Patent Application
20130297436
Businesses of all kinds and sizes have employed various methods for classifying or grouping current and prospective customers for a number of business reasons. Oftentimes termed “customer segmentation” or “market segmentation,” this classification process may help identify specific people as belonging in a particular group having one or more qualities that potentially make them a particularly valuable customer. By identifying one or more such groups, a business may target its advertising and promotional activities to these particular groups, possibly tailoring such activities in accordance with the nature of that group. The targeting of these activities may thus result in more efficient use of the activities by directing them to those groups of current or potential customers that may be the most receptive, responsive, and/or capable of forming, maintaining, or expanding a relationship with the business.
In some situations, a business may calculate a score for each of the potential and/or current customers based on any number of traits or characteristics of each person. The business may then associate a range of such scores with a particular customer or market segment or group. Examples of such scores are credit scores, which are employed by banks and other lending institutions to determine the relative creditworthiness of consumers. Another example is the value score employed by some utility companies to determine the relative value of that customer to the utility in comparison to others.
The specific characteristics or traits that are employed to determine a particular score are typically those that are considered relevant for a particular business or market. The characteristics may include, for example, demographic data, such as the age, gender, geographic location, educational background, work history, marital status, and current income level of the customer. Other data possibly employed in determining a value score may include the purchase history, credit history, personal preferences, and similar information associated with each customer of interest.
The present disclosure is illustrated by way of example and not limitation in the figures of the accompanying drawings, in which like references indicate similar elements and in which:
The description that follows includes illustrative systems, methods, techniques, instruction sequences, and computing machine program products that embody illustrative embodiments. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide an understanding of various embodiments of the inventive subject matter. It will be evident, however, to those skilled in the art that embodiments of the inventive subject matter may be practiced without these specific details. In general, well-known instruction instances, protocols, structures, and techniques have not been shown in detail.
At least some of the embodiments described herein provide various techniques for generating a user or customer value score that, in one implementation, may be employed to divide a customer or market population into useful segments for advertising, promotional, and other commercial purposes. In the examples described below, a score for a particular person may be derived from information relating to both that person as well as other people affiliated with that person. Those affiliated with the person for whom a score is generated may be identified by way of social contact information. This social contact information may provide a stated connection between that person and others, such as by way of a social network or graph. In other examples, the contact information may be data describing communications or other interactions between the person and others, such as within an online commerce system. Further, the generated user value score for the user may take into account money spent by both the user and the affiliations of the user, and may also reflect the quantity and/or quality of the connection or interactions between the user and the affiliations. Other aspects of the embodiments discussed herein may be ascertained from the following detailed description.
Turning specifically to the enterprise application platform 112, web servers 124 and application program interface (API) servers 125 are coupled to, and provide web and programmatic interfaces to, application servers 126. The application servers 126 are, in turn, shown to be coupled to one or more database servers 128 that may facilitate access to one or more databases 130. The web servers 124, application program interface servers 125, application servers 126, and database servers 128 may host cross-functional services 132. The application servers 126 may further host domain applications 134.
The cross-functional services 132 may provide user services and processes that utilize the enterprise application platform 112. For example, the cross-functional services 132 may provide portal services (e.g., web services), database services, and connectivity to the domain applications 134 for users that operate the client machine 116, the client/server machine 117, and the small device client machine 122. In addition, the cross-functional services 132 may provide an environment for delivering enhancements to existing applications and for integrating third-party and legacy applications with existing cross-functional services 132 and domain applications 134. Further, while the system 110 shown in
The portal modules 240 may enable a single point of access to other cross-functional services 132 and domain applications 134 for the client machine 116, the small device client machine 122, and the client/server machine 117 of
The relational database modules 242 may provide support services for access to the database 130 (
The connector and messaging modules 244 may enable communication across different types of messaging systems that are utilized by the cross-functional services 132 and the domain applications 134 by providing a common messaging application processing interface. The connector and messaging modules 244 may enable asynchronous communication on the enterprise application platform 112.
The application program interface (API) modules 246 may enable the development of service-based applications by exposing an interface to existing and new applications as services. Repositories may be included in the platform 112 as a central place to find available services when building applications.
The development modules 248 may provide a development environment for the addition, integration, updating, and extension of software components on the enterprise application platform 112 without impacting existing cross-functional services 132 and domain applications 134.
Turning to the domain applications 134, customer relationship management applications 250 may enable access to, and facilitate collecting and storing of, relevant personalized information from multiple data sources and business processes. Enterprise personnel that are tasked with developing a buyer into a long-term customer may utilize the customer relationship management applications 250 to provide assistance to the buyer throughout a customer engagement cycle.
Enterprise personnel may utilize financial applications 252 and business processes to track and control financial transactions within the enterprise application platform 112. The financial applications 252 may facilitate the execution of operational, analytical, and collaborative tasks that are associated with financial management. Specifically, the financial applications 252 may enable the performance of tasks related to financial accountability, planning, forecasting, and managing the cost of finance.
Human resources applications 254 may be utilized by enterprise personnel and business processes to manage, deploy, and track enterprise personnel. Specifically, the human resources applications 254 may enable the analysis of human resource issues and facilitate human resource decisions based on real-time information.
Product life cycle management applications 256 may enable the management of a product throughout the life cycle of the product. For example, the product life cycle management applications 256 may enable collaborative engineering, custom product development, project management, asset management, and quality management among business partners.
Supply chain management applications 258 may enable monitoring of performances that are observed in supply chains. The supply chain management applications 258 may facilitate adherence to production plans and on-time delivery of products and services.
Third-party applications 260, as well as legacy applications 262, may be integrated with domain applications 134 and utilize cross-functional services 132 on the enterprise application platform 112.
Additionally, collaborative applications 264 may facilitate joint creation and modification of documents and other work product by multiple users, and data management applications 266 may enable data organization and other management functions to be performed on data generated by one or more other domain applications 134.
As discussed below, the data retrieved and generated by the user value scoring system 300 may be related to a particular online commerce system. Examples of an online commerce system, as depicted in
The customer data access module 302 may access and retrieve data for a user or customer describing activities of the user with respect to an online commerce system. In the example of
The affiliation detection module 304 may detect or determine one or more people that are affiliated with a particular user or customer of the online commerce system. In one example, the affiliation detection module 304 accesses a social networking system 340 to identify friends of a particular user. In this case, the social networking system 340 may be an online system primarily dedicated to social networking, such as Facebook® and Myspace®. As a result, one or more of the friends identified via the social networking system 340 may not necessarily be users or customers of the online commerce system.
In other implementations, the social networking system 340 may be coupled with, or be an integrated part of, the online commerce system, such as the online gaming system 320 or the online sales system 330. Under these circumstances, the people identified by the affiliation detection module 304 as being affiliated with the particular user are likely to be users of the associated online commerce system. In addition, in some implementations, the identified affiliations of a user may possess some relationship with the particular user whose score is to be generated within the online commerce system. In the embodiment of the online gaming system 320, the identified affiliations may be users that have a relationship with the particular user. For example, the identified users may be in the same gaming party or group with the particular user, or may be identified as friends of the particular user specifically within the context of the online gaming system 320. In another implementation, users for whom the particular user serves in a kind of leadership role, such as the head of a group or party within the online gaming system 320, may be identified as the affiliated users. In at least some of these implementations, the user may be considered to have some influence over the purchase decisions of the affiliated users.
After the people affiliated with the particular user are identified, the user value scoring system 300 may then utilize the customer data access module 302 to access customer data for each of the identified individuals. As is described more fully below, this customer data may also be employed to generate the user value score for the particular user.
In some implementations, the affiliation detection module 304 may access or retrieve data indicating a relationship strength between the particular user for which a value score is being calculated and other users affiliated with the particular user. More specifically, the affiliation detection module 304 may retrieve information quantifying a number of interactions between the particular user and another person for each of a number of different types of interactions. In one implementation, these interactions may take place through a system especially adapted for social networking, such as the social networking system 340. In this case, examples of the interactions may include messages transmitted or passed between the particular user and people affiliated with that user, passing of content between the particular user and the affiliations of the user, wall postings between the parties, comments provided between the particular user and others, and the like.
In another implementation, the affiliation detection module 304 may retrieve information regarding the number and type of interactions between parties that occur through the online commerce system, such as the online gaming system 320 or the online sales system 330. Examples of these interactions may include, but are not limited to, a number of times the particular user and an affiliated user worked toward the same task or goal in an online game, the number of times the particular user and the affiliated user joined the same player group or unit within the online game, the amount of time the particular user and the affiliated user were part of the same player group unit, and so on.
The weighting module 306 may receive at least some of the data retrieved by the customer data access module 302 and/or the affiliation detection module 304 and weight that data according to some formula or algorithm, thus preparing that data for use in calculating a user value score. In one example, purchase data (e.g., an amount of money spent via the online commerce system over a predefined period of time) for each of the users affiliated with the particular user for whom the value score is being generated may be weighted by some factor, such as a predetermined weighting factor, in preparation for combining the weighted data with purchase data of the particular user. In another example, the weighting factor may be a value calculated from interaction data provided by the affiliation detection module 304. Such weighting examples are more completely described below in conjunction with
The score calculation module 308 may receive the weighted data from the weighting module 306 and combine that data with information regarding the value of the particular user being scored to generate the user value score for the particular user. In one example, more specifically described below in conjunction with
In one implementation, the user value scoring system 300 may generate user value scores for any or all of the users of the online commerce system of interest. Once these user value scores are generated, a customer segmentation module 350 may employ these scores to apportion the users among multiple customer or market segments based on their value to the online commerce system. Accordingly, the online commerce system may direct various promotional and/or advertising activities to users identified with specific customer segments. For example, users associated with relatively high-value scores may be the target of ongoing promotional activities designed to persuade the users to maintain their purchasing levels with the online commerce system. In other examples, the online commerce system may direct one-time enticements to lower-value users to encourage a higher level of involvement with the online commerce system.
At least some of the modules 302-308 of the user value scoring system 300 may serve as domain applications 134 illustrated in
Based on identifying the other users, the customer data access module 302 may determine a spending amount by each of the other users at the online commerce system (operation 406). The weighting module 306 may then weight the spending amounts of the other users (operation 408). This weighting may be performed in a number of ways, such as by way of a predetermined weighting factor, by use of a calculated weighting factor that considers the nature, number, and possibly other qualities of interactions between the identified affiliated users and the first user, or via some other method. The score calculation module 308 may then calculate a user value score for the first user based on the spending amount of the first user and the weighted spending amounts of the other users (operation 410). The generation of the user value score may be performed for any number of the users of the online commerce system, allowing the customer segmentation module 350 to associate each of the users with a specific market segment identified with a range of user value scores.
While the operations 402 through 410 of the method 400 of
In the method 500, the weighting module 306 (
The weighting module 306 may then multiply the total average spending by the affiliated users by a predetermined weighting factor to yield a weighted average spending amount for the affiliated users (operation 508). In one example, the predetermined weighting factor may be some value less than one (e.g., 0.5) since the amount of money spent by users affiliated with the first user may be viewed as being only partially due to the influence of the first user, whether as a friend, a fellow game-player, or in some other capacity. The average spending amount of the first user may then be added to the weighted average spending amount of the affiliated users to produce a value score for the first user (operation 510). The resulting value score as described in the method 500 of
Score(User0)=AvgSpendAmt(User0)+(WgtFactor×Σi=1n AvgSpendAmt(Useri))
In this equation, Score is the value score being calculated, User0 is the user (i.e., the first user) for whom the value score is being calculated, each Useri is one of the n users affiliated with User0, AvgSpendAmt is the average spending amount for the associated user, and WgtFactor is the predetermined weighting factor described above. As a result, the score for a first user includes not only a value to the online commerce system resulting directly from the first user, but also at least a portion of the value provided by other users that are affiliated with the first user.
The weighting module 306 may then multiply the retrieved average number of interactions by an interaction factor for the current interaction type to yield a weighting term for the current interaction type and current affiliated user (operation 604). In one example, the interaction factor may be a predefined value associated specifically with the current interaction type. In another implementation, the interaction factor may be the same for two or more of the interaction types. If more interaction types are to be processed (operation 606), the affiliation detection module 304 may determine the next interaction type to be processed for the current affiliated user (operation 608), resulting in that interaction type being processed in operations 602 and 604, as described above. This process may continue until all interactions types for the current affiliated user are processed.
If no more interaction types for the current affiliated user remain to be processed (operation 606), the weighting module 306 sums the weighting term for each interaction type of the current affiliated user to produce a weighting factor for the current affiliated user (operation 610). This weighting factor may be viewed as serving a role similar to that of the weighting factor described in the method 500 of
The affiliation detection module 304 may then determine if more users affiliated with the first user are available for processing (operation 614). If so, the affiliation detection module 304 may pass the identity of the next affiliated user to the customer data access module 302 and the weighting module 306 to produce a spending term for the current affiliated user via operations 602 through 612 (operation 616). Once all pertinent affiliated users have been processed (operation 614), the score calculation module 308 may then sum the generated spending terms for all of the affiliated users and add the average spending amount for the first user retrieved by the customer data access module 302 to yield the value score for the first user (operation 618). The resulting value score as described in the method 600 of
Score(User0)=AvgSpendAmt(User0)+Σi=1n(Σj=1n(IntNumi,j×IntFactorj)×AvgSpendAmt(Useri))
In this equation, Score is the value score being calculated, User0 is the user (i.e., the first user) for whom the value score is being calculated, each Useri is one of the n users affiliated with User0, AvgSpendAmt is the average spending amount for the associated user, IntNumi,j is the number of interactions between the first user and Useri for interaction type j, and IntFactorj is the interaction factor for interaction type j. Thus, in this particular method 600, the score for a first user includes not only a value to the online commerce system resulting directly from the first user, but also at least a portion of the value provided by other users that are affiliated with the first user. In addition, the value provided by the other users is adjusted based on interactions or connections between that user and the first user.
In both methods 500 and 600 described above, a first user that does not spend anything through the online commerce system may still be of value to the online commerce system by virtue of what the users affiliated with the first user (e.g., friends, gaming companions, and so on) spend. In addition, the number of affiliated friends, the amount of money they spend at the online commerce system, and possibly the quantity and/or quality of the interactions between the first user and the affiliated users may affect the overall value attributed to the first user.
As a result of at least some of the embodiments described above, user scores indicating a value of a user with respect to an online commerce system, such as an online sales system or an online gaming system, are generated. The scores may take into account the spending amounts of both the user for whom the score is being generated and the users affiliated with that user. Further, in the calculation of the value score for a first user, the spending amounts of the affiliated users may be weighted relative to that of the first user by a weighting factor that is either predetermined or is based on a level of interaction between the first user and the affiliated users. The resulting user value scores for the users of the online commerce system may then be used to segment the users into different customer segments for advertising, promotional, and other business purposes.
While various embodiments discussed above emphasize an online sales system and an online gaming system as two examples of online commerce systems to which the embodiments are directed, other types of online commerce systems, such as weblogs, news websites, websites dedicated to specific topics, websites hosted by specific corporations, search engine websites, and the like may also benefit from application of the various principles discussed herein.
The machine is 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 of the processing system 800 includes a processor 802 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), or both), a main memory 804 (e.g., random access memory), and static memory 806 (e.g., static random-access memory), which communicate with each other via bus 808. The processing system 800 may further include video display unit 810 (e.g., a plasma display, a liquid crystal display (LCD), or a cathode ray tube (CRT)). The processing system 800 also includes an alphanumeric input device 812 (e.g., a keyboard), a user interface (UI) navigation device 814 (e.g., a mouse), a disk drive unit 816, a signal generation device 818 (e.g., a speaker), and a network interface device 820.
The disk drive unit 816 (a type of non-volatile memory storage) includes a machine-readable medium 822 on which is stored one or more sets of data structures and instructions 824 (e.g., software) embodying or utilized by any one or more of the methodologies or functions described herein. The data structures and instructions 824 may also reside, completely or at least partially, within the main memory 804, the static memory 806, and/or within the processor 802 during execution thereof by processing system 800, with the main memory 804 and processor 802 also constituting machine-readable, tangible media.
The data structures and instructions 824 may further be transmitted or received over a computer network 850 via network interface device 820 utilizing any one of a number of well-known transfer protocols (e.g., HyperText Transfer Protocol (HTTP)).
Certain embodiments are described herein as including logic or a number of components, modules, or mechanisms. Modules may constitute either software modules (e.g., code embodied on a machine-readable medium or in a transmission signal) or hardware modules. A hardware module is a tangible unit capable of performing certain operations and may be configured or arranged in a certain manner. In example embodiments, one or more computer systems (e.g., the processing system 800) or one or more hardware modules of a computer system (e.g., a processor 802 or a group of processors) may be configured by software (e.g., an application or application portion) as a hardware module that operates to perform certain operations as described herein.
In various embodiments, a hardware module may be implemented mechanically or electronically. For example, a hardware module may include dedicated circuitry or logic that is permanently configured (for example, as a special-purpose processor, such as a field-programmable gate array (FPGA) or an application-specific integrated circuit (ASIC)) to perform certain operations. A hardware module may also include programmable logic or circuitry (for example, as encompassed within a general-purpose processor 802 or other programmable processor) that is temporarily configured by software to perform certain operations. It will be appreciated that the decision to implement a hardware module mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (for example, configured by software) may be driven by cost and time considerations.
Accordingly, the term “hardware 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 and/or to perform certain operations described herein. Considering embodiments in which hardware modules are temporarily configured (e.g., programmed), each of the hardware modules need not be configured or instantiated at any one instance in time. For example, where the hardware modules include a general-purpose processor 802 that is configured using software, the general-purpose processor 802 may be configured as respective different hardware modules at different times. Software may accordingly configure a processor 802, for example, to constitute a particular hardware module at one instance of time and to constitute a different hardware module at a different instance of time.
Modules can provide information to, and receive information from, other modules. For example, the described modules may be regarded as being communicatively coupled. Where multiples of such hardware modules exist contemporaneously, communications may be achieved through signal transmissions (such as, for example, 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 (for example, a collection of information).
The various operations of example methods described herein may be performed, at least partially, by one or more processors 802 that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors 802 may constitute processor-implemented modules that operate to perform one or more operations or functions. The modules referred to herein may, in some example embodiments, include processor-implemented modules.
Similarly, the methods described herein may be at least partially processor-implemented. For example, at least some of the operations of a method may be performed by one or more processors 802 or processor-implemented modules. The performance of certain of the operations may be distributed among the one or more processors 802, not only residing within a single machine but deployed across a number of machines. In some example embodiments, the processors 802 may be located in a single location (e.g., within a home environment, within an office environment, or as a server farm), while in other embodiments, the processors 802 may be distributed across a number of locations.
While the embodiments are described with reference to various implementations and exploitations, it will be understood that these embodiments are illustrative and that the scope of claims provided below is not limited to the embodiments described herein. In general, the techniques described herein may be implemented with facilities consistent with any hardware system or hardware systems defined herein. Many variations, modifications, additions, and improvements are possible.
Plural instances may be provided for components, operations, or structures described herein as a single instance. Finally, boundaries between various components, operations, and data stores are somewhat arbitrary, and particular operations are illustrated in the context of specific illustrative configurations. Other allocations of functionality are envisioned and may fall within the scope of the claims. In general, structures and functionality presented as separate components in the exemplary configurations may be implemented as a combined structure or component. Similarly, structures and functionality presented as a single component may be implemented as separate components. These and other variations, modifications, additions, and improvements fall within the scope of the claims and their equivalents.
