Patent Publication Number: US-2023162214-A1

Title: System and method for predicting impact on consumer spending using machine learning

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This patent application claims the priority to and benefit of U.S. Provisional Patent Application No. 63/279,863, filed on Nov. 16, 2021, the entirety of which is herein incorporated by reference. 
    
    
     FIELD 
     The present disclosure generally relates to the field of computer processing and machine learning. More specifically, the present disclosure relates to processing customer feedback to predict a financial impact using machine learning technology. 
     BACKGROUND 
     Customers may visit various stores, including both in physical brick stores and online stores, to purchase products and services. Some stores may experience a positive or negative financial impact when a customer encounters certain types of events or incidents. 
     Customer feedback may be collected as part of a store survey, either in person or online, after the customer has completed a shopping trip or experience. However, collected data is often unstructured and/or too voluminous to facilitate efficient generation of actionable insights. 
     SUMMARY 
     In accordance with an aspect, there is provided a computer-implemented system for computing economic impact of customer experiences. The system includes a communication interface, at least one processor, memory in communication with the at least one processor, and software code stored in the memory. The software code, when executed at the at least one processor causes the system to: maintain a data set including a plurality of types of negative customer experiences; maintain a tree model for predicting economic impact of one or more of the plurality of types of negative customer experiences; receive feedback data reflective of customer experiences; generate a decision tree based on the tree model, the data set and the feedback data, the decision tree having a plurality of internal nodes with each internal node corresponding to a type of the plurality of types of negative customer experiences; compute economic impact of at least one of the types of negative customer experiences using the generated decision tree and the feedback data; and cause to render, at a display screen, a graphic user interface visualizing the computed economic impact of at least one of the types of negative customer experiences. 
     In some embodiments, the tree model is a classification and regression tree (CART) model and the decision tree is a binary tree. 
     In some embodiments, the binary tree is generated using machine learning. 
     In some embodiments, each leaf of the decision tree comprises a class label indicating a classification of a type of negative customer experience corresponding to a given internal node of the decision tree. 
     In some embodiments, the class label comprises a real value between 0 and 1, and a value equal to or greater than 0.5 indicates that the associated type of negative customer experience has a meaningful economical impact. 
     In some embodiments, generating the binary tree may include: splitting the data set comprising the plurality of types of negative customer experiences into two groups based on a first cost function; and for each of the two groups: splitting the types of negative customer experiences in each respective group into two subsets based on a second cost function; and iteratively splitting the types of negative customer experiences in each respective subset into further binary subsets using a recursive binary splitting procedure until a predetermined threshold is reached. 
     In some embodiments, the predetermined threshold is a count on a total number of training instances assigned to each internal node of the binary tree. 
     In some embodiments, splitting the types of negative customer experiences includes selecting one type from the types of negative customer experiences and setting the selected type as an internal node. 
     In some embodiments, the feedback data may include a loyalty status, and the economic impact is computed based on said loyalty status. 
     In some embodiments, the software code, when executed at said at least one processor, causes said system to compute the economic impact of at least one of the types of negative customer experiences by: computing, for the at least one type of negative customer experience, a frequency of occurrence among a plurality of customers based on the feedback data; computing, for the at least one type of negative customer experience, a financial impact on the plurality of customers based on the feedback data; and determining the economic impact of for the at least one type of negative customer experience by multiplying the frequency of occurrence by the financial impact. 
     In some embodiments, computing the financial impact for the at least one type of negative customer experience on the plurality of customers based on the feedback data may include: determining, based on the feedback data, a first average amount of spending among a first group of customers that did not experience the at least one type of negative customer experience, the first group of customers from the plurality of customers; determining, based on the feedback data, a second average amount of spending among a second group of customers that experienced the at least one type of negative customer experience, the second group of customers from the plurality of customers; and computing the financial impact based on a difference between the first average amount of spending and the second average amount of spending. 
     In accordance with another aspect, there is provided a computer-implemented method for computing economic impact of customer experiences. The method may include: maintaining a data set including a plurality of types of negative customer experiences; maintaining a tree model for predicting economic impact of one or more of the plurality of types of negative customer experiences; receiving feedback data reflective of customer experiences; generating a decision tree based on the tree model, the data set and the feedback data, the decision tree having a plurality of internal nodes with each internal node corresponding to a type of the plurality of types of negative customer experiences; computing economic impact of at least one of the types of negative customer experiences using the generated decision tree and the feedback data; and causing to render, at a display screen, a graphic user interface visualizing the computed economic impact of at least one of the types of negative customer experiences. 
     In some embodiments, the tree model is a classification and regression tree (CART) model and the decision tree is a binary tree. 
     In some embodiments, the binary tree is generated using machine learning. 
     In some embodiments, each leaf of the decision tree comprises a class label indicating a classification of a type of negative customer experience corresponding to a given internal node of the decision tree. 
     In some embodiments, the class label comprises a real value between 0 and 1, and a value equal to or greater than 0.5 indicates that the associated type of negative customer experience has a meaningful economical impact. 
     In some embodiments, generating the binary tree may include: splitting the data set comprising the plurality of types of negative customer experiences into two groups based on a first cost function; and for each of the two groups: splitting the types of negative customer experiences in each respective group into two subsets based on a second cost function; and iteratively splitting the types of negative customer experiences in each respective subset into further binary subsets using a recursive binary splitting procedure until a predetermined threshold is reached. 
     In some embodiments, the predetermined threshold is a count on a total number of training instances assigned to each internal node of the binary tree. 
     In some embodiments, splitting the types of negative customer experiences includes selecting one type from the types of negative customer experiences and setting the selected type as an internal node. 
     In some embodiments, computing the economic impact of at least one of the types of negative customer experiences may include: computing, for the at least one type of negative customer experience, a frequency of occurrence among a plurality of customers based on the feedback data; computing, for the at least one type of negative customer experience, a financial impact on the plurality of customers based on the feedback data; and determining the economic impact of for the at least one type of negative customer experience by multiplying the frequency of occurrence by the financial impact. 
     In some embodiments, computing the financial impact for the at least one type of negative customer experience on the plurality of customers based on the feedback data may include: determining, based on the feedback data, a first average amount of spending among a first group of customers that did not experience the at least one type of negative customer experience, the first group of customers from the plurality of customers; determining, based on the feedback data, a second average amount of spending among a second group of customers that experienced the at least one type of negative customer experience, the second group of customers from the plurality of customers; and computing the financial impact based on a difference between the first average amount of spending and the second average amount of spending. 
     In accordance with yet another aspect, there is provided a non-transitory computer-readable storage medium storing instructions. The instructions, when executed, adapt at least one computing device to: maintain a data set including a plurality of types of negative customer experiences; maintain a tree model for predicting economic impact of one or more of the plurality of types of negative customer experiences; receive feedback data reflective of customer experiences; generate a decision tree based on the tree model, the data set and the feedback data, the decision tree having a plurality of internal nodes with each internal node corresponding to a type of the plurality of types of negative customer experiences; compute economic impact of at least one of the types of negative customer experiences using the generated decision tree and the feedback data; and cause to render, at a display screen, a graphic user interface visualizing the computed economic impact of at least one of the types of negative customer experiences. 
     In some embodiments, the tree model is a classification and regression tree (CART) model and the decision tree is a binary tree. 
     In some embodiments, the binary tree is generated using machine learning. 
     In some embodiments, each leaf of the decision tree comprises a class label indicating a classification of a type of negative customer experience corresponding to a given internal node of the decision tree. 
     In some embodiments, the class label comprises a real value between 0 and 1, and a value equal to or greater than 0.5 indicates that the associated type of negative customer experience has a meaningful economical impact. 
     In some embodiments, generating the binary tree may include: splitting the data set comprising the plurality of types of negative customer experiences into two groups based on a first cost function; and for each of the two groups: splitting the types of negative customer experiences in each respective group into two subsets based on a second cost function; and iteratively splitting the types of negative customer experiences in each respective subset into further binary subsets using a recursive binary splitting procedure until a predetermined threshold is reached. 
     In some embodiments, the predetermined threshold is a count on a total number of training instances assigned to each internal node of the binary tree. 
     In some embodiments, splitting the types of negative customer experiences includes selecting one type from the types of negative customer experiences and setting the selected type as an internal node. 
     In some embodiments, the instructions, when executed, adapt at least one computing device to: compute, for the at least one type of negative customer experience, a frequency of occurrence among a plurality of customers based on the feedback data; compute, for the at least one type of negative customer experience, a financial impact on the plurality of customers based on the feedback data; and determine the economic impact of for the at least one type of negative customer experience by multiplying the frequency of occurrence by the financial impact. 
     In some embodiments, computing the financial impact for the at least one type of negative customer experience on the plurality of customers based on the feedback data may include: determining, based on the feedback data, a first average amount of spending among a first group of customers that did not experience the at least one type of negative customer experience, the first group of customers from the plurality of customers; determining, based on the feedback data, a second average amount of spending among a second group of customers that experienced the at least one type of negative customer experience, the second group of customers from the plurality of customers; and computing the financial impact based on a difference between the first average amount of spending and the second average amount of spending. 
     In various further aspects, the disclosure provides corresponding systems and devices, and logic structures such as machine-executable coded instruction sets for implementing such systems, devices, and methods. 
     In this respect, before explaining at least one embodiment in detail, it is to be understood that the embodiments are not limited in application to the details of construction and to the arrangements of the components set forth in the following description or illustrated in the drawings. Also, it is to be understood that the phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting. 
     Many further features and combinations thereof concerning embodiments described herein will appear to those skilled in the art following a reading of the instant disclosure. 
    
    
     
       BRIEF DESCRIPTION OF THE FIGURES 
       In the Figures, which illustrate example embodiments, 
         FIG.  1    is a schematic diagram of a computer-implemented system for computing economic impact of customer experiences, in accordance with an embodiment. 
         FIG.  2    is an example flowchart for computing economic impact of customer experiences, in accordance with an embodiment. 
         FIG.  3    shows an example list of potential negative experiences customers can encounter at a physical store, in accordance with an embodiment. 
         FIG.  4    is an example graphical user interface (GUI) displaying an average revenue at risk per customer, in accordance with an embodiment. 
         FIG.  5    is an example graphical user interface (GUI) displaying an average annual revenue at risk per customer and a list of locations with the highest risk or lowest risk, in accordance with an embodiment. 
         FIG.  6    is an example graphical user interface (GUI) displaying an average revenue at risk per customer grouped by demographic and household income, in accordance with an embodiment. 
         FIG.  7    shows an example process for computing economic impact of customer experiences performed by the system in  FIG.  1   , in accordance with an embodiment. 
         FIGS.  8 A to  8 G  each show aspects of an example survey that can be presented to customers, in accordance with an embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     The present disclosure provides a computational system and method for isolating and quantifying the financial impact of sub-optimal or negative customer experiences. The system may be configured to receive a large volume and variety of data elements including customer experiences and customer spending data, and through an automated (e.g., via machine learning) process of measurement and analysis, generates actionable insights defining the relationship between a company&#39;s financial performance and a the customer feedback. The system may also automatically identify issues with the largest detrimental impact on customer experience. The system may also aggregate the findings and present one or more GUI elements to efficiently and intelligently display the customer issues affecting a financial performance of a company, with convenient and data-efficient indications as to the economical impact of each relevant customer issue. 
       FIG.  1    is a high-level schematic diagram of a computer-implemented system  100  for computing economic impact of customer experiences, exemplary of embodiments. The system  100  has data storage  120  including a memory  108  and a persistent storage  124 . The memory  108  and/or the persistent storage  124  may store one or more databases  122 . The databases  122  may store one or more data sets including a plurality of types of negative customer experiences. The data sets include, in some embodiments, a portfolio of hundreds (or more) of experiences for customers, who encounter these experiences in the course of their relationship with the company, e.g., at their physical store locations. 
     The system  100  can also include an I/O unit  102 , a processor  104 , and a communication interface  106 . The I/O unit  102  can enable the system  100  to interconnect with one or more input devices, such as a keyboard, mouse, camera, touch screen and a microphone, and/or with one or more output devices such as a display screen and a speaker. 
     The processor  104  is configured to execute machine-executable instructions to implement the processes disclosed herein such as, for example, generate a decision tree based at least on the data set stored in  122  in order to compute economic impact of customer experiences. Further, the processor  104  can execute instructions in memory  108  to implement aspects of processes described herein. Further, the processor  104  can execute instructions in memory  108  to configure a tree model  110 , one or more generated binary trees  112 , an interface application  114  which can provide control commands to display various GUI elements at display devices  130 , a training engine  116  for generating the binary tree  112 , and other functions described herein. The processor  104  can be, for example, any type of general-purpose microprocessor or microcontroller, a digital signal processing (DSP) processor, an integrated circuit, a field programmable gate array (FPGA), a reconfigurable processor, or any combination thereof. 
     The persistent storage  124  may be configured to store information associated with or created by the components in memory  108  and may also include machine executable instructions. The persistent storage  124  which may include various types of storage technologies, such as solid state drives, hard disk drives, flash memory, and may be stored in various formats, such as relational databases, non-relational databases, flat files, spreadsheets, extended markup files, etc. 
     Memory  108  may include a suitable combination of any type of computer memory that is located either internally or externally such as, for example, random-access memory (RAM), read-only memory (ROM), compact disc read-only memory (CDROM), electro-optical memory, magneto-optical memory, erasable programmable read-only memory (EPROM), and electrically-erasable programmable read-only memory (EEPROM), Ferroelectric RAM (FRAM) or the like. 
     The memory  108  may include an interface application  114  to process the input data from the databases  122 . In some embodiments, the interface application  114  can normalize input data from the databases  122  to generating decision tree(s)  112  in manners disclosed herein 
     The memory  108  can include a tree model  110 , which may include a binary tree model, for example. The tree model  110  may include a classification and regression tree (CART) model. The tree model  110  may be used to generate the decision tree for computing the economic impact of customer experiences based on feedback data  150  received via network  140 .  FIGS.  8 A to  8 G  show an example survey that can be given to by one or more customers for completion. For example, the survey can be electronically presented to the one or more customers at their display devices  130 . The answers from the survey completed by the customers may be processed and stored as the feedback data  150 , which may be used to generate the decision tree  112 . 
     The decision tree  112  may include a plurality of internal nodes with each internal node corresponding to a type of the plurality of types of negative customer experiences. An internal node may refer to a node that has child node(s). 
     In some embodiments, each leaf (or leaf node) of the decision tree may include a class label indicating a classification of a type of negative customer experience corresponding to a given internal node of the decision tree, which may be the parent node of the leaf. For example, the class label may has a real value between 0 and 1, where a value equal to or greater than 0.5 indicates that the associated type of negative customer experience has a meaningful economical impact. A meaningful economical impact may indicate that the type of negative customer experience has resulted in a economical loss above a certain threshold during a period, e.g., $1,000 per week. 
     In some embodiments, generating the binary tree  112  includes using a machine learning system for predictive modeling. An example decision tree algorithm implements one or more classification and regression trees (CART). Using a CART algorithm, the training engine  116  may be configured to generate binary tree by selecting input variables and split points on those variables until a suitable tree is constructed. The selection of which input variable to use and the specific split can be implemented using a greedy algorithm to minimize a cost function. Typically, construction of the binary tree ends based on a predefined stopping criterion, such as a minimum number of training instances assigned to each leaf node of the tree. 
     In some embodiments, the binary tree  112  may be generated by: splitting a data set representing a plurality of types of negative customer experiences into two groups based on a first cost function; and for each of the two groups: splitting the types of negative customer experiences in each respective group into two subsets based on a second cost function; and iteratively splitting the types of negative customer experiences in each respective subset into further binary subsets using a recursive binary splitting procedure until a predetermined threshold is reached. 
     In some embodiments, the predetermined threshold is a count on a total number of training instances assigned to each internal node of the binary tree. 
     In some embodiments, splitting the types of negative customer experiences includes selecting one type from the types of negative customer experiences and setting the selected type as an internal node. 
     In some embodiments, an elastic net regression model is conducted on feedback data  150  from customers who have shopped at one or more stores, which may be physical stores or online stores, and who have reported spending less than a certain amount (e.g., $2500) during a time period (e.g., last month) on a type of products or services (e.g., groceries). This elastic net regression model may eliminate the distorting effects of outliers and isolate the impact on a customer&#39;s share of wallet when they experience a problem. An elastic net regression model selects only the subset of problems that are determined to have a meaningful impact on the share of wallet, while filtering out other problems that co-occur with the subset of problems, but on their own do not have a meaningful impact on the share of wallet. Having a meaningful impact on a customer&#39;s share of wallet generally means that the issue or problem is determined to have caused the customer (or a group of customers) to spend less at a particular store. 
     If a customer&#39;s share of wallet is reported to increase when he or she has reported experiencing a problem based on the feedback data  150 , the financial risk, or economical impact, of that problem is set to 0. Such statistical artifacts can occur when said problem is very infrequently experienced and when there is a co-occurrence of multiple related problems. These are referred to as true artifacts, where the presence of the problem is not associated with a decrease in the customer&#39;s share of wallet. When the economical impact on a customer&#39;s share of wallet caused by a particular problem is determined, the reported monthly spending of the customer can be used to calculate the economical impact of the particular problem for the retailer. 
     The system  100  can receive the feedback data  150  from different data sources, e.g., different servers via a network  140 . Network  140  (or multiple networks) is capable of carrying data and can involve wired connections, wireless connections, or a combination thereof. Network  140  may involve different network communication technologies, standards and protocols, for example. 
     In some embodiments, the feedback data  150  may include a data set representing a loyalty status of a customer. For example, the feedback data  150  may include a data set indicating if a customer is a member of a loyalty program of the company, and if so, the associated level of loyalty status. For example, if a loyalty program of the company has three different levels, a value of 10 may indicate the highest member tier (e.g., “diamond member”), a value of 6 may indicate the second highest level of member tier (e.g., “gold member”), a value of 3 may indicate the third highest level of member tier (e.g., “silver member”), while a level of 0 may indicate a customer who is not a member. 
     In some embodiments, the feedback  150  may include a data item or set representing a loyalty value of a customer. For example, the loyalty value may be generated based on one or more types of behavioral data of the customer. The behavioral data may include, for instance, historical transactions, contact behaviors, and/or interaction with the company through physical or digital means, such as participation in a formal loyalty program, signing for e-mail communication, leaving a positive or negative review of the company, participation in a webinar or promotional event, and so on. 
     In some embodiments, loyalty value may be generated based on self-reported data of the customer, which may include, for example, the customer&#39;s response(s) to surveys or questions. The response(s) may indicate the customer&#39;s willingness to recommend the company, an intent to re-purchase goods or services from the company, and/or an intent to re-visit the company, either online or in a physical store. 
     The communication interface  106  can enable the system  100  to communicate with other components, to exchange data with other components, to access and connect to network resources, to serve applications, and perform other computing applications by connecting to a network (or multiple networks) capable of carrying data including the Internet, Ethernet, plain old telephone service (POTS) line, public switch telephone network (PSTN), integrated services digital network (ISDN), digital subscriber line (DSL), coaxial cable, fiber optics, satellite, mobile, wireless (e.g. Wi-Fi, WiMAX), SS7 signaling network, fixed line, local area network, wide area network, and others, including any combination of these. 
     The system  100  can be operable to register and authenticate users (using a login, unique identifier, and password for example) prior to providing access to applications, a local network, network resources, other networks and network security devices. The system  100  may serve multiple users which may operate display devices  130 . 
     The interface application  114  interacts with the display devices  130  to exchange data (including transmission of control commands) and generates visual elements for display at user devices. The visual elements can represent output generated by the system  100 , such as shown in  FIGS.  4 ,  5  and  6   . 
       FIG.  2    is an example flowchart  200  of an example method for computing economic impact of customer experiences, exemplary of embodiments. The method depicted in flowchart  200  processes a data set including a portfolio of hundreds (or more) of experiences  220  for customers  210 , who encounter these experiences in the course of their relationship with a company  230 , at their physical store locations, for example. 
     These experiences  220  include positive experiences  220   a , neutral experiences and negative experiences  220   b  for customers, the categorization of which can vary for each customer  210 . Any individual customer&#39;s evaluation of an experience encountered at a given company  230  (which may operate a physical or online store) can be influenced by competitive context (e.g., can I have a better experience with a company&#39;s competitor?) and sometimes by non-competitive context. 
     Competitors  240   a  are companies other than the company  230  which occupy the same core marketspace as the company  230 , and who compete for the same customers and addressable market. The competitors  240   a  can be an explicit data entity in the system  100 , where economic implications of the performance of the company  230  are evaluated in comparison to the one or more competitors  240   a . The competitors  240   a  can include single or multiple companies or brands, and also can be specific (e.g., a specific company) or general (represented collectively as a generic market alternative to the company  230 ). 
     Non-competitors  240   b  are companies other than the company  230  who operate in a marketspace different from the company  230 , but who provide similar, which may be superior, experiences such that customers perceive them as a credible point of comparison. Non-competitors  240   b  can be an explicit data entity in the system  100 , where the company  230  wishes to benchmark their performance against the broadest market standards available. 
     The system  100  includes models that focus on the economic impact of negative experiences  220   b  (i.e., problems), which tend to have a greater and more sustained impact on customer economic value than do neutral and positive experiences  220   a . When a customer  210  experiences one or more problems with a company  230 , those problems may reduce customer&#39;s economic value  260 , which can be determined and represented through analysis of professed loyalty behaviors and attitudes (e.g., self-reported survey data), or directly assessed through analysis of expressed loyalty behaviors (e.g., actual customer transaction and service data.) 
     When customers  210  encounter problems  220   b , they will either reach out to the company for assistance/resolution  250 , or otherwise. When a customer  210  encounters a problem  220   b  and does not seek problem resolution  250 , their economic value nearly always declines. 
     When customers  210  encounter problems  220   b  and do seek problem resolution  250 , the economic value depends on the quality of the problem resolution interaction. When the problem resolution  250  is effective, economic value may be recovered, sometimes in excess of the original value. When the problem resolution  250  is not effective, economic value decline may persist, and sometimes progress beyond the original decline. 
     Customers  210  are individuals consuming company products and services. Customer attributes are extensive, and each analysis carried out by the system  100  may be configured to address those attributes most critical to accuracy and utility of the analysis in question. A partial list of typical customer attributes for an example analysis carried out by the system  100  may include:
         Spending level (e.g., $600 a month on groceries)   Product or service penetration   Tenure   Role (if a business-to-business “B2B” study)   Region/location   Demographics   Size of company they represent (if a B2B study)   Recent date of interaction   Channel   Psychographic segmentation (for clients with a pre-establish segmentation schema)   Company/Competitor (for competitive studies)   Whether the customer is problem-free or problem-afflicted. This attribute value may be a result of analysis carried out by the system  100 .   Whether a customer is a contactor or non-contactor when problem-afflicted.   Loyalty and equity levels. This attribute value may be a result of analysis carried out by the system  100 .       

     Analysis carried out by the system  100  can quantify the overall economical impact of problem experiences on customer economic value, or by any sub-segment of customers described by one or more of the above attributes. The functionality to isolate the customer classes most prone to economic value loss from problems is a core driver of the utility and actionability of insights generated by the system  100 . 
     Experiences  220  are interactions and events between a company  230  and its customers  210 . Experiences  220  can be things that have happened (presence experiences) or things that have not happened (absence experiences). Experiences  220  can be positive, neutral or negative, the determination of which can depend on the customer interpretation of the experience  220 , and which may vary by customer. 
     In some embodiments, analysis carried out by the system  100  may be carried out based on a loyalty status of one or more customers. For example, experiences  220  can include a data set representing if a customer is a member of a loyalty program of company  230 . The data set can further include, if and when the customer is a member of a loyalty program of company  230 , a specific level of the loyalty status of the customer within the loyalty program. The higher the level of the loyalty status, the more likely the customer&#39;s economic value is higher for company  230 . In addition, the analysis carried out by the system  100  may include other, different proxies for computing an economic impact of one or more customers. 
     In some embodiments, analysis carried out by the system  100  may be carried out based on one or more types of behavioral data of the customer. For example, experiences  220  can include customer behavioral data such as historical transactions, contact behaviors, and/or interaction with the company through physical or digital means, including, for example, participation in a formal loyalty program, signing for e-mail communication, leaving a positive or negative review of the company, participation in a webinar or promotional event, and so on. 
     In some embodiments, experiences  220  can include self-reported data of the customer, which may include, for example, the customer&#39;s response(s) to surveys or questions. The response(s) may indicate the customer&#39;s willingness to recommend the company, an intent to re-purchase goods or services from the company, and/or an intent to re-visit the company, either online or in a physical store. 
     Analysis carried out by the system  100  generally focuses on negative experiences  220   b  or problems, where the economic impact is typically greatest. Problem attributes can include:
         Issue category (e.g. purchase process, account setup, training, product performance, customer service, etc.)   Customer role (different roles create different experiences, leading to role-distinct problems)   Product (different products impose different problems on customers)   Channel (different channels impose different problems on customers)   Relevance, or whether a given experience matters to economic performance in context of the relevance all other customer problems. This may be determined or derived by the system  100 .   Frequency. This attribute value may be a result of analysis carried out by the system  100 .   Absolute impact on economic value. This attribute value may be a result of analysis carried out by the system  100 .   Word of mouth dissemination. This attribute value may be a result of analysis carried out by the system  100 .       

     Problem resolution  250  can be defined as the act of a customer  210  proactively reaching out to a company  230  for assistance in addressing an issue, and the company  230  interacting with the customer  210  to provide assistance. Efficacy of problem resolution  250  is a critical factor in the recovery and preservation of customer economic value after the occurrence of a problem. Problem resolution attributes can include:
         Agency. What roles within the company  230  work with the customer  210  to address the issue?   Channel. What channels does the customer  210  use to address the issue?   Effort. How much effort does the customer  210  need to exert to address the issue?   Time. How long does it take for the issue to be addressed?   Overall Efficacy. How satisfied is the customer  210  with problem resolution performance overall?   Agent Dimensional Efficacy. How satisfied is the customer  210  with specific performance attributes of the resolving agent, including Concern, Urgency, Follow Through, Follow Up, Knowledge and Authority.       

     Economical value markers  260  are indicators of customer equity and loyalty that directly link to customer economic value. These are expressed by the customer either through a survey response (“professed”) or actual consumption behavior (“expressed”). Analysis carried out by the system  100  can accommodate a wide range of customer economic value markers  260  as a “dependent variable”, i.e. the aspect of customer value the analysis seeks to explain through problem experience quantification. These economic value markers  260  are selected to support the analytic objectives of a given analysis to be carried out by the system  100 , tailored to the circumstances of a particular company and the availability of internal customer data. 
     In general, economic value markers  260  may fall into one of three sub-categories:
         Professed Loyalty Behaviors. These are customer self-reported behaviors (e.g., sourced from survey responses) that indicate future economic behavior. These behaviors can include likelihood to recommend, future spend intent (more/same/less), and trial likelihood. In the absence of adequate customer-level transaction or service data, these markers can be associated to general economic performance of the customer base to impute economic impact.   Professed Loyalty Attitudes. These are customer self-reported attitudes that may precede and shape professed and expressed loyalty behaviors. These attitudes can include trust in company, trust in account representation, ease of doing business with the company, and belief that working with the company demonstrably improves the customer&#39;s business (in B2B environments.) These markers create explanatory context for both professed and expressed loyalty behaviors.   Expressed Loyalty Behaviors. When customer-level transaction and service data is available, the system  100  can utilize that data to directly assess the impact of problem experience and problem resolution on actual (vs. imputed) customer economic value.   This attributes of this data can be extensive, and may include:
           Total spend   Spend velocity   Share-of-spend   Customer LTV   Product-level spend   Service Costs   
               

     Quantitative Measurement 
     Conducting an analysis via the system  100  may require a direct interrogation of customer experiences, through presentation of a survey (see for example  FIGS.  8 A to  8 G ). The measurement process by which the system  100  examines and documents customer experiences is unique and specifically designed to generate efficient, accurate and comprehensive quantification of problem experiences on customer economic value. 
     Traditional customer surveys often ask respondents whether they had any problem. When the answer is “yes” the survey will then request verbatim detail on what the problem was, and may ask the respondent to assess the impact of the problem on their loyalty attitudes or behaviors. This traditional process typically yields inaccurate data and leads to inaccurate analysis in the following ways:
         It materially underestimates the frequency of problems occurring in a customer set;   It fails to create a comprehensive and accurate mapping of the majority of problems occurring in a customer set;   It relies on customer “top-of-mind” problem recall, which generally skews towards the most recent problem instead of the problem most damaging to customer economic behaviors;   It relies on customer self-evaluation of whether a problem matters or not to their economic behaviors, which introduces significant cognitive bias into the data set;   It produces unstructured data (in the form of verbatim) for analysis. Such unstructured data is challenging to accurately assess for economic impact, and requires material effort to classify and prepare for analysis. The external data manipulation necessary to work with unstructured data is generally time-consuming and resource-intensive, and creates additional opportunities for bias and error to enter the data set.       

     In some embodiments, the measurement approach undertaken by the system  100  disclosed herein resolves the shortcomings of traditional problem assessment methods by: presenting an a priori inventory of problem; conducting binary problem assessment; and detaching problem occurrence from problem importance. Further, the resulting data may be structured data. 
     In some embodiment, the system  100  presents an a priori inventory of problems. Instead of asking a customer to recollect any problem and then describe it, the system  100  can provide a highly-curated list of potential problem experiences  220   b  for the respondent to consider and select from, where the selection indicating that the problem occurred for the customer within a stated time frame. This list is developed through a rigorous qualitative assessment of the customer experiences that precedes the quantitative survey, and curated by the system  100  and a company  230  together using frequency/source analysis. The merits of this approach over open-inquiry methods may include:
         A more accurate representation of total problem frequency in the customer base;   A more accurate representation of individual problem frequency in the customer base;   Lower cognitive effort for the respondent, enabling faster problem evaluation and limiting survey fatigue (which raises both abandonment rates and response error);   Creation of a structured vs. unstructured data set, with classification of problems pre-established for analytic purposes.       

     In some embodiments, the system  100  performs binary problem assessment. When presenting a problem inventory to a respondent, the system  100  only asks for the respondent to indicate whether the problem did or did not happen. The merits of this approach may include:
         Elimination of “scale bias” error from responses;   A more accurate representation of problem frequency in the customer base;   Lower cognitive effort for the respondent;   Creation of a highly structured data set with only two primary states (yes/no) for each problem.       

     In some embodiments, the system  100  detaches problem occurrence from problem importance. In such embodiments, the system  100  does not rely on respondent assessment of problems (beyond presence/absence) to evaluate problem economic impact. Stated differently, the system  100  does not ask customer to “score” or rank problems according to degree of pain or inconvenience arising from the problem. Instead, the system  100  utilizes the “presence/absence” scores of each problem across all customer respondents as a sample set for CART and tabular analysis. This analysis isolates the problems that matter the most to customer economic value, and by how much. It also determines the relationship between each problem experience and the dependent variable (“DV” or variables) selected to represent customer economic value (which may vary among different analysis). The merits of this approach may include:
         Replacement of self-evaluated problem-importance data with derived problem-importance data, improving statistical reliability and lowering error and bias in the data set;   Elimination of the “top-of-mind” effect, lowering error and bias in the data set;   Lower cognitive effort for the respondent.       

     In some embodiments, the system  100  is configured to perform an analysis that determines the statistical relationship between the occurrence of specific customer problem experiences for a given company and that company&#39;s market performance as measured by revenue and market share. It does so by isolating which problems matter to customer economic value, by how much, and by determining the relationship between each problem experience and the dependent variable(s) selected to represent customer economic value. The specificity of this mapping is a key component of actionability for the system  100 . 
     In some embodiments, the process carried out by the system  100  is multi-staged, and may include, in some embodiments: 1) problem selection analysis, 2) c, and 3) problem resolution analysis, as further described in detail below. For example, problem selection analysis is performed to determine which problems matter to customer economic value. In some embodiments, the system  100  can be configured to carry out a CART decision tree analysis to determine, out of all the problem statements presented in a quantitative survey (generally between 60 and 80 individual statements, see e.g.,  FIGS.  8 A to  8 G ), which problems are the most influential or impactful problems on customer economic value. The CART decision tree analysis may include the following steps. 
     In the first step, a dependent variable (DV) can be selected to represent customer economic value. This DV can be either categorical (e.g. “Promoter/Detractor” or “Highly Likely to Trial/Not Likely to Trial) or continuous (e.g. customer revenue, number of products purchased, number of store visits, and so on). In the second step, a data set is created that associates the presence or absence of every problem interrogated in the survey against the DV in question, for all survey respondents. 
     In the third step, the presence or absence of a specific problem for a specific respondent is assessed against the associated DV value of that respondent. This establishes a “relationship data point” (RDP) between that particular instance of the problem and the DV value. This RDP is then compared to a second RDP created in the same way, using a different problem-DV paring. 
     The analysis continues to evaluate problem-DV parings in all relevant combinations, both within and across customers, cycling through millions of different pairings. These iterative pairings, and the underlying algorithms that interpret them, create a tree diagram in which each internal node in the tree represents a specific problem from the list or portfolio of problems. Respondents can either “have” or “not have” the problem, and the tree “branches” accordingly, partitioning the full data set into successively smaller groups. 
     Next, the CART analysis generates a binary tree that identifies and orders the problems for each node that best maximizes the homogeneity of the resulting groups at the end of each branch (e.g., leaf nodes), those groups being respondents exhibiting superior or inferior economic value as represented by the DV. 
     The end result of problem selection analysis is a list of problems that most accurately predict whether a customer will exhibit economic value decline. These problems are referred to as the “Most Damaging Problems” (MDPs) of the analysis. With this list of MDPs, the problem selection analysis can be carried out by the system  100 , which quantifies the relative impact of each problem on the DV in question. 
     Problem impact analysis is carried out to determine how damaging each MDP is to customer economic value. The system  100  can be configured to calculate the total damage impact of each MDP to customer by assessing two distinct dimensions of damage for each MDP: MDP frequency and absolute impact of the MDP. 
     The more frequently an MDP occurs within the customer base, the more opportunity the MDP has to damage customer equity and reduce customer economic value. Frequency can be directly calculated based on survey responses as the number of customers reporting having had the problem divided by the total number of customers surveyed. For example, out of 650 customers surveyed, 228 reported experiencing “MDP X”, therefore MDP X has a frequency of 35%. 
     The second dimension is the absolute impact of the MDP. This is a measure of the damage to customer economic value when a customer experiences the problem, compared to the economic value of a customer who does not experience the problem. The general process for calculating absolute problem impact of a specific MDP may include:
         1. Dividing the total sample set of customers in the survey into two classes: those who experienced MDP X (“YES” group), and those who did not experienced MDP X (“NO” group). Note that “MDP X” can represent any MDP identified in the problem selection analysis step.   2. Dividing each YES and NO customer class into discrete sub-groups that represent their relative economic value, as proxied by the DV used for the analysis. Categorical DVs that assign customers into a priori value tiers establish these sub-groups directly. For example: Promoters vs. Passives vs. Detractors (in the NPS measurement framework), or customers Highly Likely to Trial vs. customers Not Likely to Trial. Continuous DVs, such as spend or number of products purchased, may require threshold determination to classify customers into discrete value tiers.   3. Once each YES and NO class is divided into discrete sub-groups that represent their relative economic value, selecting a single economic value sub-grouping for YES/NO comparison. For example: YES Detractors (who had MDP X) vs. NO Detractors (who did not have MDP X).   4. Calculating the percentage of customers falling into each sub group according to YES/NO status for the particular problem in question. For example: 57% of YES customers (who had MDP X) are Detractors, vs. 23% of NO customers (who did not have MDP X) are Detractors.   5. Calculating the impact of YES/NO status—i.e. the presence/absence of MDP X— on the economic sub-group, using class-comparative calculation. For example, 57% of YES customers (who had MDP X) are Detractors, vs. 23% of NO customers (who did not have MDP X) are Detractors. So when MDP X happens, it increases the likelihood of Detractorship (which represents damage to customer economic value) by 149%: (57%÷ 23%)− 23%=149%.   6. Converting the impact of problem presence/absence into customer economic value. This can be implemented in several ways. For example, when using DVs that proxy for economic value, the system  100  can use the economic proxy data to calculate economic damage from the problem. For example, if on average Detractors spend $100 less per annum than non-Detractors, and MDP X increases the likelihood of Detractorship by 149%, the imputed economic damage to a particular customer&#39;s spend when MDP X happens to that customer is an incremental $149. When using DVs that directly represent economic value (such as spending), the economic impact calculation can be calculated directly. Example: if YES customers (who had MDP X) spend on average $350/annum, and NO customers (who did not have MDP X) spend on average $480/annum, the impact of MDP X on per-customer annual spend is $130. Note that in circumstances where the analytic DV is a direct representation of customer economic value, The analysis can skip the Step  5  class-comparative calculations and DV proxy conversions.       

     With MDP frequency and absolute impact of the MDP calculated for a specific MDP, the final step in quantifying the economic impact of the MDP is to multiply both values to generate a single, weighted average overall economic impact value of the MDP across the full customer set. This value can be compared against other MDP scores for business prioritization: those MDPs that represent the greatest risk of economic damage to the customer base are those that represent the highest return on investment if the MDPs can be eliminated, reduced or mitigated. For example: out of 650 customers surveyed, 228 reported experiencing “MDP X”. MDP X has a frequency of 35%. When MDP X occurs, it damages annual customer spend by $149, which is also referred to as the “revenue at risk” value. The total customer base in this example is 22,000 customers. The total economic risk value of MDP X is approximately $1,150,000. 
     Problem resolution analysis can be undertaken to determine how to address an MDP as to mitigate damage to customer economic value. In addition to the MDP identification and quantification analyses described above, the system  100  can also conduct analysis on the efficacy and economic impact of a company&#39;s problem resolution processes. This analysis can explain how effective these processes are in mitigating damage to customer value due to problem experiences, and provide a company a roadmap for handling problems when they occur. 
     The problem resolution analysis may include, for example, after survey respondents have reviewed the problem inventory and identified which problems from the inventory they personally experienced, they each select one problem they deem “most important.” This can the problem that will be assessed in the problem resolution analysis. Note that the “most important problem” may be different from the “Most Damaging Problem” (or MDP). Most important problems are self-identified by respondents, not analytically derived, and are only used as a problem resolution case examples to determine problem resolution impact on customer economic value. 
     For each problem resolution case example, a respondent can provide detail on a range of descriptive attributes describing the problem resolution experience. For an analysis by the system  100 , three attributes are considered:
         Time: how long it took for the problem to be addressed;   Effort: how much effort (e.g., contact attempts made by the respondent) it took for the problem to be addressed;   Efficacy: how satisfied the respondent was overall with the problem resolution experience provided to them.       

     The attributes of time, effort and efficacy are then cross-referenced against the DV representing customer economic value to show how customer economic value changes as the attributes change. The analysis typically shows the following:
         Time. The longer it takes to resolve a problem, the greater the residual damage to the customer economic value originally lost due to the problem. Conversely, the shorter the time to resolve, the greater the “recovery” of the economic value originally lost due to the problem. Time-to-resolve analysis quantifies the extent of this loss or recovery, and establishes the primary time-to-resolve thresholds at which loss accelerates.   Effort. The more effort it takes for a customer to resolve a problem, the greater the residual damage to the customer economic value originally lost due to the problem. Conversely, the less effort exerted to resolve, the greater the “recovery” of the economic value originally lost due to the problem. Effort-to-resolve analysis quantifies the extent of this loss or recovery, and establishes the primary effort thresholds at which loss accelerates.   Efficacy. Overall, the more satisfied a customer is with the problem resolution provided to them, the lower the residual damage to the customer economic value originally lost due to the problem. Conversely, the less satisfied a customer is with problem resolution, the greater the residual damage to the customer economic value originally lost due to the problem. Efficacy analysis quantifies the extent of this loss or recovery across four a priori thresholds:
           Complete satisfaction with problem resolution;   Acceptable satisfaction with problem resolution;   Non-acceptable satisfaction with problem resolution;   No problem resolution occurred.   
               

       FIG.  3    shows an example list  300  of potential negative experiences customers can encounter at a physical grocery store, each mapped to a corresponding number of occurrence, based on a set of example feedback data  150  obtained from a group of customers. The list  300  includes problems such as unclean store, messy store, unappealing store, cluttered store, unclear signs, unavailable product, produce is too pricey, produce is not fresh, rice was different from another store in the same chain, not prompted for loyalty card, no organic produces, no associates to help, confusing price match policy, bad selection of products, bad selection of produces, and bad flyer. Some problems may be experienced in tandem, produce not fresh being a good example, which rarely occurs on its own, so modelling it is challenging even if it frequently occurs. For example, the problem of produces being not fresh may have 205 occurrences or counts, but has a positive coefficient indicating a positive impact on a customer&#39;s spending habits. Such a problem can be filtered by the analysis undertaken by the system  100 , as described above. 
     Once the impact to share of wallet is determined for each problem, the economical impact (e.g., “customer would have spent $300 more if this problem were not experienced”) can be calculated. The customer&#39;s monthly spending can be calculated from their stated share and stated spending level at that grocer, based on the feedback data  150 . Then the spending or revenue at risk from that problem can be calculated from the model coefficient as follows, shown in table 1 below. 
     
       
         
           
               
               
               
               
               
               
               
             
               
                 TABLE 1 
               
               
                   
               
               
                   
                   
                   
                   
                   
                 Monthly 
                   
               
               
                   
                   
                   
                   
                   
                 Spending, 
                 Impacted 
               
               
                   
                   
                   
                 Share 
                 Monthly 
                 across all 
                 Spending 
               
               
                   
                 Problem 
                 Model 
                 of 
                 Spending 
                 Grocery 
                 from 
               
               
                 Customer 
                 Experienced 
                 Coefficient 
                 Wallet 
                 at Grocer X 
                 Stores 
                 Problem ($) 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
               
            
               
                 A 
                 Not Prompted for 
                  7% 
                 33% 
                 $ 100.00 
                 $ 300.00 
                 21.00 
               
               
                   
                 Loyalty 
                   
                   
                   
                   
                   
               
               
                 A 
                 NoOrganic 
                  3% 
                 33% 
                 $ 100.00 
                 $ 300.00 
                 9.00 
               
               
                   
                 Produce 
                   
                   
                   
                   
                   
               
               
                 A 
                 Store Cluttered 
                 15% 
                 33% 
                 $ 100.00 
                 $ 300.00 
                 45.00 
               
               
                 B 
                 Not Prompted for 
                  7% 
                 50% 
                 $ 100.00 
                 $ 200.00 
                 14.00 
               
               
                   
                 Loyalty 
                   
                   
                   
                   
                   
               
               
                 C 
                 Bad flyer 
                  5% 
                 10% 
                 $ 100.00 
                 $ 1,000.00 
                 50.00 
               
               
                   
               
            
           
         
       
     
       FIG.  4    is an example graphical user interface (GUI)  400  displaying an example average revenue at risk per customer based on the feedback fata  150  gathered by the system  100 . As shown, an average annual revenue at risk per customer value  410  is shown to be $−61, which means that a customer is likely to send $61 less, on average, based on a plurality of problems identified in area  425  of the GUI. The average annual revenue at risk per customer value is also part of a trend graph  415 . Out of the problems identified in  425 , the top problem  418  is limited home appliance selection, with the highest revenue at risk of $-15. Area  420  of the GUI  400  displays the corresponding revenue at risk value for each of the problem identified in area  425 . A separate area  430  shows a category of each problem identified in area  425 . 
       FIG.  5    is an example graphical user interface (GUI)  500  displaying an average annual revenue at risk per customer and a list of locations with the highest risk or lowest risk. The average annual revenue at risk per customer  510  in this case is shown to be $−167. The trend plot  515  spans from the second quarter of  2020  to the second quarter of  2021 , for example. The period may be modified based on user requirements or selection. In addition, GUI  500  shows the top 5 locations  520  in a physical store experiencing the lowest amount of revenue at risk per customer. Similarly, GUI  500  shows the bottom 5 locations  525  in a physical store experiencing the highest amount of revenue at risk per customer. 
       FIG.  6    is an example graphical user interface (GUI)  600  displaying an average revenue at risk per customer grouped by demographic and household income. Area  610  shows an average revenue at risk by demographic, including gender and generations. Area  620  shows an average revenue at risk by household income, ranging from under $35,000 income to over $200,000 income. Area  630  shows an average revenue at risk by households with or without children. 
       FIG.  7    shows an example process  700  for computing economic impact of customer experiences performed by the system  100  in  FIG.  1   , exemplary of embodiments. The method  700  may include, at block  702 , the system  100  stores and maintains, in database  122 , a data set including a plurality of types of negative customer experiences. For example, the data set may include a portfolio of hundreds of experiences  220  for customers  210 , who encounter these experiences in the course of their relationship with a company  230 , at their physical store locations. 
     At block  704 , the system  100  maintains a tree model  110  for predicting economic impact of one or more of the plurality of types of negative customer experiences. In some embodiments, the tree model  110  is a classification and regression tree (CART) model and the decision tree is a binary tree. 
     At block  706 , the system  100  receives feedback data  150  reflective of customer experiences.  FIGS.  8 A to  8 G  show an example survey that can be given to by one or more customers for completion. For example, the survey can be electronically presented to the one or more customers at their display devices  130  via an e-mail link. The answers from the survey completed by the customers may be processed and stored as the feedback data  150 . 
     At block  708 , the system  100  generates a decision tree  112  based on the tree model  110 , the data set and the feedback data  150 , the decision tree having a plurality of internal nodes with each internal node corresponding to a type of the plurality of types of negative customer experiences. The tree model  110  may be used to generate the decision tree for computing the economic impact of the customer experiences based on feedback data  150  received via network  140 . The decision tree may include a plurality of internal nodes with each internal node corresponding to a type of the plurality of types of negative customer experiences. An internal node may refer to a node that has child node(s). 
     In some embodiments, each leaf (or leaf node) of the decision tree may include a class label indicating a classification of a type of negative customer experience corresponding to a given internal node of the decision tree, which may be the parent node of the leaf. For example, the class label may has a real value between 0 and 1, where a value equal to or greater than 0.5 indicates that the associated type of negative customer experience has a meaningful economical impact. A meaningful economical impact may indicate that the type of negative customer experience has resulted in a economical loss above a certain threshold during a period, e.g., $1,000 per week. 
     In some embodiments, generating the binary tree  112  may be done through machine learning using a predictive modeling. An example decision tree algorithm is classification and regression trees (CART). Using the CART algorithm, the training engine  116  may be configured to generate binary tree by selecting input variables and split points on those variables until a suitable tree is constructed. The selection of which input variable to use and the specific split can be implemented using a greedy algorithm to minimize a cost function. Typically, construction of the binary tree ends based on a predefined stopping criterion, such as a minimum number of training instances assigned to each leaf node of the tree. 
     In some embodiments, the binary tree  112  may be generated by: splitting a data set representing a plurality of types of negative customer experiences into two groups based on a first cost function; and for each of the two groups: splitting the types of negative customer experiences in each respective group into two subsets based on a second cost function; and iteratively splitting the types of negative customer experiences in each respective subset into further binary subsets using a recursive binary splitting procedure until a predetermined threshold is reached. The predetermined threshold can be, for instance, a count on a total number of training instances assigned to each internal node of the binary tree. 
     In some embodiments, splitting the types of negative customer experiences includes selecting one type from the types of negative customer experiences and setting the selected type as an internal node. 
     At block  710 , the system  100  computes economic impact of at least one of the types of negative customer experiences using the generated decision tree and the feedback data  150 . In some embodiments, computing the economic impact of at least one of the types of negative customer experiences may include: computing, for the at least one type of negative customer experience, a frequency of occurrence among a plurality of customers based on the feedback data; computing, for the at least one type of negative customer experience, a financial impact on the plurality of customers based on the feedback data; and determining the economic impact of for the at least one type of negative customer experience by multiplying the frequency of occurrence by the financial impact. 
     In some embodiments, computing the financial impact for the at least one type of negative customer experience on the plurality of customers based on the feedback data may include: determining, based on the feedback data, a first average amount of spending among a first group of customers that did not experience the at least one type of negative customer experience, the first group of customers from the plurality of customers; determining, based on the feedback data, a second average amount of spending among a second group of customers that experienced the at least one type of negative customer experience, the second group of customers from the plurality of customers; and computing the financial impact based on a difference between the first average amount of spending and the second average amount of spending. 
     At block  712 , the system  100  causes to render, at a display screen of a display device  130 , a graphic user interface visualizing the computed economic impact of at least one of the types of negative customer experiences. Example GUI data elements are shown in  FIGS.  4 ,  5  and  6   . 
     In some embodiments, the CART analysis may be augmented with other machine-learning capabilities to identify additional high-impact relationships between data elements that will inform a more robust and actionable experience-to-value model for clients. These relationships may include, for example:
         a. How customer experiences influence a range of customer economic value markers concurrently (e.g. spend, spend velocity, trial propensity, product penetration, share allocation, service costs, etc.), and how these value markers interact between themselves to deliver a desired “meta-optima” that projects the future customer economic potential more accurately than any single value marker.   b. The customer characteristics that influence how experiences impact customer value. These would include demographic and psychographic characteristics, as well as other behavioral characteristics not comprehended by spend/value behaviors: channel proclivities, information seeking habits, usage/ownership behaviors, depth of relationship with company agents, etc.   c. The circumstances of experience creation that influence how experiences impact customer value. These could be any situational data point relevant to a “problem experience use case”, including channel, product, time/seasonality, go-to-market model (e.g. direct vs. retail intermediated), etc.       

     The foregoing discussion provides many example embodiments of the inventive subject matter. Although each embodiment represents a single combination of inventive elements, the inventive subject matter is considered to include all possible combinations of the disclosed elements. Thus if one embodiment comprises elements A, B, and C, and a second embodiment comprises elements B and D, then the inventive subject matter is also considered to include other remaining combinations of A, B, C, or D, even if not explicitly disclosed. 
     The embodiments of the devices, systems and methods described herein may be implemented in a combination of both hardware and software. These embodiments may be implemented on programmable computers, each computer including at least one processor, a data storage system (including volatile memory or non-volatile memory or other data storage elements or a combination thereof), and at least one communication interface. 
     Program code is applied to input data to perform the functions described herein and to generate output information. The output information is applied to one or more output devices. In some embodiments, the communication interface may be a network communication interface. In embodiments in which elements may be combined, the communication interface may be a software communication interface, such as those for inter-process communication. In still other embodiments, there may be a combination of communication interfaces implemented as hardware, software, and combination thereof. 
     Throughout the foregoing discussion, numerous references will be made regarding servers, services, interfaces, portals, systems, or other systems formed from computing devices. It should be appreciated that the use of such terms is deemed to represent one or more computing devices having at least one processor configured to execute software instructions stored on a computer readable tangible, non-transitory medium. For example, a server can include one or more computers operating as a web server, database server, or other type of computer server in a manner to fulfill described roles, responsibilities, or functions. 
     The technical solution of embodiments may be in the form of a software product. The software product may be stored in a non-volatile or non-transitory storage medium, which can be a compact disk read-only memory (CD-ROM), a USB flash disk, or a removable hard disk. The software product includes a number of instructions that enable a computer device (personal computer, server, or network device) to execute the methods provided by the embodiments. 
     The embodiments described herein are implemented by physical computer hardware, including computing devices, servers, receivers, transmitters, processors, memory, displays, and networks. The embodiments described herein provide useful physical machines and particularly configured computer hardware arrangements. 
     Although the embodiments have been described in detail, it should be understood that various changes, substitutions and alterations can be made herein. 
     Moreover, the scope of the present application is not intended to be limited to the particular embodiments of the process, machine, manufacture, composition of matter, means, methods and steps described in the specification. 
     As can be understood, the examples described above and illustrated are intended to be exemplary only. 
     Of course, the above described embodiments are intended to be illustrative only and in no way limiting. The described embodiments are susceptible to many modifications of form, arrangement of parts, details and order of operation. The disclosure is intended to encompass all such modification within its scope, as defined by the claims.