of diverse internal stakeholders. For sites with hundreds of thousands or millions of webpages, this divergence may make it extremely difficult to coordinate information across a large company. And unless the multiple ways discrepancies may arise between how data is capture inside a web analytics system and search optimization systems is reconciled, page groupings that include search performance metrics may be misleading. In some embodiments, it may be beneficial for marketing teams to be able to analyze and act on performance metrics in ways that align with changing business structures and management reporting needs. It may also be beneficial for marketing teams to be able to reconcile with how external information is captured, such as in search engine optimization systems, rather than how the external information may be recorded in a web analytics system.

[0039] A system and/or methods described herein may provide marketing performance measurement and optimization systems with efficient and accurate ways to analyze metrics in a consistent way at the webpage level across search performance systems and web analytics systems. The system and/or methods described here may also provide an ability to report performance at a group level while incorporating ways that resolve discrepancies between how systems track and analyze data and may be able to accommodate and take into account heterogeneous types of digital content and multiple marketing channels that drive traffic to pages in ways that may be readily understood by diverse marketing stakeholders. Systems that look only at keyword-level data or that try to link web analytics data to search data structures without resolving the different types of systems that record and report performance metrics run the risk of delivering inaccurate or non-representative metrics and sub-optimal recommendations to improve campaign performance.

[0040] Reference will now be made to the figures wherein like structures will be provided with like reference designations. It is understood that the figures are diagrammatic and schematic representations of some embodiments and are not limiting of the present invention, nor are they necessarily drawn to scale.

[0041] Figure 1 illustrates an example embodiment of a content system 100, arranged in accordance with at least some embodiments described herein. The content system 100 may be configured to perform content performance analytics. In particular, the content system 100 may be configured to perform content performance analytics on digital content of an entity. The content performance analytics of digital content may provide insights into how the content of the digital content, e.g. the content that is provided to viewers of the digital content over web browsers, such as video, pictures, written descriptions, sounds, and other content is contributing to revenue generated by the digital content. In some embodiments, the content performance analytics may be similar to keyword performance analytics performed by systems expect instead of focusing on keywords, the analysis focuses on the content of the digital content, and in some embodiments, the categories of the content of the digital content.

[0042] In some embodiments, the content system 100 may include a correlator 104, a deep index engine 108, a content module 110, a mapping module 113, an analyzer module 115, and a reporting module 116. The content system 100 may be communicatively coupled to a network 102. The network 102 may be communicatively coupled to a web analytics module 112, webserver 114, a management system 120, and to digital content 130. It will be appreciated that while these are shown as separate, the components may be combined as desired. For example, in some embodiments, the content system 100 may include the web analytics module 112 and the webserver 114.

[0043] In some embodiments, the network 102 may include the Internet, including a global internet work formed by logical and physical connections between multiple wide area networks and/or local area networks and may optionally include the World Wide Web ("web"), including a system of interlinked hypertext documents accessed via the Internet. Alternately or additionally, the network 102 may include one or more cellular RF networks and/or one or more wired and/or wireless networks such as, but not limited to, 802.XX networks, Bluetooth access points, wireless access points, IP -based networks, or the like. The network 102 may also include servers that enable one type of network to interface with another type of network. A user of the network 102 may access digital content on the network 102.

[0044] Digital content as used herein may include any type of content that may be located on the network 102 or other places. For example, digital content may include non-paid media, such as webpages; websites; blogs; social media including user reviews, social media preferences, social media messages, social media posts, social medial videos, and other content posted in or on a social media platform such as Facebook, Twitter, Pinterest, Linked In, Foursquare, etc.; videos; audio; images, games, applications; emails; chats, paid media, such as ads, banner ads, display ads, videos, audio, images, paid search among other paid media and any other form of content that may be located on a network. In some embodiments, the digital content may also refer to owned media (e.g., content that a marketer owns, such as for example websites, blogs, Twitter or Facebook accounts and related corporate pages and services, etc.); eamed media (e.g., user-generated or user-submitted content, reviews, social media content, and user- generated social media content and other media types), and paid media (e.g., sponsorships, display and banner ads, paid search), with the understanding that sometimes owned media and earned media are used variously and interchangeably in the industry to describe non- paid media generally.

[0045] In some embodiments, the digital content may be linked or otherwise associated. For example, a web site may have multiple webpages that are linked with the web site. The aggregation of visits or estimation of such visits to digital content may be referred to as traffic.

[0046] Digital content as used herein may also refer to any online posting, including domains, subdomains, web posts, Uniform Resource Identifiers (URI), Uniform Resource Locators (URL), images, videos, non-permanent postings such as e-mail and chat among others unless otherwise specified. Digital content may further include any discrete digital item that may be associated with other digital items and may or may not be published online. For example, digital content may include web sites and/or webpages that are in testing phases, development phases, prior to being published, prior to access by users, etc. Digital content may be associated with an entity, which may be any business, corporation, partnerships, collaboration, foundation, individual, or other person or groups of people, that own, have interest in, or may be otherwise affiliated with the digital content.

[0047] Digital content may further include SEO objects. The SEO objects may be any portion of digital content as used herein or information or related data about the digital content that may be used in SEO. For example, search terms, sometimes referred to as keywords; social media promotions; digital items and templates existing within a third party system; items and templates generated by the third party system, and items and templates linked to the third party system are some examples of SEO objects including all other examples of digital content presented herein.

[0048] In some embodiments, digital content may be associated with an entity, which may be any business, corporation, partnerships, collaboration, foundation, individual, or other person or groups of people, that own, have interest in, or may be otherwise affiliated with the digital content. Note that the entity associated with the digital content may be an end-user of the digital content, a controller of the digital content such as the company for which the page reporting and content performance analytics is performed. Alternately or additionally, the digital content may be controlled by a third party enterprise, such as a competitor of the company for which the page reporting and content performance analytics is performed.

[0049] The webserver 114 may be configured to host digital content of the entity. In some embodiments, the webserver 114 may retrieve the digital content from the content 130. For example, the webserver 114 may host a website, webpages, or other digital content of an entity. The webserver 114 may receive incoming requests from the network 102 for digital content of the entity and provide the digital content through the network 102 to the requesting party. For example, the webserver 114 may provide a webpage to a requesting party.

[0050] The web analytics module 112 may be configured to track information about requests for digital content sent to the webserver 114. For example, the web analytics module 112 may be configured to determine where the request for the digital content is originating. All different types of digital content may direct users to a webpages and websites. For example, the digital content may be unique, embedded in other digital content, replicate other digital content, or cross-reference other digital content. The digital content may also be text-based, video content, audio content, interactive, passive, paid, unpaid, among others. Other digital content may include Youtube videos, Facebook posts, Tweets, Pinterest pins, blog mentions, and many other types of "earned media" digital content, as well as search engine results and paid PPC media. All this digital content may direct users to a webpage and influence them to take action on a website. This digital content may also direct the users to a webpage or website by directing users to click on a link or activate an application that subsequently takes a visitor to a webpage where that user is tracked, logged and indexed, etc. Sometimes the digital content many not have a link, but instead it may embed a tracking cookie or pixel into a web browser of the user. The embed tracking cookie or pixel may facilitate attribution for subsequent visits to a webpage or website and related conversion events.

[0051] In these and other embodiments, the web analytics module 112 may determine where the request is originating by looking at information included in the request or by accessing a tracking cookie or pixel. For example, a webpage request from a web browser received by the webserver 114 may include the information regarding the requests origin. As another example, the tracking cookie or pixel in the web browser being used to request the webpage may store the information regarding the origin of the request. In these and other embodiments, the web analytics module 112 may communicate with the webserver 114 or the tracking cookie or pixel to determine from where the request is originating.

[0052] The web analytics module 112 may determine that a request for a webpage of the entity originated from the digital content described above as well as from a blog, a microblog, a social networking site, such as a social networking post, a webpage, a search engine results page, a video, a video player, applications, audio players, among other digital content. [0053] By communicating with the webserver 114, the web analytics module 112 may also determine the requests that result in conversions on the digital content hosted by the webserver 114. A conversion may occur when a page is viewed, a product or service associated with the digital content is purchased, an article is downloaded, or some other event occurs that is designated as a conversion event.

[0054] The deep index engine 108 may be configured to use the SEO objects to collect SEO data associated with the SEO object and/or digital content. For example, when the SEO object is a search term, the deep index engine 108 may perform a search of the network 102 using the search term to produce search results and identify references to an entity within the search results. To identify references to the entity, the deep index engine 108 may be configured to crawl the search results. In some embodiments, the deep index engine 108 may be configured to crawl digital content that linked a visitor to the digital content of an entity. For example, an entity may have a website. A visitor may visit one or more webpages on the website by linking to the webpages from other digital content. The deep index engine 108 may be configured to crawl the digital content that brought the visitor to the webpages of the website of the entity. In these and other embodiments, the deep index engine 108 may be configured to determine the digital content on the digital content that it crawls. The deep index engine 108 may place the results of the crawl and the content discovered in a table or may otherwise store the results of the crawl.

[0055] In some embodiments, the deep index engine 108 may also be configured to crawl the network 102 for digital content associated with the entity or that references the entity. The deep index engine 108 may categorize the digital content that it locates that is associated with the entity or references the entity. For example, digital content that references or includes products, services, or other objects that are produced, affiliated, or otherwise reference the entity may be categorized. In these and other embodiments, the deep index engine 108 may place the results of the crawl and the digital content discovered in a table or may otherwise store the results of the crawl.

[0056] A deep index engine 108 according to some embodiments is described in more detail in copending U.S. Patent No. 8, 190,594 entitled COLLECTING AND SCORING ONLINE REFERENCES, issued May 29, 2012, which is hereby incorporated by reference in its entirety.

[0057] The content module 110 may be configured to receive information from the web analytic module 112, including the referring digital content from which requests for digital content to the webserver 114 originated, along with other information passed to the webserver 114 by the referring digital content. The content module 110 may then categorize the referring digital content.

[0058] In some embodiments, to categorize the referring digital content, the content module 110 may include or may be able to access a data structure that correlates digital content with a category. In these and other embodiments, the content module 110 may search for the referring digital content and determine the category for which the referring digital content should be associated based on the data structure. The categories for the data structure may be defined or determined by various stakeholders, including the webmaster, the owner(s) of digital content, the business unit manager, the earned media manager, or another coordinating body.

[0059] In some embodiments, to categorize the referring digital content, the content module 110 may use information passed by the webserver 114 that is included in the request for the digital content on the webserver 114 by the referring digital content. For example, a header passed to the webserver 114 from the referring digital content may include information indicating the category of the digital content of the referring digital content. The information to pass to the webserver 114 from the referring digital content may result from the referring digital content being tagged with ID's and/or metatags. Alternately or additionally, the information may be passed to the webserver 114 and may be accessed by the web analytics module 112 in referral headers, link headers, or embedded in anchor text.

[0060] The content module 110 may also be configured to determine a category for the referring digital content. The categories may be determined by the content module 110 using a learning algorithm. For example, the content module 110 may inspect the content of referring digital content over time and learn to categorize the content. Alternately or additionally, the content module 110 may categorize the referring digital content based on the business units within the entity that controls the webserver 114.

[0061] Alternately or additionally, the content module 110 may categorize the referring digital content based on categories of digital content generated by the entity that controls the webserver 114. For example, the entity that controls the webserver 114 may also generate or direct the generation of digital content that references or is associated with the digital content hosted on the webserver 114. For example, the entity may generate social media posts on social media websites, blogs on blogging websites, microposts on microblogs, videos on video players, among other digital content. In these and other embodiments, the content created for this digital content may be categorized.

[0062] To categorize referring digital content, the content module 110 may request information from the deep index engine 108 about the referring digital content. In particular, the content module 110 may request information about the content of the digital content from the deep index engine 108. The deep index engine 108 may provide the information that has been previously stored or by crawling the referring digital content and obtaining the information after the request. Based on the information about the content of the referring digital content and based on the categories for the content that is generated or directed to be generated by the entity, the content module 110 may categorize the referring digital content. After the referring digital content is categorized, the content module 110 may note the content of the referring digital content in a data structure for future use.

[0063] In some embodiments, the content module 110 may first search for a category of referring digital content in a data structure. Upon not being able to find the referring digital content, the content module 110 may take no further steps to categorize the content of the referring digital content. Alternately or additionally, the content module 110 may categorize the digital content as explained above.

[0064] Alternately or additionally, the content module 110 may determine how to categorize tags placed on referring digital content that are placed by third parties and are not in-line with the categories in data structure of the content module 110. In these and other embodiments, the content module 110 may have a set of rules to categorize the content.

[0065] Alternately or additionally, the content system 100 may be configured through the deep index engine 108 to identify digital content, which based on correlation analysis, is determined to have affected digital content of the entity. In these and other embodiments, the content module 110 may add the content to the data structure so that if or when the impacting digital content refers a user to the digital content of the entity, the content module 110 may categorize the referring digital content.

[0066] In these and other embodiments, how the digital content is categorized may vary. In some embodiments, the referring digital content may be categorized based on metatag information of the referring digital content. In some embodiments, a method to glean the metatag information may be different depending on the channel. For example, some channels include a built-in mechanism to categorizing the referring digital content. For example, hashtags on Twitter or other direct means (e.g. an application store on iPhone or Android) may provide the category. In other cases, metatag information may be understood by parsing the content of the referring digital content. For example, all the common tweets about a particular topic could be parsed to understand by keyword density or commonly used phrases to understand the metatag information.

[0067] The correlator 104 may be configured to collect SEO data associated with an SEO object and/or digital content among other data associated with digital content. For example, the correlator 104 may receive information from the web analytics module 112 regarding visits, correlations, and referring digital content. Based on this information, the correlator 104 may determine various metrics, such as revenue, conversions, and visits, among others for digital content hosted by the webserver 114 for desired periods. For example, Figures 2G and 2D depict graphs 200G and 200D that may be generated by the reporting module 116 based on the data provided by the correlator 104. The graph 200D illustrates organic visits compared to organic revenue over time. The graph 200G illustrates activity on Google+ related to the entity that may be complied by the correlator 104 based on information from the network 102 or from other locations, such as from the deep index engine 108.

[0068] For example, when the digital content is a webpage, the correlator 104 may determine how many visitors are directed to the webpage resulting from a search using a specific search term, an SEO rank of the digital content based on a specific search term, estimate a total number of visitors to the digital content, etc. based on information from the web analytics module 112. Alternately or additionally, the correlator 104 may determine the number of conversions on a webpage resulting from a search using a specific search term based on information from the web analytics module 112.

[0069] The correlator 104 may also receive information about the category of referring digital content from the content module 110. In these and other embodiments, the correlator 104 may group information from referring digital content in the same category together. In this manner, the correlator 104 may determine traffic, revenue, conversions, among other things generated by referring digital content of the same category.

[0070] The correlator 104 may also be configured to correlate changes in search engine optimization performance of digital content of an entity to changes in digital content that is associated with the entity or that references the entity. For example, the correlator 104 may receive data from the deep index engine 108 indicating an increase in tweets that reference a product produced by the entity. The correlator 104 may also receive data regarding an improvement in search engine rankings of digital content related to the product and/or increase in traffic on digital content of the entity related to the product. The correlator 104 may determine whether the increase in tweets may have resulted, at least partially, from the improvements in the search engine rankings or increase in traffic. Figures 2C, 2F, and 2H illustrate graphs 200C, 200F, and 200H that depict information that may be correlated by the correlator 104.

[0071] Graph 2C illustrates how the number of post likes, mentions, and fan posts, correlate with the visits to digital content of the entity that are referred from Facebook. Graph 2H illustrates how the number of Google Plus audience members correlates with revenue generated from referrals to digital content of the entity from Google Plus. Graph 2F illustrates how the number of Facebook fans correlates with revenue generated from referrals to digital content of the entity from Facebook. Using this information, the correlator 104 may be able to determine how activity on the network 102 may affect revenue even when the activity is not captured by the web analytic module 112. In these and other embodiments, the content module 110 may be configured to categorize the non- referring digital content into the categories in a similar manner that the referring digital content is categorized. This information may be shared with the correlator 104. The correlator 104 may analyze the non-referring digital content and the groupings of the non- referring digital content in a similar manner as the referring digital content. In short, whether the digital content is referring digital content or digital content that results in conversions, revenue, or other trackable metrics, the digital content may be tagged, categorized, tracked, grouped, and analyzed as discussed herein.

[0072] Additionally, the correlator 104 may estimate attribution for various digital content to conversion events by determining the correlation between estimated or measure user exposure and interaction with digital content as well as, and optionally, a user of such a system could incorporate a custom "attribution model" that weighs different content variously in terms of how much impact the marketer/user estimates it to have on conversion events of interest to the marketer. Such attribution models may variously include attribution models suggested by the processing of data by the system (using correlation analysis between measured variables or by machine learning techniques) and user-defined attribution models, and combinations and permutations of various types. Use of a custom attribution model which is defined by the user/marketer may be utilized in such system to accommodate traffic, conversion events, and cross-channel and cross- digital content interactions that may resulting from factors beyond the detection and analytic capabilities of the system itself.

[0073] A correlator 104 according to some embodiments is described in more detail in co-pending U.S. Patent Application Serial No. 12/574,069, filed October 6, 2009 entitled CORRELATING WEBPAGE VISITS AND CONVERSIONS WITH EXTERNAL REFERENCES, which application is hereby incorporated by reference in its entirety.

[0074] The correlator 104 may send the information to the reporting module 116. The reporting module 116 may be configured to generate reports that indicate the information determined by the correlator 104. Figure 2a depicts a graph 200a that may be generated by the reporting module 116. The graph 200a illustrates the revenue generated by referring digital content of various categories. In particular, Figure 2a depicts revenue for various categories over time where the categories include sports athlete interviews, holiday gift ideas, promotional items, new products, others, and customer testimonial. Figure 2b depicts revenue for the categories depicted in Figure 2a in a pie graph 200b that may also be generated by the reporting module 116. Figure 2J depicts a chart 200 J that illustrates revenue generated for tweets based on the categorized tweet content. In addition to depicting revenue, the chart 200j illustrates the mentions, replies, and retweets for the different categories. The chart 200j also depicts how different information collected by the content system 100 may be categorized into categories. In some embodiments, the mentions, replies, and retweets may not be directly related to the revenue, i.e., they may not be the referring digital content, however, the number of mentions, replies, and retweets may be correlated with the revenue based on categories. This level of granularity may allow the content system 100 to determine correlations between none referring digital content and revenue based on categories that would otherwise not be able to be correlated without the categories. In short, the reporting module 116 may generate various types of charts, graphs, illustrations, to illustrate the information generated by the correlator 106.

[0075] Using the graphs 200a and 200b, a better understanding of the referring digital content that is generating revenue may be understood. Previously, it may be determined whether social media, blogs, among other types of digital content were generating revenue. Using the content system 100, the content of the digital content that is generating revenue may be determined. Using this information, digital content of all different types may be generated with content that may generate higher revenue. Furthermore, conversion events associated with various content assets could be analyzed to determine their effectiveness, value to the business, and other information useful to marketing professionals that enables them to better direct investments in different marketing assets. The digital content may thus allow for a digital content marketing performance measurement and better optimization.

[0076] The correlator 104 may also be configured, in some embodiments, to perform online performance analysis metrics at the webpage level of an entity, rather than at the keyword level. Some known SEO systems analyze data and create reports that start with and are organized around keyword-based metrics. In such systems, keyword search engine ranks are shown for keywords where, for each keyword, webpages that rank on that keyword are indicated (along with their respective webpage rank on search engines for that keyword). In a webpage-based marketing analytic system, rather than starting with data and reports organized around keyword-based metrics, data instead may be organized around webpages in various channels and their associated metrics. Analyzing data around webpages rather than keywords enables more direct and accurate analysis of factors that drive a webpage 's online performance and the relative importance of different factors. With a webpage-based model, numerous factors that may contribute to a webpage's overall online performance may be analyzed together side -by-side. Correlations and interactions between different variables are more easily detected in such a model. Deeper analyses of individual factors may also be explored in detailed reports on the individual metrics incorporated in higher-level summary reports on webpage performance. Furthermore, within a single channel, such as organic search, it is possible to further analyze the traffic by individual keywords, such as "top 10 keywords for a webpage."

[0077] In addition, it is possible to further adjust traffic metrics for individual keyword traffic using an allocation formula or algorithm to distribute estimated traffic for each keyword. For example, adjusting traffic metrics is described in co-pending U.S. Patent Application Serial No. 13/345,543, filed on January 6, 2012, entitled SYSTEM AND METHOD FOR ESTIMATING ORGANIC WEB TRAFFIC FROM A SECURED SOURCE. Such an allocation formula or algorithm could include parameters intended to account for the Serial behavior of different visitor segments, demographic groups, or user personae who visit a website. For instance, on a webpage about cocktail drinks, a marketer might assume that different demographic groups are searching on different terms about cocktails (e.g., vodka, gin, whiskey, and scotch) differently and that different visitor demographic groups (e.g., visitors under 30 and visitors over 50) might be more inclined to use encrypted search, i.e. secured search, than other groups. For example, it may be determined that visitors under 30 are more likely to search on vodka than scotch but be more likely to also use encrypted search. In this situation, a system could allow for adjustments for the percentage of encrypted search represented by those younger visitors, resulting in estimated search traffic that is more highly weighted toward vodka than would be the indicated in raw web analytics logs. Figure 2E depicts a graph 200E that may be generated by the reporting module 116 that depicts the impact of encrypted search. [0078] Also, with a webpage -based reporting and analytic system, the performance metrics may be user-selectable or user configurable. The ability to allow user-selectable metrics on a webpage -based reporting system customized to address specific business metrics, organizational structures, channels, categories, data & digital content types, internal business processes, or marketing stakeholders. For instance, measurements of keyword-based metrics may be shown, identifying various channels that contribute to a webpage 's performance, including keyword-not-provided data associated with the webpage.

[0079] A variety of different analyses may now be made with respect to webpage-based analytics. For example, for a webpage or group of webpages associated with a product, a share-of-voice may be calculated for a webpage or for groups of webpages for specific channels, categories, data & digital content types, etc. as discussed in U.S. Patent No. 8,478,746 entitled OPERATIONALIZING SEARCH ENGINE OPTIMIZATION.

[0080] To help to perform online performance analysis metrics at the webpage level, the information from the web analytics module 112 may be aligned with the webpages of the entity. Web analytics modules, such as the web analytic module 112 may have internal naming conventions that are often not readily re-configurable by product line, marketing channel, and other dimensions. In some embodiments, one or more methods described herein may map internal web analytics data formatted for a web analytics system into a reporting structure that organizes performance that is better aligned with organizational structures, business units, product lines, and teams. The internal web analytics data may be mapped in such a way that the method may be executed easily & quickly without having to re-structure the web analytics system itself. Furthermore, in some embodiments, once the data is so organized, information, including performance metrics of webpages in such groups and across the various channels that drive traffic to those pages (organic search information, social media activity, mobile apps, earned media mentions, mobile apps etc.) may be more easily incorporated into a system.

[0081] Before or during the mapping of the information from the web analytics module 112 may be aligned with the webpages of the entity, redirect issues may be resolved. Redirects may occur when web servers redirect web page visitors using redirect codes. Among the various types of redirect codes, common ones include 301 and 302 redirects, which are typically used for permanent and temporary redirects. When these redirects are not correctly resolved, they can complicate the collection and interpretation of website traffic when attempting to collect and analyze website traffic and page performance metrics. There are several ways that redirect codes could be resolved to facilitate the collection and processing of web visitor traffic data that should be associated with web pages and their URLs. One way may be to manually identify and tabulate or record which individual pages have redirects and, then, when attempting to collect and process the information about the underlying web page data, select the corresponding pages associated with specific redirects. Another method is to create a software module that reads and interprets the redirect codes themselves to create a map of webserver redirects automatically. Such a map, created by reading the redirect codes themselves and automatically associating web analytic data with pages after resolving the redirects, may then be used for subsequent data processing and analysis purposes. Such an automated mapping mechanism that reads the redirect codes, also may adapt to changes in website construction and changes in redirect codes as such changes are manifest in website software and page markup language that may be made at any time. Resolution of redirects, using a software module, furthermore, can also be applied before the web page activity data is retrieved from a web analytics system, or in a subsequent step later in the process of analyzing the data to better understand the performance of a page. Canonical representative of webpages may be address in a similar manner as the redirect issues.

[0082] To convert page names to business units, a mapping table may be created by a mapping module 113, which classifies individual pages as belonging to one or more groups. In some embodiments, the mapping table may create a layer on top of the web analytics data reporting structure itself that maps internal page names to actual URLs for the 301 & 302 redirects and relative "mayonical" issues that have been resolved.

[0083] Once the mapping table is in place, the user of such a system may select pages to group together so that conversion data and external signal data related to those pages as well as search engine performance analytics data may be related to pages and correlated with one another. Once such a system is in place, users may include the same page(s) in different groups for business reporting purposes. Such groupings could be hierarchical or horizontal. For example, a shoe department in a retail site may be reported on within a country, or across countries.

[0084] In some embodiments, the mapping module 113 may create the mapping function based on a site crawl of the website of the entity that includes the webpages to be categorized. The crawl may categorize the webpages, identifying webpage-template, webpage-patterns, or other categories that correspond to the company's business structure. Information on how to crawl the website may be described in co-pending U.S. Patent Application Serial No. 13/648,962, filed October 10, 2012, entitled AUDITING OF WEBPAGES. [0085] In some embodiments, the mapping module 113 may create the mapping function by analyzing the structure of the website's content management system (CMS system) to organize page reporting data that may be better aligned to organization teams, business units, product lines, accounting structures, and internal teams. In these and other embodiments, the mapping module 113 may create the mapping function by extracting and processing information about page hierarchy, page layouts, templates, and authoring structures as organized and managed in a CMS system.

[0086] In some embodiments, the mapping module 113 may create the mapping function by analyzing the structure of other management reporting systems, such as KPI dashboards, to determine page groupings that align with organization teams, business units, product lines, accounting structures, and internal teams.

[0087] Alternately or additionally, the mapping module 113 may enable roll-ups of information by different groups to arrive at summary information that may eliminate duplicate summation (e.g., the sum of the parts should equal the whole). For websites that are entirely hierarchical, the summation may be the addition of webpages within a group. For summations across groups that are not hierarchical, an allocation formula may be applied for reporting page metrics for roll -up purposes.

[0088] After grouping the webpages into the categories associated with business units, the performance metrics associated with the groups of pages may be analyzed by an analyzer module 115. By analyzing the performance metric based on business units, a better understanding of what aspects of the business are performing better, such as producing more revenue on a website, may be achieved than if the analyzes was not based on the business units. Figure 21 illustrates a graph 200i that depicts the revenue generated by individual webpages of a website of the entity that have been grouped in business units. For example, graph 200i illustrates the business unit that brings together groups of webpages that include NFL, NHL, Kids, Men, Women, Jerseys. The graph 200i further illustrates the visits. In this manner or other manners, an understanding of a business unit that may be underperforming or over performing may be determined and appropriate resources may be allocated based on the information gathered.

[0089] In some embodiments, a rules-based permission, reporting, and workflow management system 120 may be implemented that operates in conjunction with the content system 100. The management system 120 may be configured to define and manages rules-based permissions for those individuals or teams inside or outside an entity who may be allowed to view, modify and create digital content 130 and/or receive access to analyses of digital content and the performance of such digital content as generated by the web analytics module 112 and the content system 100. A rules based permission system may help to achieve more efficient and coordinated ways to group together digital content for management purposes, including controlling who has access to reports, digital content authoring rights and allowances, among other digital content management functions.

[0090] A rules based system may also assign to specific individuals and/or teams differential rights to view, modify and create digital content 130, to release digital content 130 to the public on a website or other channels, such as through the web server 114, and/or estimate the results of performance-based metrics for individual pieces of digital content, for groups of digital content, and for other related digital content. The rules based system may also allow for perform the above functionality across multiple venues, channels, and locations within certain periods and over time, through the content system 100 or some other system. Alternately or additionally, a rules-based permission system may be applied to a system that includes SEO metrics as one of the metrics being measured, as well as to systems that do not necessarily include SEO metrics. Alternately or additionally, a rules-based permission system could be integrated with a content authoring workflow system, a marketing task management system, and an SEO system as well as a content-optimization system. Reports by team and content types associated with team, recommendations about optimizations, tasks, and/or task assignments associated with different digital content assets, and results by team and by digital content assets that are associated with such teams, etc. could then be implemented. Alternately or additionally, such a rules-based permission system may be used to create systems, reports, and workflows that may address complex organizational needs for large enterprises where roles and responsibilities change and must adapt to fast-moving technical innovations and changing market dynamics.

[0091] Figure 3 is a flow chart of an example method 300 of determining revenue related to content of referring digital content, in accordance with at least some embodiments described herein. The method 300 may be implemented, in some embodiments, by a content system, such as the content system 100 of Figure 1. For instance, the content system 100 of Figure 1 may be configured to execute computer instructions that result in the content system 100 performing operations for managing digital content as represented by one or more of blocks 302, 304, 306, 308, and 310 of the method 300. Although illustrated as discrete blocks, various blocks may be divided into additional blocks, combined into fewer blocks, or eliminated, depending on the desired implementation. [0092] At 302, the method 300 may include receiving a request for accessing a digital content from a referring digital content. At 304, the method 300 may include determining the referring digital content. At 306, the method 300 may include determining a content category for the referring digital content based on content associated with the referring digital content.

[0093] At 308, the method 300 may include determining conversions on the digital content based on the request referred from the referring digital content. At 310, the method 300 may include grouping the conversions associated with the referring digital content with conversions for other referring digital content in a same content category.

[0094] One skilled in the art will appreciate that, for this and other processes and methods disclosed herein, the functions performed in the processes and methods may be implemented in differing order. Furthermore, the outlined steps and operations are only provided as examples, and some of the steps and operations may be optional, combined into fewer steps and operations, or expanded into additional steps and operations without detracting from the essence of the disclosed embodiments. For example, the method 300 may include displaying the grouping of conversions associated with the same content category.

[0095] Some embodiments described herein include a computer program product having computer-executable instructions for causing a computing system having the computer program product to perform a computing method of the computer-executable instructions for managing digital content. The computing method may be any method described herein as performed by a computing system. The computer program product may be located on a computer memory device, which may be removable or integrated with the computing system.

[0096] Some embodiments described herein include a computing system capable of performing the methods described herein. As such, the computing system may include a memory device that has the computer-executable instructions for performing the method.

[0057] In some embodiments, a computing device, such as a computer or memory device of a computer, may include one or more modules or systems discussed with reference to Figures 1. These modules may be configured to perform any of the methods described herein. In addition, these modules may be combined into a single module or on a single platform. In some embodiments, a computer program product may include one or more algorithms for performing any of the methods of any of the claims.

[0097] The present disclosure is not to be limited in terms of the particular embodiments described in this application, which are intended as illustrations of various aspects. Many modifications and variations may be made without departing from its spirit and scope, as will be apparent to those skilled in the art. Functionally equivalent methods and apparatuses within the scope of the disclosure, in addition to those enumerated herein, will be apparent to those skilled in the art from the foregoing descriptions. Such modifications and variations are intended to fall within the scope of the appended claims. The present disclosure is to be limited only by the terms of the appended claims, along with the full scope of equivalents to which such claims are entitled. It is also to be understood that the terminology used herein is for describing particular embodiments only, and is not intended to be limiting.

[0098] In an illustrative embodiment, any of the operations, processes, etc. described herein may be implemented as computer-readable instructions stored on a computer- readable medium. The computer-readable instructions may be executed by a processor of a mobile unit, a network element, and/or any other computing device.

[0099] There is little distinction left between hardware and software implementations of aspects of systems; the use of hardware or software is generally (but not always, in that in certain contexts the choice between hardware and software may become significant) a design choice representing cost vs. efficiency tradeoffs. There are various vehicles by which processes and/or systems and/or other technologies described herein may be effected (e.g., hardware, software, and/or firmware), and that the preferred vehicle will vary with the context in which the processes and/or systems and/or other technologies are deployed. For example, if an implementer determines that speed and accuracy are paramount, the implementer may opt for a mainly hardware and/or firmware vehicle; if flexibility is paramount, the implementer may opt for a mainly software implementation; or, yet again alternatively, the implementer may opt for some combination of hardware, software, and/or firmware.

[00100] The foregoing detailed description has set forth various embodiments of the processes via the use of block diagrams, flowcharts, and/or examples. Insofar as such block diagrams, flowcharts, and/or examples contain one or more functions and/or operations, it will be understood by those within the art that each function and/or operation within such block diagrams, flowcharts, or examples may be implemented, individually and/or collectively, by a wide range of hardware, software, firmware, or virtually any combination thereof. In some embodiments, several portions of the subject matter described herein may be implemented via Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs), digital signal processors (DSPs), or other integrated formats. However, those skilled in the art will recognize that some aspects of the embodiments disclosed herein, in whole or in part, may be equivalently implemented in integrated circuits, as one or more computer programs running on one or more computers (e.g., as one or more programs running on one or more computer systems), as one or more programs running on one or more processors (e.g., as one or more programs running on one or more microprocessors), as firmware, or as virtually any combination thereof, and that designing the circuitry and/or writing the code for the software and or firmware would be well within the skill of one of skill in the art in light of this disclosure. In addition, those skilled in the art will appreciate that the mechanisms of the subject matter described herein are capable of being distributed as a program product in a variety of forms, and that an illustrative embodiment of the subject matter described herein applies regardless of the particular type of signal bearing medium used to carry out the distribution. Examples of a signal bearing medium include, but are not limited to, the following: a recordable type medium such as a floppy disk, a hard disk drive, a CD, a DVD, a digital tape, a computer memory, etc.; and a transmission type medium such as a digital and/or an analog communication medium (e.g., a fiber optic cable, a waveguide, a wired communications link, a wireless communication link, etc.).

[00101] Those skilled in the art will recognize that it is common within the art to describe devices and/or processes in the fashion set forth herein, and thereafter use engineering practices to integrate such described devices and/or processes into data processing systems. That is, at least a portion of the devices and/or processes described herein may be integrated into a data processing system via a reasonable amount of experimentation. Those having skill in the art will recognize that a typical data processing system generally includes one or more of a system unit housing, a video display device, a memory such as volatile and non-volatile memory, processors such as microprocessors and digital signal processors, computational entities such as operating systems, drivers, graphical user interfaces, and applications programs, one or more interaction devices, such as a touch pad or screen, and/or control systems including feedback loops and control motors (e.g., feedback for sensing position and/or velocity; control motors for moving and/or adjusting components and/or quantities). A typical data processing system may be implemented utilizing any suitable commercially available components, such as those generally found in data computing/communication and/or network computing/communication systems.

[00102] The herein described subject matter sometimes illustrates different components contained within, or connected with, different other components. It is to be understood that such depicted architectures are merely exemplary, and that in fact many other architectures may be implemented which achieve the same functionality. In a conceptual sense, any arrangement of components to achieve the same functionality is effectively "associated" such that the desired functionality is achieved. Hence, any two components herein combined to achieve a particular functionality may be seen as "associated with" each other such that the desired functionality is achieved, irrespective of architectures or intermedial components. Likewise, any two components so associated may also be viewed as being "operably connected", or "operably coupled", to each other to achieve the desired functionality, and any two components capable of being so associated may also be viewed as being "operably couplable", to each other to achieve the desired functionality. Specific examples of operably couplable include but are not limited to physically mateable and/or physically interacting components and/or wirelessly interactable and/or wirelessly interacting components and/or logically interacting and/or logically interactable components.

[00103] Figure 4 illustrates an example computing device 400 that is arranged to perform any of the computing methods described herein. In a very basic configuration 402, computing device 400 generally includes one or more processors 404 and a system memory 406. A memory bus 408 may be used for communicating between processor 404 and system memory 406.

[00104] Depending on the desired configuration, processor 404 may be of any type including but not limited to a microprocessor (μΡ), a microcontroller (μΟ), a digital signal processor (DSP), or any combination thereof. Processor 404 may include one more levels of caching, such as a level one cache 410 and a level two cache 412, a processor core 414, and registers 416. An example processor core 414 may include an arithmetic logic unit (ALU), a floating-point unit (FPU), a digital signal-processing core (DSP Core), or any combination thereof. An example memory controller 418 may also be used with processor 404, or in some implementations, memory controller 418 may be an internal part of processor 404.

[00105] Depending on the desired configuration, system memory 406 may be of any type including but not limited to volatile memory (such as RAM), non- volatile memory (such as ROM, flash memory, etc.) or any combination thereof. System memory 406 may include an operating system 420, one or more applications 422, and program data 424. Application 422 may include an analysis algorithm 426 that is arranged to perform the functions as described herein including those described with respect to methods described herein. In some embodiments, application 422 may be arranged to operate with program data 424 on operating system 420.

[00106] Computing device 400 may have additional features or functionality, and additional interfaces to facilitate communications between basic configuration 402 and any required devices and interfaces. For example, a bus/interface controller 430 may be used to facilitate communications between basic configuration 402 and one or more data storage devices 432 via a storage interface bus 434. Data storage devices 432 may be removable storage devices 436, non-removable storage devices 438, or a combination thereof. Examples of removable storage and non-removable storage devices include magnetic disk devices such as flexible disk drives and hard-disk drives (HDD), optical disk drives such as compact disk (CD) drives or digital versatile disk (DVD) drives, solid state drives (SSD), and tape drives to name a few. Example computer storage media may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data.

[00107] System memory 406, removable storage devices 436, and non-removable storage devices 438 are examples of computer storage media. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which may be used to store the desired information and which may be accessed by computing device 400. Any such computer storage media may be part of computing device 400.

[00108] Computing device 400 may also include an interface bus 440 for facilitating communication from various interface devices (e.g., output devices 442, peripheral interfaces 444, and communication devices 446) to basic configuration 402 via bus/interface controller 430. Example output devices 442 include a graphics processing unit 448 and an audio processing unit 450, which may be configured to communicate to various external devices such as a display or speakers via one or more A/V ports 452. Example peripheral interfaces 444 include a serial interface controller 454 or a parallel interface controller 456, which may be configured to communicate with external devices such as input devices (e.g., keyboard, mouse, pen, voice input device, touch input device, etc.) or other peripheral devices (e.g., printer, etc.) via one or more I/O ports 458. An example communication device 446 includes a network controller 460, which may be arranged to facilitate communications with one or more other computing devices 462 over a network communication link via one or more communication ports 464.

[00109] The network communication link may be one example of a communication media. Communication media may generally be embodied by computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and may include any information delivery media. A "modulated data signal" may be a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media may include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), microwave, infrared (IR) and other wireless media. The term computer readable media as used herein may include both storage media and communication media.

[00110] Computing device 400 may be implemented as a portion of a small- form factor portable (or mobile) electronic device such as a cell phone, a personal data assistant (PDA), a personal media player device, a wireless web-watch device, a personal headset device, an application specific device, or a hybrid device that include any of the above functions. Computing device 400 may also be implemented as a personal computer including both laptop computer and non-laptop computer configurations. The computing device 400 may also be any type of network computing device. The computing device 400 may also be an automated system as described herein.

[00111] In some embodiments, the computing device 400 may be referred to as a computing system. Alternately or additionally, a computing system may be an arrangement of multiple different types of computing devices, such as the computing device 400 that are network together to perform methods, procedures, and instructions as discussed herein.

[00112] The embodiments described herein may include the use of a special purpose or general-purpose computer including various computer hardware or software modules. Embodiments within the scope of the present invention also include computer-readable media for carrying or having computer-executable instructions or data structures stored thereon. Such computer-readable media may be any available media that may be accessed by a general purpose or special purpose computer. By way of example, and not limitation, such computer-readable media may comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which may be used to carry or store desired program code means in the form of computer-executable instructions or data structures and which may be accessed by a general purpose or special purpose computer. When information is transferred or provided over a network or another communications connection (either hardwired, wireless, or a combination of hardwired or wireless) to a computer, the computer properly views the connection as a computer-readable medium. Thus, any such connection is properly termed a computer-readable medium. Combinations of the above should also be included within the scope of computer-readable media.

[00113] Computer-executable instructions comprise, for example, instructions and data that cause a general-purpose computer, special purpose computer, or special purpose-processing device to perform a certain function or group of functions. Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.

[00114] As used herein, the term "module" or "component" may refer to software objects or routines that execute on the computing system. The different components, modules, engines, and services described herein may be implemented as objects or processes that execute on the computing system (e.g., as separate threads). While the system and methods described herein are preferably implemented in software, implementations in hardware or a combination of software and hardware are also possible and contemplated. In this description, a "computing entity" may be any computing system as previously defined herein, or any module or combination of modulates running on a computing system.

[00115] With respect to the use of substantially any plural and/or singular terms herein, those having skill in the art may translate from the plural to the singular and/or from the singular to the plural as is appropriate to the context and/or application. The various singular/plural permutations may be expressly set forth herein for sake of clarity.

[00116] It will be understood by those within the art that, in general, terms used herein, and especially in the appended claims (e.g., bodies of the appended claims) are generally intended as "open" terms (e.g., the term "including" should be interpreted as "including but not limited to," the term "having" should be interpreted as "having at least," the term "includes" should be interpreted as "includes but is not limited to," etc.). It will be further understood by those within the art that if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation no such intent is present. For example, as an aid to understanding, the following appended claims may contain usage of the introductory phrases "at least one" and "one or more" to introduce claim recitations. However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles "a" or "an" limits any particular claim containing such introduced claim recitation to embodiments containing only one such recitation, even when the same claim includes the introductory phrases "one or more" or "at least one" and indefinite articles such as "a" or "an" (e.g., "a" and/or "an" should be interpreted to mean "at least one" or "one or more"); the same holds true for the use of definite articles used to introduce claim recitations. In addition, even if a specific number of an introduced claim recitation is explicitly recited, those skilled in the art will recognize that such recitation should be interpreted to mean at least the recited number (e.g., the bare recitation of "two recitations," without other modifiers, means at least two recitations, or two or more recitations). Furthermore, in those instances where a convention analogous to "at least one of A, B, and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., " a system having at least one of A, B, and C" would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). In those instances where a convention analogous to "at least one of A, B, or C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., " a system having at least one of A, B, or C" would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). It will be further understood by those within the art that virtually any disjunctive word and/or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase "A or B" will be understood to include the possibilities of "A" or "B" or "A and B."

[00117] In addition, where features or aspects of the disclosure are described in terms of Markush groups, those skilled in the art will recognize that the disclosure is also thereby described in terms of any individual member or subgroup of members of the Markush group.

[00118] As will be understood by one skilled in the art, for any and all purposes, such as in terms of providing a written description, all ranges disclosed herein also encompass any and all possible subranges and combinations of subranges thereof. Any listed range may be easily recognized as sufficiently describing and enabling the same range being broken down into at least equal halves, thirds, quarters, fifths, tenths, etc. As a non- limiting example, each range discussed herein may be readily broken down into a lower third, middle third and upper third, etc. As will also be understood by one skilled in the art all language such as "up to," "at least," and the like include the number recited and refer to ranges which may be subsequently broken down into subranges as discussed above. Finally, as will be understood by one skilled in the art, a range includes each individual member. Thus, for example, a group having 1-3 cells refers to groups having 1, 2, or 3 cells. Similarly, a group having 1-5 cells refers to groups having 1, 2, 3, 4, or 5 cells, and so forth.

[00119] From the foregoing, it will be appreciated that various embodiments of the present disclosure have been described herein for purposes of illustration, and that various modifications may be made without departing from the scope and spirit of the present disclosure. Accordingly, the various embodiments disclosed herein are not intended to be limiting, with the true scope and spirit being indicated by the following claims. All references recited herein are incorporated herein by specific reference in their entirety.

Claims

CLAIMS What is claimed is:

1. A method of determining revenue related to content of referring digital content, the method comprising:

receiving a request for accessing a digital content from a referring digital content; determining the referring digital content;

determining a content category for the referring digital content based on content associated with the referring digital content;

determining conversions on the digital content based on the request referred from the referring digital content; and

grouping the conversions associated with the referring digital content with conversions for other referring digital content in a same content category.

2. The method of claim 1, further comprising displaying the grouping of conversions associated with the same content category.