Systems and Methods for Predicting Purchase Behavior Based on Consumer Transaction Data in a Geographic Location

• US 20170300948A1
• Filed: 04/18/2016
• Published: 10/19/2017
• Est. Priority Date: 04/18/2016
• Status: Active Application

• Current Assignee: Mastercard International Incorporated (MasterCard Incorporated)
• Original Assignee: Mastercard International Incorporated (MasterCard Incorporated)
• Inventors: Chauhan, Rohit, Hu, Po
• Application Number:
  US15/131,664
• Publication Number:
  US 20170300948A1
• Time in Patent Office: Days
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0205 Location or geographical consideration
• CPC Class Codes: G06Q 30/0259 based on store location

Abstract

Systems and methods are provided for use in predicting purchases in regions based on payment account transaction data from in and around the regions. One exemplary method includes generating a purchase propensity model based on historic transaction data and determining a recent sales value of the region based on a set of transactions that occurred in the region during a recent time interval. A set of consumer accounts that include transactions in the region during the recent time interval is determined. The computing device calculates consumer propensity scores for the consumer accounts based on the purchase propensity model. The propensity scores are combined into an overall purchase propensity for the region. The overall purchase propensity and recent sales value of the region are used to determine a predicted sales value of the region, whereby businesses in the region make business decisions confident that the predicted sales value is accurate.

Claims

1. A computer-implemented method for use in predicting purchases in a region, the method comprising:

generating, by a computing device, a purchase propensity model based on historic transaction data;

determining, by the computing device, a recent sales value of the region based on a set of transactions that occurred in the region during a recent time interval;

determining, by the computing device, a set of consumer accounts that include transactions in the region during the recent time interval;

calculating, by the computing device, propensity scores associated with each of the consumer accounts, said propensity scores being based on the purchase propensity model;

combining, by the computing device, the propensity scores of each of the consumer accounts into an overall purchase propensity; and

determining, by the computing device, a predicted sales value for the region based on the recent sales value of the region and the overall purchase propensity.

2. The computer-implemented method of claim 1, further comprising directing targeted advertising in the region based on the predicted sales value for the region.

3. The computer-implemented method of claim 1, further comprising:

determining at what location additional supplies will be needed in the region based on the forecasted sales value for the region; and

placing orders for the determined additional supplies.

4. The computer-implemented method of claim 1, wherein the purchase propensity model includes a propensity for consumers to make purchases at a first merchant within the region after recently making purchases at a second merchant within the region.

5. The computer implemented method of claim 4, wherein the first merchant is associated with a first merchant category and the second merchant is associated with a second merchant category different from the first merchant category.

6. The computer implemented method of claim 1, wherein the purchase propensity model is based on a relationship between a first merchant category and a second merchant category.

7. The computer implemented method of claim 6, wherein the first merchant category relates to an industry of a first merchant and the second merchant category relates to an industry of a second merchant.

8. A non-transitory computer readable storage media including executable instructions for predicting purchase propensity within a region based on payment account transaction data, which when executed by at least one processor, cause the at least one processor to:

access a purchase propensity model based on historic transaction data;

determine a recent sales value of the region based on a set of transactions that occurred in the region during a recent time interval;

determine a set of consumer accounts that include transactions in the region during the recent time interval;

apply the purchase propensity model to each of the consumer accounts to form consumer purchase propensities for each of the consumer accounts;

combine the consumer purchase propensities of each of the consumer accounts into an overall purchase propensity; and

determine a forecasted sales value for the region based on the recent sales value of the region and the overall purchase propensity.

9. The non-transitory computer readable storage media of claim 8, further including executable instructions, which when executed by the at least one processor, cause the at least one processor to direct targeted advertising in the region based on the forecasted sales value for the region, the targeted advertising including at least one of advertisements, offers, and coupons to mobile devices of consumers in the region.

10. The non-transitory computer readable storage media of claim 8, further including executable instructions, which when executed by the at least one processor, cause the at least one processor to access the historic transaction data and generate the purchase propensity model based on the historic transaction data.

11. The non-transitory computer readable storage media of claim 10, wherein the purchase propensity model includes a propensity for consumers to make purchases at merchants of a first merchant category after recently making purchases at merchants of a second, different merchant category.

12. The non-transitory computer readable storage media of claim 8, wherein the region includes a central region and regions within a predefined distance of the central region.

13. The non-transitory computer readable storage media of claim 8, further including executable instructions, which when executed by the at least one processor, cause the at least one processor to:

determine at what location additional supplies will be needed in the region based on the forecasted sales value for the region; and

place orders for the determined additional supplies.

14. The non-transitory computer readable storage media of claim 8, wherein the recent sales value indicates the occurrence of a special event in the region and the purchase propensity model includes a propensity that accounts for the occurrence of the special event, such that the forecasted sales value for the region accounts for the occurrence of the special event.

15. The non-transitory computer readable storage media of claim 8, wherein the purchase propensity model includes a propensity for consumers to make purchases at a second merchant within the region after recently making purchases at a first merchant within the region.

16. A system for use in predicting consumer purchasing behavior, the system comprising:

a memory; and

at least one processor in communication with the memory, the at least one processor configured to;

access historical transaction data for multiple consumers for a time interval prior to a target time and for a target region;

generate multiple purchase propensity models based on the historical transaction data and store the purchase propensity models in the memory, the purchase propensity models indicating likelihoods that the consumers will perform future transactions at particular merchants and/or at particular categories of merchants;

access current transaction data for a transaction made by a target one of the consumers at a merchant within the target region and within an interval after the target time; and

generate a propensity score for the target consumer based on the current transaction and at least one of the purchase propensity models, to thereby predict if the consumer will perform a future transaction at one of the particular merchants and/or at one of the particular categories of merchants, and store the propensity score in the memory.

17. The system of claim 16, wherein the at least one processor is further configured to:

access current transaction data for transactions made by multiple target consumers at within the target region and within the interval after the target time;

generate propensity scores for each of the target consumers based on the current transactions and at least one of the purchase propensity models and store each of the propensity scores in the memory; and

aggregate the propensity scores for each of the target consumers to thereby provide a regional propensity score for the target region, to thereby predict if one or more consumer will perform a future transaction within the target region.

18. The system of claim 17, wherein the at least one processor is further configured to:

generate a real-time micro geo-economics (MGE) measure, where the MGE measure includes a dynamic measure of retail business sales or sales potential in the target region at or before the target time; and

calculate a forecasted MGE measure for the target region, based on the real-time MGE measure the regional propensity score, the forecasted MGE measure including an overall predicted purchase propensity for the target region for the interval after the target time.

19. The system of claim 18, wherein the target region includes a central region and regions within a predefined distance of the central region.

20. The system of claim 19, further comprising a payment network configured to process purchase transactions by consumers to payment accounts, the at least one processor associated with the payment network.