Dynamic checkout page optimization to forestall negative user action

Abstract

In an example embodiment, a method for processing payments made via an electronic payment processing system is provided. An example method includes obtaining training data from a data source. The training data relates to prior purchases made via the electronic payment processing system, wherein the data source includes, in some examples, only a checkout page in a purchase transaction funnel. Features associated with a negative user action in relation to prior purchases are identified. A machine learning algorithm produces a dynamic transactional behavior score indicative of a probability that a purchase will invoke a negative user action.

What is claimed is: 
     
         1 . A method of processing payments made via an electronic payment processing system, the method comprising:
 obtaining training data relating to prior purchase transactions processed via the electronic payment processing system, the training data including data derived from respective checkout pages presented by respective interfaces of the electronic payment processing system in respective checkout flows of the prior purchase transactions;   extracting one or more features from the training data, the one or more features associated with a negative user action invoked in relation to at least one of the prior purchase transactions;   for a real-time purchase transaction not included in the training data, using the extracted one or more features associated with the negative user action to derive a transactional behavior score indicative of a probability that the real-time purchase transaction will invoke the negative user action taken by a user in relation to the real-time purchase transaction;   based on the transactional behavior score, causing a dynamic optimization, during a respective checkout flow of the real-time purchase transaction, of a user interface presenting a checkout page in the respective checkout flow, the dynamic optimization of the user interface including insertion in the respective checkout flow of a targeted remedial action to reduce a probability that the real-time purchase transaction will invoke the negative user action taken in relation to the real-time purchase transaction;   receiving a request to invoke the targeted remedial action; and   in response to receiving the request to invoke the targeted remedial action, triggering the targeted remedial action.   
     
     
         2 . The method of  claim 1 , wherein the training data is obtained from a data source, the data source confined to data contained in or associated with the respective checkout pages. 
     
     
         3 . The method of  claim 2 , wherein the invoked negative user action includes one or more of a refund request, a chargeback request, and a return request. 
     
     
         4 . The method of  claim 3 , further comprising confining the training data to information extracted from the respective checkout pages and associated with the invoked negative user action. 
     
     
         5 . The method of  claim 1 , wherein the training data includes a data structure, the data structure including a first user interaction section and a checkout page data section. 
     
     
         6 . The method of  claim 5 , wherein the first user interaction section includes data relating to one or more of:
 a time period between a loading of the checkout page and a taking or completion of a payment action;   a number of times or frequency a customer viewed the checkout page before taking or completing a payment action;   a number of typos or other mis-entries corrected prior to taking or completing a payment action;   a detection of an omitted field completed prior to taking or completing a payment action;   a number, type, or frequency of a mouse movement;   a detection of a payment denial;   a detection of a decline of a payment instrument;   a detection of a substitution of the payment instrument;   an IP address associated with a prior purchase or the real-time purchase; and   a local user time of the prior purchase or the real-time purchase.   
     
     
         7 . The method of  claim 5 , wherein the checkout page data section includes data relating to one or more of:
 a detection of a first-time customer;   a detection of a repeat customer;   a time period of an interval between a prior purchase and the real-time purchase, assessed relative to a customer average for the interval;   a user cart size associated with the real-time purchase, assessed relative to an average user cart size for a purchase;   a locality or default currency of a payment instrument to give an indication of a customer's location relative to a merchant;   user order or shipping API data;   a selection of a shipping service or shipping rate;   embedded metadata associated with a user action;   a detection of a refund; and   a detection of a support ticket or request.   
     
     
         8 . A system for processing electronic payments, the system comprising:
 at least one processor; and   a memory storing instructions that, when executed by the at least one processor, cause the system to perform operations comprising, at least:   obtaining training data relating to prior purchase transactions processed via the electronic payment processing system, the training data including data derived from respective checkout pages presented by respective interfaces of the electronic payment processing system in respective checkout flows of the prior purchase transactions;   extracting one or more features from the training data, the one or more features associated with a negative user action invoked in relation to at least one of the prior purchase transactions;   for a real-time purchase transaction not included in the training data, using the extracted one or more features associated with the negative user action to derive a transactional behavior score indicative of a probability that the real-time purchase transaction will invoke the negative user action taken by a user in relation to the real-time purchase transaction;   based on the transactional behavior score, causing a dynamic optimization, during a respective checkout flow of the real-time purchase transaction, of a user interface presenting a checkout page in the respective checkout flow, the dynamic optimization of the user interface including insertion in the respective checkout flow of a targeted remedial action to reduce a probability that the real-time purchase transaction will invoke the negative user action taken in relation to the real-time purchase transaction;   receiving a request to invoke the targeted remedial action; and   in response to receiving the request to invoke the targeted remedial action, triggering the targeted remedial action.   
     
     
         9 . The system of  claim 8 , wherein the training data is obtained from a data source, the data source confined to data contained in or associated with the respective checkout pages. 
     
     
         10 . The system of  claim 9 , wherein the invoked negative user action includes one or more of a refund request, a chargeback request, and a return request. 
     
     
         11 . The system of  claim 10 , wherein the operations further comprise confining the training data to information extracted from the respective checkout pages and associated with the invoked negative user action. 
     
     
         12 . The system of  claim 8 , wherein the training data includes a data structure, the data structure including a first user interaction section and a checkout page data section. 
     
     
         13 . The system of  claim 12 , wherein the first user interaction section includes data relating to one or more of:
 a time period between a loading of the checkout page and a taking or completion of a payment action;   a number of times or frequency a customer viewed the checkout page before taking or completing a payment action;   a number of typos or other mis-entries corrected prior to taking or completing a payment action;   a detection of an omitted field completed prior to taking or completing a payment action;   a number, type, or frequency of a mouse movement;   a detection of a payment denial;   a detection of a decline of a payment instrument;   a detection of a substitution of the payment instrument;   an IP address associated with a prior purchase or the real-time purchase; and   a local user time of the prior purchase or the real-time purchase.   
     
     
         14 . The system of  claim 12 , wherein the checkout page data section includes data relating to one or more of:
 a detection of a first-time customer;   a detection of a repeat customer;   a time period of an interval between a prior purchase and the real-time purchase, assessed relative to a customer average for the interval;   a user cart size associated with the real-time purchase, assessed relative to an average user cart size for a purchase;   a locality or default currency of a payment instrument to give an indication of a customer's location relative to a merchant;   user order or shipping API data;   a selection of a shipping service or shipping rate;   embedded metadata associated with a user action;   a detection of a refund; and   a detection of a support ticket or request.   
     
     
         15 . A non-transitory machine-readable medium comprising instructions which, when read by a machine, cause the machine to perform operations for processing payments made via an electronic payment processing system, the operations comprising, at least:
 obtaining training data relating to prior purchase transactions processed via the electronic payment processing system, the training data including data derived from respective checkout pages presented by respective interfaces of the electronic payment processing system in respective checkout flows of the prior purchase transactions;   extracting one or more features from the training data, the one or more features associated with a negative user action invoked in relation to at least one of the prior purchase transactions;   for a real-time purchase transaction not included in the training data, using the extracted one or more features associated with the negative user action to derive a transactional behavior score indicative of a probability that the real-time purchase transaction will invoke the negative user action taken by a user in relation to the real-time purchase transaction;   based on the transactional behavior score, causing a dynamic optimization, during a respective checkout flow of the real-time purchase transaction, of a user interface presenting a checkout page in the respective checkout flow, the dynamic optimization of the user interface including insertion in the respective checkout flow of a targeted remedial action to reduce a probability that the real-time purchase transaction will invoke the negative user action taken in relation to the real-time purchase transaction;   receiving a request to invoke the targeted remedial action; and   in response to receiving the request to invoke the targeted remedial action, triggering the targeted remedial action.   
     
     
         16 . The medium of  claim 15 , wherein the training data is obtained from a data source, the data source confined to data contained in or associated with the respective checkout pages. 
     
     
         17 . The medium of  claim 16 , wherein the invoked negative user action includes one or more of a refund request, a chargeback request, and a return request. 
     
     
         18 . The medium of  claim 17 , wherein the operations further comprise confining the training data to information extracted from the respective checkout pages and associated with the invoked negative user action. 
     
     
         19 . The medium of  claim 15 , wherein the training data includes a data structure, the data structure including a first user interaction section and a first checkout page data section. 
     
     
         20 . The medium of  claim 19 , wherein the first user interaction section includes data relating to one or more of:
 a time period between a loading of the checkout page and a taking or completion of a payment action;   a number of times or frequency a customer viewed the checkout page before taking or completing a payment action;   a number of typos or other mis-entries corrected prior to taking or completing a payment action;   a detection of an omitted field completed prior to taking or completing a payment action;   a number, type, or frequency of a mouse movement;   a detection of a payment denial;   a detection of a decline of a payment instrument;   a detection of a substitution of the payment instrument;   an IP address associated with a prior purchase or the real-time purchase; and   a local user time of the prior purchase or the real-time purchase; and wherein the checkout page data section includes data relating to one or more of:   a detection of a first-time customer;   a detection of a repeat customer;   a time period of an interval between a prior purchase and the real-time purchase, assessed relative to a customer average for the interval;   a user cart size associated with the real-time purchase, assessed relative to an average user cart size for a purchase;   a locality or default currency of a payment instrument to give an indication of a customer's location relative to a merchant;   user order or shipping API data;   a selection of a shipping service or shipping rate;   embedded metadata associated with a user action;   a detection of a refund; and   a detection of a support ticket or request.