Patent Publication Number: US-2013254019-A1

Title: User level incremental revenue and conversion prediction for internet marketing display advertising

Description:
TECHNICAL FIELD 
     The present application relates generally to the technical field of data management and, in one specific example, to user level incremental revenue and conversion prediction for internet marketing display advertising. 
     BACKGROUND 
     In online publication systems, advertisements or related content may be displayed in a particular area of the user interface to promote sales of related products/services. The resulting sales of related products/services can be increased if the displayed advertisements or related content are particularly suited to the user viewing the ads. For example, advertisements may be displayed that relate to content previously searched by a particular user. However, there may be millions of advertisements or related content from which to choose and millions of users to whom the advertisements or related content must be served. It is important to efficiently and quickly determine which advertisements or related content are served to a particular user. But, it is also important to efficiently and quickly determine the appropriate users to whom advertisements should be shown. It is not always cost-effective to show advertisements to certain groups of users. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Some embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings in which: 
         FIG. 1  is a diagrammatic representation of a portion of a user interface according to an example embodiment. 
         FIG. 2  is a diagrammatic representation of a portion of a user interface according to another example embodiment. 
         FIG. 3  is a block diagram of a placement system according to an example embodiment. 
         FIG. 4  is a flowchart of a process according to an example embodiment. 
         FIGS. 5A and 5B  illustrate state diagrams correlating the displaying of secondary content with purchase actions in an example embodiment. 
         FIG. 6  is a flowchart of a process according to an example embodiment. 
         FIG. 7  is a network diagram depicting a client-server system, within which one example embodiment may be deployed. 
         FIG. 8  is a diagrammatic representation of machine in the example form of a computer system within which a set of instructions, for causing the machine to perform any one or more of the methodologies discussed herein, may be executed. 
     
    
    
     DETAILED DESCRIPTION 
     Example methods and systems providing user level incremental revenue and conversion prediction for internet marketing display advertising are described. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of example embodiments. It will be evident, however, to one of ordinary skill in the art that the various embodiments may be practiced without these specific details. In general, well-known instruction instances, protocols, structures, and techniques have not been shown in detail. 
     As used herein, the term “or” may be construed in either an inclusive or exclusive sense. Additionally, although various example embodiments discussed below focus on a network-based publication environment, the embodiments are given merely for clarity in disclosure. Thus, any type of electronic publication, electronic commerce, or electronic business system and method, including various system architectures, may employ various embodiments of the system and method described herein and is considered as being within a scope of example embodiments. Each of a variety of example embodiments is discussed in detail below. 
     In a publication system, a graphical user interface (GUI) may be divided into one or more portions for different types of content. For example, a GUI may include a portion for receiving an input from a user such as a form or a search query box. The GUI may also include one or more content fields. Some GUIs may include a primary content field that includes content of particular interest to the user, for example, an article, a description of an item for sale, a map, or the like. Other GUIs, such as a search results page or a landing page, may not have a primary content field. Regardless of whether a particular GUI has a primary content field, the GUI may comprise slots placed at designated positions within the GUI populated with secondary content such as advertisements (ads), recommended content, related content, or the like. A slot is a predefined area of a GUI at a predetermined position. For example, the size of a particular slot may be defined based on a percentage of the area displayed to a user or be set in pixels. The GUI may be partitioned to include slots using a technology such as frames in hypertext mark-up language (HTML), HTML Tables, Java Script, HTML &lt;div&gt; tags, and the like. 
     It may be desirable to place certain secondary content based on a predicted revenue yield generated by a particular item of secondary content. The “revenue yield” of an item of secondary content displayed in a particular slot is defined as an anticipated revenue to be derived from the user&#39;s interaction with the item of secondary content when it is placed in the particular slot. Examples of interactions include the user following a link in the secondary content (e.g., redirects), sales resulting from merchandizing items described by the primary content (e.g., conversions). For example, in a magazine site, it may be desirable to place a popular article in a slot at a top of the GUI. In an online marketplace, it may be desirable to place popular accessories related to an item for sale near an option to purchase the item. In some instances, a revenue yield may be predicted for each available item of secondary content. The revenue yield may be used to calculate an improved way to populate the slots in an interface with the secondary content. In some instances, the population of the slots is “optimized” using a matrix of the revenue yields for each available item of secondary content. The selection of the best collection of secondary content for a particular user based on user level incremental revenue and conversion prediction is described in more detail below. 
       FIG. 1  is a diagrammatic representation of a portion of a GUI  100  according to an example embodiment. The GUI  100  includes a search box  102 , and a set of slots  104 . The twenty-four slots of the GUI  100  are respectively labeled A1-A3 to H1-H3. Each slot may be available or unavailable for placing secondary content and the GUI  100  may contain a mixture of available and unavailable slots. An unavailable slot is a slot that is populated with secondary content separately from the available slots which can be populated based on revenue yield. For example, a certain slot may be unavailable if it is designated as a “paid” slot that is to be populated with a paid advertisement. The unavailable slots may be independently populated according to a revenue yield. For example, the slots A1, A2, and A3 in the top row of the set of slots  104  may be sold to advertisers. 
     In an online marketplace, the search box  102  may be used to receive a query from a user for descriptions of items for sale. To illustrate, a user may enter, “music player.” The search results may include listings describing items for sale such as an IPOD music player, a ZUNE music player, and a WALKMAN portable cassette player. To present the search results to the user, the listings (or links to the listings) may be used to populate at least a portion of the slots A1 to H3. 
       FIG. 2  is a diagrammatic representation of a portion of a GUI  200  according to another example embodiment. The GUI  200  may include a search box  102  and a primary content field  202 . The GUI  200  also includes two sets of slots, set A  204  and set B  208 . Set A  204  includes a row of slots, A1-A3 along a bottom of the GUI  200 . Set A, as depicted, includes three slots that can be populated with secondary content. In some embodiments, a user may be able to cause the secondary content in the slots A1, A2 and A3 to scroll by selecting scroll buttons  206  on either side of the set A  204 . GUI  200  further includes set B  208  that includes four slots, B1, B2, B3, and B4 positioned vertically along the right side of the GUI  200 . In some instances, the secondary content to be displayed in GUI  200  may be independently determined for set A  204  and set B  208 . For example, set A  204  may be designated for secondary content related to the primary content in the field  202  and set B  208  may be designated for paid content. 
     In an online marketplace, the primary content field  202  may include a product description or item listing that has been selected by the user from the GUI  100 . Set A  204  may be populated with links to descriptions of related products (e.g., music player cases, earphones, batteries, and chargers). Set B  208  may be populated with links to descriptions of other items for sale based, for example, on user search history. 
     While example GUIs  100  and  200  are depicted in  FIGS. 1 and 2 , respectively, it is understood that alternative embodiments may comprise any combination of one or more primary content fields, secondary content fields, and user input fields (e.g., forms and search query boxes). 
       FIG. 3  is a block diagram of a placement system  300  according to an example embodiment. In one embodiment, the placement system  300  may be implemented by way of one or more software modules that include non-transitory instructions embodied on a computer-readable storage medium. In alternative embodiments, the placement system  300  may comprise hardware-based or processor-implemented modules. The placement system  300  is configured to place secondary content in slots within a GUI. In some embodiments, the placement system  300  places secondary content in available slots, but not in unavailable slots. 
     In response to a request for secondary content, a relevancy module  302  is configured to identify a set of secondary content to be used to populate the slots. The request for secondary content may be in the form of, for example, a search query received from a client device of a user, a server call for primary content, a selection received from a client device of a user to provide certain primary content, or the like. The request may include a request for a certain number of items of secondary content that, in turn, may or may not be included in a GUI. 
     In some instances, the request includes the number of items of secondary content to be placed and positions of the slots in the GUI. The request may include a GUI identifier that indicates a format of the GUI to be generated. The GUI identifier may be received from the system providing primary content, a search engine, or the like. Examples of GUI formats are depicted in  FIGS. 1 and 2 . 
     In some embodiments, particularly in online marketplace environments, the relevancy module  302  may include, or have access to, search capabilities to refine the available secondary content to those deemed most relevant to the user or to users who request to view a certain item of primary content. To illustrate, a user may submit a search query for primary content. The secondary content may be content that is determined to be related to the results of the primary content. Therefore, there is a determination by the relevancy module  302  of similar or corresponding categories of content that are related to the primary content. In some embodiments, the relationship of the secondary content to the primary content may be based on user preferences, past histories, user searches, user purchases, etc. But, the relevancy module  302  should also take into consideration the actions and behaviors of other users. For example, if a majority (or high percentage) of users who enter the same search terms for the primary content eventually purchase an accessory related to the primary content, that accessory (and an item of secondary content related thereto) can be identified by the relevancy module and weighted higher or more relevant by the relevancy module  302 . In some instances, a selection or collection of secondary content may be selected from a much larger set of secondary content based on user search or purchase history and preferences, social network data about the user, using algorithms such as collaborative filtering and machine learning. 
     Upon identification of the selected set of secondary content, the yield module  304  is configured to calculate a predicted revenue value associated with the respective items of secondary content. In some instances, the number of items of secondary content may be limited to a pre-defined number. In some instances, the revenue yield is calculated as a time-series estimation or a moving average of a number of factors associated with the item of secondary content. The revenue yield may be a value between 0 and 1. The factors may include a relevancy weight used to determine the relevancy of the item of secondary content by the relevancy module  302 , revenue generated by the website based on traffic to the secondary content (e.g., for paid advertisements), click-through probability, popularity (e.g., most e-mailed, most blogged, most watched), etc. 
     Specifically, in an online marketplace, the revenue yield may be calculated based on factors such as user search history; revenue generated by the online marketplace upon sale of a particular item; click-through history of the item description; if the user has previously purchased, bid on, or watched particular items; time remaining to purchase or bid on an item described in a listing; a number of items remaining for sale. The revenue yield may be calculated using a weighted average, a normalization factor, or the like. For example, a sample embodiment uses a formula such as: 
       revenue yield=0.20*(clickthough probability)+0.40*(price of item)+(0.10)*quality of item+(0.5)*relevancy of item 
     to calculate the revenue yield of a particular item of secondary content. The effect of placing an item of secondary content at a first slot versus at a second slot may be calculated using a second formula or be incorporated into a variable in the above equation, such as “clickthrough probability.” 
     In some embodiments, the revenue is calculated for each item of secondary content based on each particular slot. For example, using moving averages, it may be determined that secondary content A may have a revenue yield of 0.95 if placed in slot A1 but a revenue yield of 0.25 if placed in slot B1. 
     When each item of secondary content is associated with a corresponding revenue yield value, the evaluation module  306  calculates where each item of secondary content should be placed in the slots in the GUI to be presented to the user. In some instances, this may be performed separately for the content to be placed in available slots and in unavailable slots. The evaluation module  306  may first discard items of secondary content associated with a revenue yield that does not meet or exceed a predetermined threshold. The threshold may be determined empirically. 
     In various embodiments, the respective revenue yields are used to populate a matrix where each row is assigned a particular item of secondary content and each column is assigned to a particular slot. The values within the matrix represent an anticipated revenue yield if that particular item of secondary content is used to populate that particular slot. 
     In one embodiment, to calculate the matrix values, the revenue yields associated with the items of secondary content may be multiplied by a multiplier associated with that particular slot. The multiple may be a positive value between zero and one. For example, a left-most slot (being most likely to be selected by a user based on its location) may be associated with a multiplier of 1.0 while a right-most slot may be associated with a multiplier closer to zero, such as 0.1. 
     The evaluation module  306  may then perform a combinatorial optimization algorithm, such as the Hungarian algorithm, on the matrix and/or revenue yields calculated. Other optimization calculations may, additionally or alternatively, be structured such as dynamic programming problems. 
     Based on the results calculated by the evaluation module  306 , a presentation module  308  generates a GUI having available slots populated with the secondary content. The presentation module  308  may generate HTML instructions to send to a user device for displaying the secondary content in the respective slots. 
     It is noted, that depending upon the secondary content identified by the relevancy module  302  and the revenue yields calculated by the evaluation module  306 , two separate users may not have access to the same secondary content even if they are viewing the same primary content. 
       FIG. 4  is a flowchart of a process  400  to place listings according to an example embodiment. The process  400  may be performed by the placement system  300 . 
     In an operation  402 , the relevant items of secondary content are identified. The relevant items of secondary content may include, for example, advertisements, content related to primary content to be displayed to the user, related search results, listings describing items for sale, and user reviews or comments related to the primary content. 
     In an operation  404 , the revenue yields for each item of secondary content is determined. In some instances, the revenue yield is calculated independent of an anticipated placement. In other instances, the revenue yield is determined as a function of its anticipated placement. 
     In an operation  406 , the revenue yields are analyzed using a combinatorial optimization technique to determine how to collectively place the secondary content for a potential maximum yield. 
     In an operation  408 , instructions for generating a GUI are generated. The instructions are generated by the presentation module  308  and transmitted to a client device of the requesting user. The instructions indicate placement of the respective items of secondary content in the available slots included in the GUI based on the analysis of operation  406 . 
     User Level Incremental Revenue and Conversion Prediction 
     For display advertising, especially for real time bidding, if we can predict how much incremental revenue a user is going to bring into a particular e-commerce site, we can decide how much we would like to pay for each impression shown to the user. Note that high revenue may not necessarily imply high incremental revenue, as some active users will visit a particular e-commerce site anyway, whether they see secondary content on the site or not, while some other inactive users do not visit the particular e-commerce site, even if they see a lot of secondary content. The past purchasing or transaction (conversion) history of a particular user can be used to determine a likelihood that the particular user will or will not be affected by viewing secondary content. The past history of presenting secondary content to the user can also be used. The prediction model of an example embodiment provides support for solving this issue. 
     As mentioned above, for many e-commerce systems, some active users will visit a particular website to buy goods or services whether or not they are shown secondary content. Other inactive users will visit the particular website and not make a purchase no matter how many times they are shown secondary content. It is a waste of funds to show secondary content to a user who will not make a purchase (e.g., convert). It is also a waste of funds to show secondary content to a user who will make a purchase regardless. As described in more detail below, the user level incremental revenue and conversion prediction model of an example embodiment provides support to identify which users are likely to be affected by viewing secondary content and convert on the site, thereby bringing in incremental revenue. 
     The user level incremental revenue and conversion prediction model of an example embodiment provides a system and method to predict: 1) if a user is not originally likely to convert on the e-commerce site after viewing secondary content, how likely is it that the user can be affected into becoming a purchaser, and 2) if a user is originally likely to convert on the e-commerce site, how likely is it that the user can be affected into purchasing more than the user would have purchased without viewing the secondary content. 
     Referring now to  FIG. 5A , four prediction models are provided in an example embodiment to predict any of the following conditions:
         a. If a user is not shown secondary content, how likely is it that the user will convert—denoted as P(control).   b. If a user is not shown secondary content and the user is likely to convert, how much is the user likely to buy on the e-commerce site—denoted as G(control).   c. If a user is shown secondary content, how likely is it that the user will convert—denoted as P(test).   d. If a user is shown secondary content and the user is likely to convert, how much is the user likely to buy on the e-commerce site—denoted as G(test).       

     Having defined the conditions of interest and the mechanisms for metering the conditions, we can predict the incremental revenue as follows: 
         P (test)* G (test)− P (control)* G (control) up to the take rate.
 
     Once the incremental revenue is determined for each user by use of the prediction models described above, we can decide how much we are willing to pay for each impression shown to the user. If the user&#39;s predicted incremental revenue is more that the cost of the impression to be shown to the user, we could pay a pre-determined amount, in the real time bidding, to maximize the incremental revenue for the e-commerce site. 
       FIG. 5B  illustrates a state diagram correlating the displaying of secondary content with purchase actions in an example embodiment. As shown in  FIG. 5B , one purpose of the embodiments described herein is to separate States A and D from States B and C. This can be achieved by a classification or prediction model as described above. Once we can separate States A and D from States B and C, we can use a classification model to rank the conversion probability for a particular user. In short, we can use two classification models; one is to differentiate the diagonal and non-diagonal conditions as shown in  FIG. 5B . The other classification model is to differentiate the horizontal conditions as shown in  FIG. 5B . The final score for each user will be the multiplication of the results of the two classification models. This final score can be used to adjust the amount of funds bid for impressions to be shown to the particular user. As a result, the user&#39;s predicted incremental revenue can be correlated to the secondary content shown to the user. 
       FIG. 6  is a processing flow diagram illustrating an example embodiment of a system and method for user level incremental revenue and conversion prediction for internet marketing display advertising as described herein. The method of an example embodiment includes: identifying a plurality of items of secondary content for display to a particular user on an e-commerce site (processing block  1010 ); calculating, using one or more processors, a predicted incremental revenue value for a particular user, the predicted incremental revenue value being based in part on a likelihood that the particular user will convert if the particular user is not shown secondary content, a likelihood that the particular user will convert if the particular user is shown secondary content, and how much the particular user is likely to buy on the e-commerce site if the particular user is not shown secondary content, and how much the particular user is likely to buy on the e-commerce site if the particular user is shown secondary content (processing block  1020 ); using the predicted incremental revenue value for a particular user to rank a conversion probability for the particular user (processing block  1030 ); and generating instructions to place one or more of the plurality of items of secondary content in slots of a graphical user interface (GUI) based on the predicted incremental revenue value and conversion probability for a particular user (processing block  1040 ). 
       FIG. 7  is a network diagram depicting a client-server system  500 , within which one example embodiment may be deployed. A networked system  502 , in the example forms of a network-based marketplace or publication system, provides server-side functionality, via a network  504  (e.g., the Internet or Wide Area Network (WAN)) to one or more clients.  FIG. 7  illustrates, for example, a web client  506  (e.g., a browser, such as the Internet Explorer browser developed by Microsoft Corporation of Redmond, Wash. State), and a programmatic client  508  executing on respective client machines  510  and  512 . The client machine  510  may be a client device of a user submitting the primary content request. In response, a browser of the client machine  510  may generate the GUI shown in  FIGS. 1 and 2  based on the instructions received from the presentation module  308 . 
     An Application Program Interface (API) server  514  and a web server  516  are coupled to, and provide programmatic and web interfaces respectively to, one or more application servers  518 . The application servers  518  host one or more publication applications  520  and payment applications  522 . The application servers  518  are, in turn, shown to be coupled to one or more databases servers  524  that facilitate access to one or more databases  526 . 
     The publication applications  520  may provide a number of publication functions and services to users that access the networked system  502 . In example embodiments, the publication applications  520  encompass the placement system  300 . The payment applications  522  may likewise provide a number of payment services and functions to users. The payment applications  522  may allow users to accumulate value (e.g., in a commercial currency, such as the U.S. dollar, or a proprietary currency, such as “points”) in accounts, and then later to redeem the accumulated value for products (e.g., goods or services) that are made available via the publication applications  520 . While the publication and payment applications  520  and  522  are shown in  FIG. 7  to both form part of the networked system  502 , it will be appreciated that, in alternative embodiments, the payment applications  522  may form part of a payment service that is separate and distinct from the networked system  502 . The placement system  300  may be included in the publication applications  520 . 
     Further, while the system  500  shown in  FIG. 7  employs a client-server architecture, the various embodiments are of course not limited to such an architecture, and could equally well find application in a distributed, or peer-to-peer, architecture system, for example. The various publication and payment applications  520  and  522  could also be implemented as standalone software programs, which do not necessarily have networking capabilities. 
     The web client  506  accesses the various publication and payment applications  520  and  522  via the web interface supported by the web server  516 . Similarly, the programmatic client  508  accesses the various services and functions provided by the publication and payment applications  520  and  522  via the programmatic interface provided by the API server  514 . The programmatic client  508  may, for example, be a seller application (e.g., the TurboLister application developed by eBay Inc., of San Jose, Calif.) to enable sellers to author and manage listings on the networked system  502  in an off-line manner, and to perform batch-mode communications between the programmatic client  508  and the networked system  502 . 
       FIG. 7  also illustrates a third party application  528 , executing on a third party server machine  530 , as having programmatic access to the networked system  502  via the programmatic interface provided by the API server  514 . For example, the third party application  528  may, utilizing information retrieved from the networked system  502 , support one or more features or functions on a website hosted by the third party. The third party website may, for example, provide one or more promotional, marketplace, or payment functions that are supported by the relevant applications of the networked system  502 . In one embodiment, the third party server  520  may provide the paid advertisement that is used to populate the unavailable slots. 
     Additionally, certain embodiments described herein may be implemented as logic or a number of modules, engines, components, or mechanisms. A module, engine, logic, component, or mechanism (collectively referred to as a “module”) may be a tangible unit capable of performing certain operations and configured or arranged in a certain manner. In certain example embodiments, one or more computer systems (e.g., a standalone, client, or server computer system) or one or more components of a computer system (e.g., a processor or a group of processors) may be configured by software (e.g., an application or application portion) or firmware (note that software and firmware can generally be used interchangeably herein as is known by a skilled artisan) as a module that operates to perform certain operations described herein. 
     In various embodiments, a module may be implemented mechanically or electronically. For example, a module may comprise dedicated circuitry or logic that is permanently configured (e.g., within a special-purpose processor, application specific integrated circuit (ASIC), or array) to perform certain operations. A module may also comprise programmable logic or circuitry (e.g., as encompassed within a general-purpose processor or other programmable processor) that is temporarily configured by software or firmware to perform certain operations. It will be appreciated that a decision to implement a module mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software) may be driven by, for example, cost, time, energy-usage, and package size considerations. 
     Accordingly, the term “module” should be understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a certain manner or to perform certain operations described herein. Considering embodiments in which modules or components are temporarily configured (e.g., programmed), each of the modules or components need not be configured or instantiated at any one instance in time. For example, where the modules or components comprise a general-purpose processor configured using software, the general-purpose processor may be configured as respective different modules at different times. Software may accordingly configure the processor to constitute a particular module at one instance of time and to constitute a different module at a different instance of time. 
     Modules can provide information to, and receive information from, other modules. Accordingly, the described modules may be regarded as being communicatively coupled. Where multiples of such modules exist contemporaneously, communications may be achieved through signal transmission (e.g., over appropriate circuits and buses) that connect the modules. In embodiments in which multiple modules are configured or instantiated at different times, communications between such modules may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple modules have access. For example, one module may perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further module may then, at a later time, access the memory device to retrieve and process the stored output. Modules may also initiate communications with input or output devices and can operate on a resource (e.g., a collection of information). 
       FIG. 8  shows a diagrammatic representation of machine in the example form of a computer system  600  within which a set of instructions, for causing the machine to perform any one or more of the methodologies discussed herein, may be executed. In alternative embodiments, the machine operates as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine may operate in the capacity of a server or a client machine in server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine may be a server computer, a client computer, a personal computer (PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), a cellular telephone, a web appliance, a network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein. 
     The example computer system  600  includes a processor  602  (e.g., a central processing unit (CPU) a graphics processing unit (GPU) or both), a main memory  604  and a static memory  606 , which communicate with each other via a bus  608 . The computer system  600  may further include a video display unit  610  (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)). The computer system  600  also includes an alphanumeric input device  612  (e.g., a keyboard), a cursor control device  614  (e.g., a mouse), a disk drive unit  616 , a signal generation device  618  (e.g., a speaker) and a network interface device  620 . Some embodiments may include a touchscreen (not shown). 
     The disk drive unit  616  includes a machine-readable medium  622  on which is stored one or more sets of instructions (e.g., software  624 ) embodying any one or more of the methodologies or functions described herein. The software  624  may also reside, completely or at least partially, within the main memory  604  and/or within the processor  602  during execution thereof by the computer system  600 , the main memory  604  and the processor  602  also constituting machine-readable media. The software  624  may further be transmitted or received over a network  626  via the network interface device  620 . 
     While the machine-readable medium  622  is shown in an example embodiment to be a single medium, the term “machine-readable medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term “machine-readable medium” shall also be taken to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the various embodiments. The term “machine-readable medium” shall accordingly be taken to include, but not be limited to, solid-state memories, optical and magnetic media, and carrier wave signals. Specific examples of machine-readable storage media include non-volatile memory, including by way of example semiconductor memory devices (e.g., Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), and flash memory devices); magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. In one embodiment, the machine-readable medium is a non-transitory machine-readable storage medium. 
     The instructions  624  may further be transmitted or received over a communications network  626  using a transmission medium via the network interface device  620  and utilizing any one of a number of well-known transfer protocols (e.g., HTTP). Examples of communication networks include a local area network (LAN), a wide area network (WAN), the Internet, mobile telephone networks, POTS networks, and wireless data networks (e.g., WiFi and WiMax networks). The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding, or carrying instructions for execution by the machine, and includes digital or analog communications signals or other intangible medium to facilitate communication of such software. 
     Although an overview of the inventive subject matter has been described with reference to specific example embodiments, various modifications and changes may be made to these embodiments without departing from the broader spirit and scope of embodiments of the present invention. Such embodiments of the inventive subject matter may be referred to herein, individually or collectively, by the term “invention” merely for convenience and without intending to voluntarily limit the scope of this application to any single invention or inventive concept if more than one is, in fact, disclosed. 
     The embodiments illustrated herein are described in sufficient detail to enable those skilled in the art to practice the teachings disclosed. Other embodiments may be used and derived there from, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. The Detailed Description, therefore, is not to be taken in a limiting sense, and the scope of various embodiments is defined only by the appended claims, along with the full range of equivalents to which such claims are entitled. 
     Moreover, plural instances may be provided for resources, operations, or structures described herein as a single instance. Additionally, boundaries between various resources, operations, modules, engines, and data stores are somewhat arbitrary, and particular operations are illustrated in a context of specific illustrative configurations. Other allocations of functionality are envisioned and may fall within a scope of various embodiments of the present invention. In general, structures and functionality presented as separate resources in the example configurations may be implemented as a combined structure or resource. Similarly, structures and functionality presented as a single resource may be implemented as separate resources. These and other variations, modifications, additions, and improvements fall within a scope of embodiments of the present invention as represented by the appended claims. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense. 
     Thus, a method and system to provide user level incremental revenue and conversion prediction for internet marketing display advertising have been described. Although the various embodiments have been described with reference to specific example embodiments, it will be evident that various modifications and changes may be made to these embodiments without departing from the broader spirit and scope of the invention. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense. 
     The Abstract of the Disclosure is provided to comply with 37 C.F.R. §1.72(b), requiring an abstract that will allow the reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. In addition, in the foregoing Detailed Description, it can be seen that various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter lies in less than all features of a single disclosed embodiment. Thus the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separate embodiment.