Patent Publication Number: US-2020286110-A1

Title: System and method for creating segmentation of a populuation

Description:
TECHNICAL FIELD 
     The present disclosure generally relates to computerized systems and methods for creating a segmentation of a general population of consumers. The present disclosure also relates to creating a segmentation according to motivation- or value-based differences. 
     BACKGROUND 
     A vendor may target specific consumers, in a population of consumers, to address individualized marketplace needs. For example, a vendor may provide promotions customized for certain potential customers. Such promotional content (e.g. advertisements) may be uniquely tailored to different consumers. Personalizing promotional content for electronic delivery can lead to an increase in revenues, but there are some shortcomings. For example, marketing to address singular customer needs may be overly burdensome, time consuming, infeasible due to scalability, and expensive. 
     Consumer needs and wants may overlap with other needs and wants. Marketing based on dividing a prospective consumer audience into discrete categories representative of a particular defining characteristic can be beneficial. For example, clustering based on select behavioral data, demographic data, and product preferences may improve efficiency and reduce cost. Segmentation according to these conventional categories, however, may deprive a marketer of the benefits of marketing by category. For example, two consumers of the same age may receive the same advertisement because they are categorized in the same manner based on age. However, these consumers may be at different stages of their lives and thus have different motivations or values. This can lead to one consumer in the category enthusiastically purchasing the advertised product while the other consumer is vehemently opposed to purchasing the product. Segmenting these two consumers solely on a single basis (e.g., age) can be inefficient and ineffective. Therefore, there is a need for an improved method of segmentation that addresses the values, attitudes, and interests that inform the decision-making processes of individual consumers. 
     SUMMARY 
     One aspect of the present disclosure is directed to a system including a processor. The system may filter a population to identify a group having members, and deliver, over an electronic network, a survey to members of the group to determine value preferences and value gaps (e.g., gaps between a current state and a desired state relative to a particular value) for the members of the group. The survey may be made according to a set of paired comparisons. The system may generate vectors according to the determined value preferences and value gaps for members of the group. The vectors may have lengths equal to numbers of determined values for the members. The system may cluster members of the group into multiple segments by calculating differences between the vectors. The system may also create a predictive model to estimate segment membership derived from the survey of a subset of consumers from the full potential consumer population by aid of a marketing database. The system may also generate a promotion for display corresponding to a clustered segment, and deliver the promotion to a client device associated for display. 
     Another aspect of the present disclosure is directed to a method for creating segmentation of a population. The method may be performed by a processor configured to execute the instructions. The method may include filtering a population to identify a group having members, and delivering, over an electronic network, a survey to members of the group to determine value preferences and value gaps for the members of the group. The survey may be made according to a set of paired comparisons. The method may include generating vectors according to the determined value preferences and value gaps for members of the group, and the vectors may have lengths equal to numbers of determined values for the members. The method may include clustering members of the group into multiple segments by calculating differences between the vectors. The method may also include creating a predictive model to estimate segment membership derived from the survey of a subset of consumers from the full potential consumer population by aid of a marketing database. The method may also include generating a promotion for display corresponding to a clustered segment, and delivering the promotion to a client device associated for display. 
     Yet another aspect of the present disclosure is directed to a non-transitory computer-readable medium. The non-transitory computer-readable medium may store instructions executable by a processor to perform for a method for creating a segmentation of a population. The method may include filtering a population to identify a group having members, and delivering, over an electronic network, a survey to members of the group to determine value preferences and value gaps for the members of the group. The survey may be made according to a set of paired comparisons. The method may include generating vectors according to the determined value preferences and value gaps for members of the group, and the vectors may have lengths equal to a number of determined values for the members. The method may include clustering members of the group into multiple segments by calculating differences between the vectors. The method may also include creating a predictive model to estimate segment membership derived from the survey of a subset of consumers from the full potential consumer population by aid of a marketing database. The method may also include generating a promotion for display corresponding to a clustered segment, and delivering the promotion to a client device associated for display. 
     Other systems, methods, and computer-readable media are also discussed herein. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a diagrammatic illustration of prior art identifying an exemplary organization of human values by the correlational structure of their individual importance scores; 
         FIG. 2  is a schematic block diagram illustrating an exemplary embodiment for creating a segmentation of a population; 
         FIG. 3  is a schematic block diagram illustrating an exemplary embodiment of a network server, consistent with the disclosed embodiments; 
         FIG. 4  is a schematic block diagram illustrating an exemplary embodiment of an online advertising system, consistent with the disclosed embodiments; 
         FIG. 5  is a flow chart illustrating an exemplary process for filtering and clustering a population, consistent with the disclosed embodiments; 
         FIG. 6  is a diagrammatic illustration of exemplary survey questions, consistent with the disclosed embodiments; and 
         FIG. 7  is a diagrammatic illustration of a consumer decision map used by the systems and methods described throughout for generating and delivering promotions to consumer devices, consistent with the disclosed embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     The following detailed description refers to the accompanying drawings. Wherever possible, the same reference numbers are used in the drawings and the following description to refer to the same or similar parts. While several illustrative embodiments are described herein, modifications, adaptations and other implementations are possible. For example, substitutions, additions, or modifications may be made to the components and steps illustrated in the drawings, and the illustrative methods described herein may be modified by substituting, reordering, removing, or adding steps to the disclosed methods. Accordingly, the following detailed description is not limited to the disclosed embodiments and examples, Instead, the proper scope of the invention is defined by the appended claims. 
       FIG. 1  is a diagrammatic illustration of prior art identifying an exemplary correlational organization of values by their rated importance scores, from “Universals in the content and structure of values: Theoretical advances and empirical tests in 20 countries,” by Shalom H. Schwartz (Advances in experimental social psychology 25.1 (1992), pp. 1-65). These values have been studied globally with the nature of the values, and their relative positioning has remained fairly stable across cultures, with value preferences varying more than the value structure. As is shown in  FIG. 1 , the organization of human values includes a structure  10  segmented according to the categories of security, power, achievement, hedonism, stimulation, self-direction, universalism, benevolence, conformity, and tradition. 
     Included within each category are corresponding values and fundamental motivational interests. For example, the category of security includes values and fundamental motivational interests including national security, social order, family security, clean, sense of belonging, reciprocation of flavors, and healthy. Each value and fundamental motivational interest operates to further describe human behavior, and informs human choice in a variety of contexts including but not limited to economic decision-making. For example, prudent economic decisions are necessary to maintain family security. While the availability of resources and market constraints may dictate the buying and selling within a market, rational and irrational exuberance according to these behavioral economic values may drive purchasing decisions. 
     Accordingly, marketing to these categorical values and fundamental motivational interests may lead to improved sales. Specifically, clustering or segmenting a population according to these prior art values or others, as further contemplated consistent with the following disclosed embodiments, may enhance vending. 
     However, problems exist with traditional methods of computerized marketing to consumers using these categories. For example, while being “helpful” is important to two different consumers, being “independent” may be much more important to the first of those two consumers. Thus, marketing a product that emphasizes togetherness and helpfulness may not be appealing to the first consumer, 
       FIG. 2  is a schematic block diagram illustrating an exemplary embodiment for segmenting a population, consistent with the disclosed embodiments. As illustrated in  FIG. 2 , a system  200  may include one or more client devices  220 , an online advertising system  230 , and a network  250 . 
     System  200  may include client devices  220 , such as smartphones, tablets, computers, or other computing devices, to access network  250 . Client devices  220  may include client computing devices such as, for example, desktop computers, notebook computers, mobile devices, tablets, smartphones, wearable devices such as smart watches, smart bracelets, smart glasses, or any other client devices that communicate with network  250 . Client devices  220  may also include software and executable programs including but not limited to promotional and advertising applications. Internet, HTML, cookie-based, adware, and other applications are contemplated. Client devices  220  may be operated by one or more consumers (also known as users or clients) to which promotions are targeted. 
     An online advertising system  230  of system  200  may include system storage, system applications, caches, and relevance and ranking modules. (Online advertising system  230  is an example of a type of computerized system which benefits from the technical aspects of the inventive concepts.) Online advertising system  230  may also include graphical and banner advertising applications, consumer clicking applications, pricing applications, and publishing applications. Online advertising system  230  may be configured to be operated by a network administrator controlling online advertising system  230 , remotely controlled, and/or autonomously controlled via network server  250 . Online advertising system  230  may include the components as further described and shown (with reference to  FIG. 4 ). 
     System  200  may allow for one or more client devices  220  to transfer information associated with answers to the one or more survey questions (with reference to  FIG. 6 ) over network  250  to a cloud platform  290  and/or online advertising system  230 . Value preference and value “gap” information may also be transferred. A value gap may include a distance or displacement between an optimal value status and a current value state, System  200  may include mobile or stationary (not shown) client devices  220  located in residential premises and non-residential premises configured to communicate with network  250 . Client devices  220  may connect to network  250  by Wi-Fi or wireless access points (WAP). Bluetooth® or similar wireless technology may be contemplated. Those skilled in the art will appreciate that network  250  may include a cellular network, a satellite network, the Internet, or a combination of these (or other) networks that are used to transport data. Furthermore, client devices  220  may connect to network  250  according to a wired network, such as an Ethernet network (not shown). Network  250  may provide, for example, authentication services that enable client devices  220  to access information, including stored survey questions, responses, and consumer data. This data and metadata may include, for example, demographic data, channel preference data, psychographic data including attitudinal and behavioral information, media usage data, and product experience (further described below with reference to  FIG. 4 ) corresponding to associated value data, consumer data, and advertisement data. 
     In exemplary system  200 , client devices  220  and online advertising system  230  may communicate with one or more servers in cloud platform  290  through network  250 , Cloud platform  290  may comprise one or more network servers  260 , third party servers  270 , and/or databases  280 . Servers  260  and  270  may provide cloud services for users and their client devices  220 . For example, a cloud-based microservice may be implemented comprising a distributed portion that executes at another location in network  250  and a corresponding cloud portion that executes on a network server  260  in cloud platform  290 . Servers in cloud platform  290  may also communicate with online advertising system  230  over network  250  using appropriate cloud-based communication protocols, such as SOAP or REST and/or other protocols that would be known to those skilled in the art. Such communication may allow for control of online advertising system  230  consistent with, for example, network server  260 . 
     As shown in  FIG. 2 , network  250  may be accessible to network servers  260 , third party servers  270 , and databases  280  in cloud platform  290 , for sending and receiving of information, such as value preference or gap information, consumer information, and advertising instructions, within system  200 . Network server  260 , third party server  270 , and database  280  may include networking, cloud, and/or backup services. For example, in some embodiments, network server  260  may include a cloud computing service such as Microsoft Azure™ or Amazon Web Services™. Additional cloud-based wireless access solutions compatible with the LTE standard (e.g., using the 3.5 GHz spectrum in the US) are contemplated. In some embodiments, third party server  270  may include a messaging or notification service, for example, that may notify a group member of updates or changes to a member&#39;s survey responses, and corresponding updates to a member&#39;s value preferences and gaps (further described with reference to  FIG. 6 ). 
       FIG. 3  is a schematic block diagram illustrating an exemplary embodiment of network server  260 , consistent with the disclosed embodiments. As shown in  FIG. 3 , network server  260  may include, among other things, a processor  320 , input/output (I/O) devices  330 , memory  340  for storing and/or accessing value, advertising, and consumer affiliated data and programs, and a database  360 , each coupled to one or more interconnected internal buses (not shown). The programs stored in memory may include, for example, server programs  344  and an operating system  346 . Server programs  344  may implement predictive learning advertising algorithms consistent with disclosed embodiments using a cloud-based microservice or, alternatively, a separate software program for execution in network server  260 . In alternative embodiments, software program  344  may be located in client devices  220  or in online advertising system  230  (as referenced in  FIG. 4 ), to implement the predictive learning advertising systems and methods according to captured value information, advertisement data, and consumer data. 
     Memory  340  and/or database  360  may store consumer data  356  based on individual and/or aggregate consumer behavior, such as data indicating consumers sending or modifying responses to survey questions to online advertising system  230 . The memory also may store other data and programs (not shown). Consumer data  356  may include consumer login and/or other identification (ID) or consumer credentials, group membership, authentication timestamp information, network node or access point location(s) and/or preferences, and other metadata generated by algorithms in the server programs  344 . Memory  340  and/or database  360  also may store advertisement data  354  and value information  352 , and may further store other software programs and data that are not shown. Advertisement data  354  may include a list of promotional graphics or banners advertised according to received consumer data  356  and value information  352 . Other advertisement data may be contemplated. Value information  352  may include saved and stored value preference and value gap information (further described below with reference to  FIG. 6 ). Group membership clustering or clustering outcomes may be determined based on value information  352  and associated consumer data  356 , and promotional advertising may be generated and delivered to a client device  220  based on the group segmentation and associated advertisement data  354 , as determined by one or more processors  320 . 
     Database  360  may include Microsoft SQL databases, SharePoint databases, Oracle™ databases, Sybase™ databases, or other relational databases. Database  360  may also be implemented as one or more other types of data stores, such as arrays, linked lists, text files, tables, or the like. Memory  340  and database  360  may be implemented using any volatile or non-volatile memory including, for example, magnetic, semiconductor, tape, optical, removable, non-removable, or any other types of storage devices or computer-readable mediums. 
     I/O interfaces  330  may include not only network interlace devices, but also user interface devices, such as one or more keyboards, mouse devices, and graphical user interfaces (GUIs) capable of use with client devices  220 . Network server  260  may receive consumer data  356 , advertisement data  354 , and value information  352  from network  250  through I/O device  330 , and analyze such data. Network server  260  may determine clustering outcomes based on the value information, identify advertising based on the clustering outcomes, and send instructions to online advertising system  230  across network  250  to deliver advertising over the Internet to a client device  220 . Network server  260  may store a copy of the customer data  356 , advertising data  354 , and value data  352 , clustering outcomes or group membership, and promotional segmentation it transmits to online advertising system  230  for advertising, for example, in memory  340 , database  360 , database  380 , or in any other database accessible to the server  260 , 
       FIG. 4  is a schematic block diagram illustrating an exemplary embodiment of online advertising system  230 , consistent with the disclosed embodiments. As illustrated in  FIG. 4 , online advertising system  230  may include a controller  460  capable of communicating with network  250 . Controller  460  may function in a manner similar to network server  260  and may operate independently or co-dependently with network server  260 . Online advertising system  230  may be capable of receiving value preferences or gaps (further described below with reference to  FIG. 6 ) from one or more client devices  220  and/or cloud platform  290  over network  250 . Controller  460  may receive consumer data  456 , advertisement data  454 , and value information  452 , and associated clustering outcomes and corresponding promotional segmentation, and may store them in memory  440  or database  462 . Controller  460  may include one or more processors  420 , input/output devices  430 , controller programs  444 , and operating system  446 . Controller  460  may be configured to receive information relating to consumer demographics  470  (e.g., information about consumers), communication channel preferences  472  (e.g., information about the types of communications that consumers prefer, such as email, text messaging, or phone calls), psychographics  474  (e.g., information about consumers&#39; attitudes, aspirations, and other criteria), media usage  476  (e.g., information about the types of media that consumers use, such as movies, music, books, or websites), and product experience  478  (e.g., information about consumers&#39; experience with particular products that are related to potential marketing opportunities). Psychographics  474  may include consumer attitudes  480  and behaviors  482 . Demographics  470  may include at least one of age, gender, socio-economic status, or demographics associated with each consumer member of a segmented group. 
     According to received demographics  470 , channel preferences  472 , psychographics  474 , media usage  476 , product experience  478 , attitudes  480 , and behaviors  482 , controller  460  may calculate value information  452  and cluster outcomes according to a vector analysis, and subsequently determine advertising for an online consumer. The vector analysis may include, for example, an LCA analysis (Latent Class Analysis) or a Mixture Analysis, by analyzing vectors representing the relative strengths of value preferences and value gaps. Selection of particular types of advertisements and advertisement content (not shown) may also be performed by controller  460 . Furthermore, controller  460  may receive modifications for one or more value preferences or gaps in real-time, and may alter online advertising consistent with the changes in value preferences and gaps. Other operations may also be performed by controller  460 . For example, controller  460  may identify segment membership related to users that are sent one or more surveys, and extrapolate from the information received in the surveys to estimate segment membership for an entire population (e.g., using information from a database containing information on consumers in that population). 
       FIG. 5  is a flow chart illustrating an exemplary process for filtering and clustering a population, consistent with the disclosed embodiments. While the exemplary method  500  is described herein as a series of steps, it is to be understood that the order of the steps may vary in other implementations. In particular, steps may be performed in any order, or in parallel. The one or more processors may include processors in network server  260  and/or processors in controller  460 . In other words, each step of process  500  may be performed by one or more of network server  260 , controller  460 , or another device. 
     At step  502 , process  500  may include filtering a population to identify a group. Population information may periodically be stored, accessed, modified, maintained, and received by client device  220  and/or network server  260 . Population information may be received and filtered all at once, periodically, or as determined by network server  260  or controller  460  over time. Population information may be filtered prior to being received by network  250 . Filtering the population may include filtering consumer members based on at least one of age, gender, socio-economic status, or demographics associated with each member. Other filtering criteria are contemplated to identify a group and consumer membership according to a particular group. 
     At step  504 , process  500  may include delivering an electronic survey to consumer members of the group. The survey may, for example, be delivered to each member over an electronic network via email, text message (e.g., SMS or MMS), mobile notification, pop-up advertisement, pop-under advertisement, interstitial advertisement, or the like. The survey may correspond to a particular category, value, or fundamental motivational interest. The survey may be transmitted or sent as the result of instructions provided by network server  260  according to consumer data  356 . For example, the instructions may indicate a survey should be transferred or exported from network server  260  and received or imported by client device  220  for use by a prospective consumer member. In some alternative embodiments, cloud platform  290  may transmit a survey automatically or periodically to client device  220 , without any request. A plurality of surveys may be received by client device  220  from cloud platform  290  or may be delivered in sequence according to a predefined schedule. Several surveys may be delivered at the same time and stored in database  280  as instructed by a third party server  270  or network server  260 . Other delivery methods may be used as well. Network server  260  may receive results (e.g., answers) of the survey over the same network the survey was sent using or over a different network. 
     The survey may be configured to determine value preferences and value gaps for each member of the group, and the survey may be analyzed according to a set of paired comparisons. This analysis may include a calculation or determination of a frequency of possible pairs of value preferences and gaps (as discussed below h reference to  FIG. 6 ). 
     At step  506 , process  500  may include generating a vector according to determined value preferences and value gaps. A packet of survey data including a set of paired survey comparisons may be received, and the relative strengths of value preferences and value comparisons based on the set of paired survey comparisons may be analyzed. Executable code may analyze the relative strength of value preferences and value gaps using a latent variable model to determine an interval nature and ranking of the value preferences and value gaps. Processors  320  may utilize a Bradley-Terry-Luce (BTL) model to analyze the relative strength of value preferences and value gaps. For example, processors  320  may implement the BTL model with an arbitrary center set at zero, in order to determine which of the preferences and gaps are more important or more valuable to the particular consumer. Other models may be used as well. 
     The determination of the value preferences and value gaps may be determined for some or all members of the surveyed group, and the vector may have a length equal to a number of measured values for each member. Each member may be represented by a binary vector with the length equal to the number of values being investigated. Other vector lengths not corresponding to a number of measured values are contemplated as well. 
     The generated vector may include a predetermined threshold to determine if a value preference and value gap are both positive, both negative, or if one is positive and one is negative. If the threshold indicates a positive preference and a positive gap, the value is considered important (or “preferred”) and an important high gap exists. If the threshold indicates a negative preference and negative gap, the value is not considered important (or “preferred”) and a low gap—or no gap—exists. The threshold may indicate any combination of importance or preference and high or low (no) gap according to positive or negative determinations. 
     The vector may, in some embodiments, include a value preference and a value gap. For example, the vector may be created with one slot for each of top level values, and accordingly, may be created with one slot for each of top level value preferences and value gaps. According to a predetermined threshold, a “1” may be placed in a slot if both the value preference and value gap for that value is positive, and a “0” otherwise. Algorithmic and programmatic methods according to server programs  344  for generating a binary vector are contemplated. Other methods for analyzing and generating the vector are contemplated. 
     At step  508 , process  500  may include clustering the group of consumers into consumer segments. If it is determined that both a vector value preference and value gap are positive, the vector may be clustered into a segment with other generated vectors having both a value preference and value gap that are positive in similar patterns. Similarly, if the generated vector may have a value preference and value gap that are negative, the vector may be clustered into a segment with other generated vectors having both a value preference and value gap that are negative in similar patterns. The clustering of the group of consumers may also be accomplished by latent class analysis or mixture analysis. Other clustering methods may be contemplated. The clustering may include clustering at least two members of the group into multiple segments by calculating at least one difference between each generated vector and at least one other generated vectors associated with other members of the group. The clustering may include clustering the group of consumers into multiple consumer segments according to at least one of demographics, channel preferences, insurance experience, media usage, attitudes, behaviors, value preferences, value gaps, and psychographics (e.g., based on information discussed above with respect to  FIG. 4 ). Various algorithmic and programmatic methods according to server programs  344  for determining cluster outcomes or segments based on identifying value information may be used. 
     At step  510 , process  500  may include creating a predictive model. The predictive model may estimate segment membership derived from at least one received survey. In some embodiments, the predictive model may be created in order to estimate segment membership of a second set of members of the group using the received results. This second set of members of the group may include, for example, one or more members that did not receive a survey. 
     At step  512 , process  500  may include generating a promotion for display corresponding to a clustered segment. Step  512  may include using the predictive model to determine which promotion to display to a particular member of the group. and at step  514 , process  500  may include delivering the promotion to a client device  220 . Network server  260  may generate a promotion for display to one or more client devices  220 . Promotions may be identified, generated, and/or selected by the controller  460  or network server  260  based on value information  352 . The one or more server programs  344  may interact with processor  320  and one or more controllers  460  to provide promotional content. The promotional content may include banner ads, animations, overlay video, audio, graphical displays, text, alerts, interactive user interfaces, or any type of advertisement as contemplated. The promotional content may also include any content corresponding to advertisement data  354  generated from the value-based clustered segmentation. The value-based clustered segmentation may be generated as a result of consumer data  356  including survey questions and survey responses and value data  352  including value preferences and value gaps vectors. Other promotional content may be contemplated. 
     Promotional content may be transmitted or sent as the result of instructions provided by network server  260  according to consumer data  356  or advertisement data  354 . For example, the instructions may indicate that a promotion should be transferred or exported from network server  260  and received or imported by client device  220  for viewing by a prospective consumer member. In some alternative embodiments, cloud platform  190  may transmit a promotion automatically or periodically to client device  220 , without any request. A plurality of promotions may be received by client device  220  from cloud platform  290  or may be delivered in sequence according to a predefined schedule. Several promotions corresponding to different value criteria may be delivered at the same time and stored in database  280  as instructed by a third party server  270  or network server  260 . Other delivery methods may be contemplated. 
       FIG. 6  is a diagrammatic illustration of exemplary survey questions, consistent with the disclosed embodiments. As illustrated in  FIG. 6 , survey questions  600  may include questions directed to determining a value preference  601 , questions directed to determining a value gap  603 , and questions directed to determining a modeling calibration  605 . Consistent with disclosed embodiments, survey questions may include any number of questions querying paired comparisons or any other numbers of options presented for a group member&#39;s selection. The survey questions may query any particular value including but not limited to a consumer&#39;s financial security and fiscal prudence, or any other value that may be contemplated as evaluated by use of an online advertising system  230 . For example, as illustrated in  FIG. 6 , a survey question ( 601 ) may include determining a consumer&#39;s value preference between having financial security or being a wise steward. Upon a member selection of being a wise steward, another survey question ( 603 ) may include determining a consumer&#39;s value gap evaluating the extent to which a consumer is a wise steward. Other survey questions and responses are contemplated. Determination of a member&#39;s value preference and value gap may be stored in vector form and used for clustering segmentation, as described generally above. 
     In some embodiments, contingent upon the responses to the questions relating to prior value preference ( 601 ) and value gap ( 603 ), a subsequent modeling calibration ( 605 ) may query a paired comparison of two company products. Selection of a particular modeling calibration response may indicate to system  200  a particular advertisement to deliver corresponding to a member&#39;s clustered segmentation. The answers to the value preference questions  601 , value gap questions  603 , and modeling calibration questions  605  may be stored on hard drives of one or more client devices  220 , and may also be stored as value information  352  in memory  340  or database  360  on cloud platform  290 . Value information may also be stored in memory  340  or database  462  of controller  460 . Survey response answers to questions  601 ,  603 ,  605  may be altered by a client device  220  in communication with online advertising system  230  or network server  260  to modify or change responses and/or to update response or value information. 
       FIG. 7  is a diagrammatic illustration of a consumer decision map used by the systems and methods described herein for generating and delivering promotions to consumer devices, consistent with the disclosed embodiments. In accordance with a selected answer to modeling calibration  605  (in  FIG. 6 ),  FIG. 7  illustrates a resulting link between values and specific company products (and corresponding promotions) that may be marketed to a consumer. As illustrated in  FIG. 7 , personal values and personal consequences in collaboration with attributes and functional consequences indicate decision making choices for a consumer, for example, battling a critical illness. 
     As shown in  FIG. 7 , the consumer decision map indicates that a consumer may be more likely to purchase a policy from Bob&#39;s Insurance or a policy from Sister&#39;s Whole Life Insurance LLC according to the particular primary and secondary decision points surrounding personal value preferences as selected in survey questions (e.g., as shown in  FIG. 6 ). For example, a consumer facing a critical illness may have a value preference of family love and security, and accordingly, have a value preference for being a good parent. As a value gap, a consumer facing a critical illness may desire to be a better provider for his or her family. Accordingly, as a functional consequence, the consumer may value spending more quality time with his or her family. The consumer decision map in  FIG. 7  indicates that such a consumer may be likely to purchase a policy from Sister&#39;s Whole Life Insurance LLC. Based on this, a promotion can be tailored according to that purchase and electronically delivered to a client device. 
       FIG. 7  illustrates multiple value preferences and gaps that may need to be considered by a consumer, suffering from a critical illness, who is considering purchasing insurance. Accordingly, by presenting tailored survey questions (as discussed above with reference to  FIG. 6 ) reflective of all the many value preferences and gaps, a tailored product can be identified for the consumer (as referenced in  FIG. 7 ). Accordingly, a clustered consumer segmentation can be created to deliver promotional material to consumers according to specific psychographic values on one or more of their client devices  220 . 
     While the present disclosure has been shown and described with reference to particular embodiments thereof, it will be understood that the present disclosure can be practiced, without modification, in other environments. The foregoing description has been presented for purposes of illustration. It is not exhaustive and is not limited to the precise forms or embodiments disclosed. Modifications and adaptations will be apparent to those skilled in the art from consideration of the specification and practice of the disclosed embodiments. Additionally, although aspects of the disclosed embodiments are described as being stored in memory, one skilled in the art will appreciate that these aspects can also be stored on other types of computer readable media, such as secondary storage devices, for example, hard disks or CD ROM, or other forms of RAM or ROM, USB media, DVD, Biu-ray, or other optical drive media. 
     Computer programs based on the written description and disclosed methods are within the skill of an experienced developer. Various programs or program modules can be created using any of the techniques known to one skilled in the art or can be designed in connection with existing software. For example, program sections or program modules can be designed in or by means of .Net Framework, .Net Compact Framework (and related languages, such as Visual Basic, C, etc.), Java, C++, Objective-C, HTML, HTML/AJAX combinations, XML, or HTML with included Java applets. 
     Moreover, while illustrative embodiments have been described herein, the scope of any and all embodiments having equivalent elements, modifications, omissions, combinations (e.g., of aspects across various embodiments), adaptations and/or alterations as would be appreciated by those skilled in the art based on the present disclosure. The limitations in the claims are to be interpreted broadly based on the language employed in the claims and not limited to examples described in the present specification or during the prosecution of the application. The examples are to be construed as non-exclusive. Furthermore, the steps of the disclosed methods may be modified in any manner, including by reordering steps and/or inserting or deleting steps. It is intended, therefore, that the specification and examples be considered as illustrative only, with a true scope and spirit being indicated by the following claims and their full scope of equivalents.