Patent Publication Number: US-2019188735-A1

Title: Data driven customer loyalty prediction system and method

Description:
TECHNICAL FIELD 
     The present disclosure generally relates to systems and methods for predicting the likelihood of customer loyalty and value at the time of a sales transaction. 
     BACKGROUND 
     Customer loyalty is key to the success of many small businesses. However, some businesses generally lack the data and resources to identify potentially valuable or loyal customers in a manner and time that permits the business to respond to the customer appropriately at a point of sale. Because of the limited resources that businesses have available to market to new customers and retain existing customers, being able to identify potentially loyal (and thus more valuable) customers for which to focus resources is critical. In addition, in many small businesses, the customer transaction is of short duration. As such, many small businesses do not have the opportunity to identify and then market to the customer at the time of sale using conventional methods. 
     Therefore a need exists for systems and methods for identifying potentially loyal repeat customers at the time of sale using information easily and unobtrusively obtainable at the time of sale. 
     SUMMARY 
     In an exemplary embodiment, a data ecosystem may receive transaction data during and in conjunction with the approval process of a purchase card transaction. The data ecosystem can process the transaction data in conjunction with historical data and environmental data in order to identify whether the customer is likely to be a desirable customer in the future. 
     In another exemplary embodiment, the data ecosystem employs a logistic regression algorithm to estimate behavioral probabilities of the customer. The algorithm can learn from historical data and apply variable weights to fine-tune the predicted behavior. 
     In yet another exemplary embodiment, a prediction of future customer behavior produced by the data ecosystem is used to provide an indication of the behavior to a merchant in real-time or near real-time. In some exemplary embodiments, one or more promotional offers may be determined based on the likelihood of desirable future customer behavior. In such embodiments, these offers may be presented to a merchant during payment card processing for a sale so that the merchant can provide the customer with these offers to reward or encourage more of the type of customer behavior desired by the merchant. By providing such offers during the payment processing, the merchant has the opportunity to interact directly with the customer to encourage the desired behavior. For instance, if the merchant wishes to encourage the customer to visit more frequently, the offer may consist of a reward or discount that results from frequent visits. Another exemplary embodiment may present the customer with free upgrades or additions in conjunction with the pending transaction. As a result, the customer is encouraged to take action while they are already at the merchant. This differs from the delayed interaction that may (or may not) take place as the result of a printed coupon or similar future offering. 
     In yet another exemplary embodiment, a method for predicting future customer behavior at a point of sale is provided. The method comprising: receiving current transaction data related to a digital transaction between a merchant and a customer at the point of sale; receiving historical transaction data related to past purchasing behavior of the customer; identifying the customer as a desirable customer based on the current transaction data and the historical transaction data. 
     In yet another exemplary embodiment, a system for predicting future customer behavior at a point of sale is provided. The system comprising: a data ecosystem configured to: collect current transaction data related to a digital transaction between a merchant and a customer at the point of sale; receive historical transaction data related to past purchasing behavior of a customer; identify the customer as a desirable customer based on the current transaction data and the historical transaction data. 
     In yet another exemplary embodiment, A method for offering a promotional award at the point of sale between a customer and a merchant, the method comprising: receiving current transaction data related to a digital transaction between the customer and the merchant; receiving historical transaction data related to past purchasing behavior of the customer; identifying the customer as a desirable customer based on the current transaction data and the historical transaction data; and, generating at least one promotional offer for the customer. 
     These and other objects, features, and advantages of the present disclosure will become apparent form the following detailed description of illustrative embodiments thereof, which is to be read in connection with the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       These and other features of the present disclosure will become better understood with regard to the following description and accompanying drawings in which: 
         FIG. 1  is a diagram illustrating known methods of interaction between merchants and customers conducting an electronic transaction at a point of sale; 
         FIG. 2  is a diagram of an interaction between a merchant and customer conducting an electronic transaction according to an exemplary embodiment; 
         FIG. 3  is a diagram of a machine learning logistic regression algorithm and data associated with that algorithm according to an exemplary embodiment; 
         FIG. 4  is an illustration of a logistic regression algorithm of the type used by an exemplary embodiment; 
         FIG. 5  is a user interface produced by an exemplary embodiment; and 
         FIG. 6  is another example of a user interface produced by an exemplary embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     As is illustrated in  FIG. 1 , known methods of marketing to a customer  104  before and at the time of sale include providing incentives and advertising materials  102  directly at the point of sale. These incentives and materials are frequently directed to the general public as is the case with direct mail, radio, or television advertising. This unfocused approach does not allow merchants  106  to focus their efforts on potential customers or current customers that are likely to be desirable customers (e.g., those that are frequent customers, those that make large purchases, or both). Using known methods, the interaction between the customer  104  and the merchant  106  was generally limited to the exchange of goods or services and a payment for those goods and services. As is illustrated, when a customer  104  pays using a purchase card or credit card, the card is presented to a point of sale (POS) terminal  108 . The POS terminal  108  reads the card information and contacts a transaction authorization system  110 . The transaction authorization system  110  is provided with such information as the card number, transaction account, and certain authorization codes. This information is then used to authorize (or decline) the transaction. In some cases, the transaction information may be stored in a database  112 . 
     When a customer  104  uses a purchase card to conduct a transaction, the card processor generally obtains a certain amount of information about the customer. For example, the customer&#39;s credit card number and card features are generally received from the POS terminal  108 . In addition, the merchant name, the merchant location, the interaction date, the time of day, the industry, and the purchase price are also generally determinable and thus may be collected and stored in the database  112 . If the credit card processor also happens to be the issuer of the purchase card being used, the name of the customer  104 , their mailing address, email address, credit limit, purchase card currency, credit rating at the time of card issue, and other demographic information may also be known. Thus, the database  112  likely comprises this information for the customer  104  as well as many others. It is therefore an aspect of the present disclosure to provided improved systems and methods for collecting such data and leveraging the data to predict customer behavior at the time of a transaction. 
       FIG. 2  is illustrative of a data flow  200  during a digital sales transaction between a customer  104  and a merchant  106  at a point of sale according to an exemplary embodiment. As used herein, a digital sales transaction or digital transaction refers to a transaction completed without the need for cash, such as credit or debit card purchases. It will be appreciated that other types of digital transactions where it is possible to collect real-time data about the transaction could also be used in connection with the systems and methods as disclosed here. In particular, the data flow  200  generally relates to the back end systems  202 . The data processed and/or collected is used to develop the regression model used by the exemplary embodiment. As is illustrated in  FIG. 2 , data may be collected, stored, and processed by a big data ecosystem  204 . It should be appreciated that in various other embodiments as described herein, big data ecosystem  204  may comprise other data ecosystems (i.e. smaller data ecosystems), environments, databases, and/or repositories, and that the term “big data” should not be construed as limiting. Big data ecosystem may comprise a plurality of servers or databases operable to collect, process, and store large amounts of data. It will be appreciated that big data ecosystem  204  comprises processing components operable to perform processing on data that is collected or stored at the big data ecosystem. The processing components of the big data ecosystem  104  may comprise at least one processor in communication with a memory, wherein the processor is operable to execute instructions stored in the memory. It will be further appreciated that big data ecosystem  204  may comprise one or more storage devices operable to store data collected or received by the big data ecosystem  204 . In some embodiments, the storage devices of big data ecosystem  204  are distributed across one or more computers or servers operable to transmit and receive data over a network. It will be further appreciated that big data ecosystem  104  may have alternative configurations of hardware and software, and the above exemplary components are offered for illustrative purposes only. 
     In some embodiments, the data collected or received by the big data ecosystem  204  may include merchant profile data  206 , other source data such as environmental data  208 , and transaction data  210 . The data collected by the big data ecosystem  204  may be collected from a single database or data repository, such as a kind operated by a large bank or financial institution, or additionally, from a plurality of databases, data repositories, or similar data sources distributed across a network. In some embodiments transaction data  210  may comprise historical transaction data related to past purchases by customer  104  and/or other customers. In one embodiment, this historical data may be obtained from authorization systems  110 . Similarly, the big data ecosystem  204  may also collect transaction data related to a current, ongoing transaction. This data is collected as current transaction data  212 , and may be collected at the time of the transaction, for example, in real-time or near real-time. The collected data may be processed at the big data ecosystem  204  by machine learning algorithms executed by processing components within the data ecosystem  204 . These algorithms may be utilized by the big data ecosystem  204  in order to build additional algorithms used to provide a prediction of future customer behavior to the merchant at the time of a digital point-of-sale transaction. 
     Merchant profile data  206  may comprise information related to the merchant&#39;s business and indicate the characteristics of a desirable or “good” customer and corresponding customer behavior or spending habits. It will be appreciated that this behavior may be relative and can vary depending upon the merchant&#39;s business. For example, a coffee shop may consider a customer that spends $5 per visit and visits 5 days per week, a desirable customer. Conversely, a car dealer may require a much higher level of spending but would consider one visit every 24 months to be a desirable customer. The service department of that same car dealer may have an altogether different standard for a desirable customer. Thus, merchant profile data  206  facilitates consideration of customer behavior as related to the business type. 
     In addition to merchant data  206 , the data ecosystem  204  may collect or receive data from other sources, such as environmental data  208 . Environmental data  208  may comprise data relating to customer behavior. This data may be used to understand current customer activity (e.g. current transaction  212 ) and to predict future activity. In an exemplary embodiment, environmental data  208  may comprise information such as, but not limited to, a calendar of events, location data, location demographics, social media data, and/or merchant promotional activity. This information may be collected and processed by the big data ecosystem  204  to extract customer behavioral predictors. For example, a merchant  106  located along a parade route may experience a higher than normal level of traffic during the parade. In another example, a merchant  106  may be mentioned in a social media post which results in spike in customer activity. This environmental data  208  can be used to enhance the granularity of the predictions produced by models for predicting customer behavior. Examples of environmental data include, without limitation, a moving 60-day average transaction amount and weighting factors for weekday versus weekend data. 
     Transaction data  210  comprising historical data may be provided as a training dataset to the big data ecosystem  204  to develop an initial model to predict customer behavior. The historical data may comprise information regarding discrete transaction behavior of the customer as well as purchasing trends indicative of longer term behavior. Discrete transaction behavior may be correlated with longer term customer behavior to establish a model of the relationship between the two data sets. As is illustrated in  FIG. 3 , the data ecosystem  204  may collect data and build a logistic regression algorithm  304  for the use by a merchant (such as merchant  106 ). After this initial training dataset is constructed, the big data ecosystem  204  may receive transaction data  210  comprising historical data as well as current transaction data  212 . For purposes of constructing training data sets for the big data ecosystem  204 , transaction data  210  comprises historical transaction data which may include historical data from a plurality of merchants. In addition to historical data, the big data ecosystem  204  may receive current transaction data  212  e.g. in real-time or near real-time. 
     In an exemplary embodiment, the big data ecosystem  204  may learn from transaction data  210  which comprises historical transaction data associated with customers  104  who exhibit the behavior desired by the merchant  106 . This behavior can be obtained from the transaction data  210  but may also be obtained in real-time or near real-time by processing current transaction data  212  and feature data  303  that has been correlated with historical data  210  reflecting the past purchasing behaviors of the customer  104 . As a result, the logistic regression algorithm  304  utilized by the big data ecosystem  204  can continuously learn from customer behavior. As is illustrated in  FIG. 2 , other data, such as environmental data  208  can also be used by the big data ecosystem  204  to account for various other factors in order to further refine the predictive capabilities of the data ecosystem. 
     The transaction data  210  and current transaction data  212  may be combined and loaded or otherwise transferred into a table  302  which can accumulate the data into combined transaction data  216 . The table  302  may be a digital spreadsheet or the like configured to provide structure and enhance readability and interpretation of the data. Table  302  may also contain data related to prediction data  308  which comprises determined predictions of customer behavior produced by the logistic regression algorithm  304 . In some embodiments, the combined transaction data  216  (e.g. transaction data  210  and current transaction data  212 ) may comprise merchant identifiers, timestamps, and amounts spent during a digital sales transaction. The combined transaction data  216  may further comprise feature data  303  which is data related to the time of the transactions and their amounts. This feature data  303  may be used to correlate data related to the change in transaction details from a first to a second transaction and generate new data describing this change. The frequency of customer visits may be determined from this information as well as the changes in spending during each visit and an average transaction amount. A logistic regression algorithm  304  may be applied to the data to calculate a function representative of customer behavior. An example of the logistic regression algorithm  304  is illustrated in  FIG. 4 . As is shown, a threshold curve  402  is determined that can indicate a positive or negative prediction of future customer behavior depending upon where the customer&#39;s data falls relative to the threshold curve  402 . This curve is developed and tested using the machine learning logistic regression algorithm  304  and the associated training dataset provided by the transaction data  210 . Results of this testing may be output by the logistic regression algorithm  304  as test results  310 . 
     Once the threshold curve  402  is determined, the logistic regression algorithm  304  can be applied to current transaction data  212  e.g. in real-time or near real-time. Referring again to  FIG. 2 , a customer  104  makes a purchase from a merchant  106  and the parties initiate a digital transaction. During the purchase, the customer  104  pays for the purchase using a payment card. As was noted in  FIG. 1 , this process generally requires that the payment card be read by a POS terminal  108  and the transaction be approved by a transaction authorization system  110 . As is illustrated, the current transaction data  212  is provided to both the authorization system  110  and to the data ecosystem  204 . In the illustrated example, the current transaction data  212  is transmitted to the data ecosystem  204  using an encrypted data cache  214 . The processing of both the approval by the authorization system  110  and the customer behavior prediction from the data ecosystem  204  can be performed in real-time or near real-time. Thus, the merchant  106  may receive approval concurrently with receipt of the predicted customer behavior. 
     An example payment card processing interface  500  is illustrated in  FIG. 5 . As shown, the card account number  502  is provided. In addition to the card number  502 , the expiration date  504 , the amount  506 , and the cardholder&#39;s postal code  508  may be provided either directly from the card or by the customer  104  or merchant  106 . In some embodiments, additional information pertaining to the transaction may be required, such as personal identification number (PIN), card issuer, bank, etc. This information can be provided to the big data ecosystem  204  and authorization system  110  as illustrated in  FIG. 2 . In some embodiments, the big data ecosystem  204  is in communication directly with the authorization system  110  and can request and collect the data from the authorization system. Presuming the transaction is approved, a user interface  600  such as the exemplary embodiment illustrated in  FIG. 6  is displayed to the merchant  106  in response to the approval of the transaction. As shown, the user interface  600  may comprise an approval  602 , customer information  604 , and, in the example illustrated, a selection of rewards  606  that may be presented to the merchant  106  or the customer  104 . Typically, at least one of the offered rewards will involve an immediate benefit (e.g., a free additional item or a refund or reduction in the current transaction amount). In an exemplary embodiment of the invention, the selection of offers  606  is determined by the result of calculations performed by the big data ecosystem  204 . 
     As illustrated in  FIG. 3 , the machine learning logistic regression algorithm  304  may receive a communication or call  306  from the POS terminal  108 , which includes transaction data as illustrated in  FIG. 5 . The logistic regression algorithm  304  may then generate prediction data  308 . The prediction data  308  may be used to generate a selection of offers  606 . The selection of offers  606  may be generated based on the data collected by the big data ecosystem  204 , such as the current transaction data, historical transaction data, environmental data, or the like. In an exemplary embodiment, this prediction process can happen in real-time or near real-time such that the prediction may be made and the selection of offers generated and sent for display to the merchant or the customer immediately after the purchase has been approved. In certain embodiments, the selection of offers  606  is presented to the user in the form of a text message or notification delivered to a mobile device associated with the customer or the customer&#39;s payment card. In some embodiments, the approval may be withheld to account for any discounts or variations in the purchase offered in the selection of offers  606 . In some embodiments, the selection may vary according to preferences determined by the merchant  106 . In other embodiments, a promotional offer or reward may automatically be applied to the transaction based on merchant  106 &#39;s preferences. 
     The merchant  106  may choose to offer one or more of the rewards to the customer  104  at the time of the transaction based on the selection of offers  606 . Because the transaction and customer information can be processed by the exemplary embodiment in real-time or near real-time, the customer  104  may be presented with the rewards concurrently or immediately after the transaction. This interaction may help to make the customer  104  feel appreciated instantly and also serve to reward the behavior desired by the merchant (making a purchase in the illustrated example), which is expected to better strengthen the relations (e.g. loyalty) between the merchant and the customer than other forms of rewards that provide a more delayed form of satisfaction. 
     In some embodiments, a customer may choose to pay with a contactless payment method, for example, contactless credit card, Apple Pay, Google Pay, or the like. In these embodiments, the transaction between customer  104  and merchant  106  occurs identically to a traditional credit or debit card payment transaction, however, the merchant does not handle a physical payment card, and instead, the customer may place a payment card or mobile device in proximity to a contactless payment terminal, for example a reader utilizing radio frequency identification (RFID) or near field communication (NFC) to collect payment information in the same or similar way as a traditional payment card. In such situations, the presently described systems and methods may be applied such that a customer is provided with an interface such as user interface  600  to select a reward, discount, or other promotional offer. In certain embodiments, a reward or promotional offer is given to customer  104  automatically based on calculations performed by the big data ecosystem  204 . 
     While the present disclosure and associated concepts have been illustrated by the description of various embodiments thereof, and while these embodiments have been described in considerable detail, it is not the intention of the Applicant to restrict or in any way limit the scope of the disclosure to such detail. Additional advantages and modifications will readily appear to those skilled in the art. Moreover, in some instances, elements described with one embodiment may be readily adapted for use with other embodiments. Therefore, the disclosure, in its broader aspects, is not limited to the specific details, the representative apparatus, and illustrative examples shown and described. Accordingly, departures may be made from such details without departing from the spirit or scope of the general concepts described and enabled herein.