Intelligent Prediction of An Expected Value of User Conversion

Abstract

Highly user-specific data is used to calculate user intent to make a purchase and the value of such a purchase. User activity and information is aggregated, per user, for a set window of time and real-time data on recent site behavior is obtained. Aggregated and/or real-time data is considered by a predictive intent model (calculating the probability that the user will make a purchase) and a predictive value model (calculating the expected revenue such a purchase may generate). Weights, specific to each model, are assigned to predictor features tracked in the aggregated and/or real-time user data. The most highly-weighted features of the intent model relate to users' viewing history, and the most highly-weighted features of the value model relate to price and market. By these means, a user conversion value can be obtained, guiding the application of user acquisition strategies for different home sharing markets.

The following is claimed: 
     
         1 . A method for determining a predicted value of a user purchase via a computer system, the method comprising:
 storing a first plurality of user metrics in a raw data format, the first plurality of user metrics relating to a user's activity on the computer system;   storing a second plurality of user metrics associated with the user's activity on the computer system within a window of time, the second plurality of user metrics being generated by aggregation of data in the first plurality of user metrics;   grouping the second plurality of user metrics into one or more features, each feature corresponding to a statistic of the data in the second plurality of user metrics;   training a first machine learning model, using a first regression-based algorithm, based on a first set of feature-specific weighting that distinguishes between the one or more features;   training a second machine learning model, using a second regression-based algorithm, based on a second set of feature-specific weighting that distinguishes between the one or more features;   generating a predicted probability of the user purchase, based on (a) the second plurality of user metrics and (b) the first machine learning model; and   generating a predicted return on investment (ROI) value, based on (a) the second plurality of user metrics, (b) the second machine learning model, and (c) the generated predicted probability of the user purchase.   
     
     
         2 . The method of  claim 1 , wherein the training of the first machine learning model comprises identifying features, of the one or more features, relevant to the user's past activity on the computer system, and
 wherein the training of the second machine learning model comprises identifying features, of the one or more features, relevant to the price of the user purchase.   
     
     
         3 . The method of  claim 1 , wherein the training of the first machine learning model comprises identifying features, of the one or more features, relevant to a frequency of the user's activity on the computer system. 
     
     
         4 . The method of  claim 1 , wherein generating the predicted ROI value further comprises:
 determining, based on (a) the second plurality of user metrics and (b) the second machine learning model, the expected value of a purchase if made by the user; and   calculating the predicted ROI value by multiplying the expected value of the purchase by the predicted probability of the user purchase.   
     
     
         5 . The method of  claim 1 , wherein the first regression-based algorithm is a logistic regression model, and the second regression-based algorithm is a linear regression model. 
     
     
         6 . The method of  claim 1 , wherein the user purchase relates to a booking of a property listing viewed by the user via the computer system; and
 wherein the first set of feature-specific weightings weighs features corresponding to one or more of the following items more heavily than features that do not correspond to one or more of the following items: a) user account history, b) past or historical user activity, c) recent user activity, d) availability of a set of property listings viewed by the user, e) location of the user, and f) location of the set of property listings viewed by the user.   
     
     
         7 . The method of  claim 1 , wherein the user purchase relates to a booking of a property listing viewed by the user via the computer system; and
 wherein the second set of feature-specific weightings weighs features corresponding to one or more of the following items more heavily than features that do not correspond to one or more of the following items: a) location of a set of property listings viewed by the user, and b) a median price of the set of property listings viewed by the user.   
     
     
         8 . A system for obtaining a real-time computative prediction of a return on investment (ROI) value for a user based on user data provided by one or more servers over a network, the system comprising:
 a web server;   a memory configured to store a plurality of user data in a raw data format, the plurality of user data relating to a user's activity on the system, the plurality of user data being obtained from the web server; and   at least one processor configured to:
 transform the plurality of user data into a set of aggregated user metrics associated with the user's activity on the system within a window of time; 
 store the aggregated user metrics in the memory; 
 categorize the aggregated user metrics into one or more training signals; 
 generate a first set of weighted multipliers for the one or more training signals using a first regression-based algorithm; 
 train a first machine learning model based on the first set of weighted multipliers; 
 generate a second set of weighted multipliers for the one or more training signals using a second regression-based algorithm; 
 train a second machine learning model based on the second set of weighted multipliers; 
 apply the first machine learning model to the aggregated user metrics to calculate a predicted probability that the user will make a purchase; 
 apply the second machine learning model to the aggregated user metrics to calculate a predicted value of the purchase; and 
 generate the prediction of the ROI value for the user based on the predicted probability that the user will make the purchase and the predicted value of the purchase. 
   
     
     
         9 . The system of  claim 8 , wherein the at least one processor is further configured to:
 obtain real-time data relating to the user's activity on the system; and   store the real-time data in the memory,   wherein the first machine learning model is applied to (a) the aggregated user metrics and (b) the real-time data to calculate the predicted probability, and   wherein the second machine learning model is applied to (a) the aggregated user metrics and (b) the real-time data to calculate the predicted value.   
     
     
         10 . The system of  claim 8 , wherein generating the first set of weighted multipliers comprises identifying training signals relevant to the user's past activity on the system. 
     
     
         11 . The system of  claim 8 , wherein generating the first set of weighted multipliers comprises identifying training signals relevant to a frequency of the user's activity on the system. 
     
     
         12 . The system of  claim 8 , wherein generating the second set of weighted multipliers comprises identifying training signals relevant to a purchase price of the purchase. 
     
     
         13 . The system of  claim 8 , wherein the first regression-based algorithm is a logistic regression model, and the second regression-based algorithm is a linear regression model. 
     
     
         14 . The system of  claim 8 , wherein the first set of weighted multipliers weighs training signals corresponding to one or more of the following items more heavily than training signals that do not correspond to one or more of the following items: a) user account history, b) past or historical user activity, c) recent user activity, d) availability of a set of property listings viewed by the user, e) location of the user, and f) location of the set of property listings viewed by the user. 
     
     
         15 . The system of  claim 8 , wherein the second set of weighted multipliers weighs training signals corresponding to one or more of the following items more heavily than training signals that do not correspond to one or more of the following items: a) location of a set of property listings viewed by the user, and b) median price of the set of property listings viewed by the user. 
     
     
         16 . A method comprising:
 storing, in a memory, user data relating to a user's activity on a computer system;   storing, in a memory, user metrics associated with the user's activity on the computer system within a window of time, the user metrics being generated by aggregating a set of data of the stored user data;   identifying a first set of target variables directed to optimizing user acquisition;   training a first machine learning model to generate a predicted probability of a user action, using a first regression-based algorithm, based on the first set of target variables;   identifying a second set of target variables directed to optimizing revenue resulting from the user action;   training a second machine learning model, using a second regression-based algorithm, based on the second set of target variables;   generating the predicted probability of the user action, based on the application of the first machine learning model to the user metrics;   generating a predicted return on investment (ROI) value, based on the predicted probability of the user action and the application of the second machine learning model to the user metrics.   
     
     
         17 . The method of  claim 16 , further comprising:
 obtaining real-time data relating to the user's activity on the computer system,   wherein the predicted probability of the user action is generated based on the application of the first machine learning model to (a) the user metrics and (b) the real-time data, and   wherein the predicted ROI value is generated based on the application of the second machine learning model to (a) the user metrics and (b) the real-time data.   
     
     
         18 . The method of  claim 16 , wherein the first set of target variables directed to optimizing user acquisition relate to a frequency of the user's activity on the computer system. 
     
     
         19 . The method of  claim 16 , wherein the user action is the booking of a property rental via the computer system, and
 wherein the target variables directed to optimizing revenue relate to a price of the property rental.