DETERMINING TARGET CUSTOMERS DURING MARKETING

• US 20140278967A1
• Filed: 03/13/2013
• Published: 09/18/2014
• Est. Priority Date: 03/13/2013
• Status: Abandoned Application

• Current Assignee: Ent Services Development Corporation LP (DXC Technology Company)
• Original Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Inventors: Misra, Subhasish, Saha, Abhisek, Pal, Biswajit
• Application Number:
  US13/801,712
• Publication Number:
  US 20140278967A1
• Time in Patent Office: Days
• US Class Current: 705/14.52
• CPC Class Codes: G06Q 30/0254 based on statistics

Abstract

A method of determining which customers to target during marketing is disclosed. Parameters are estimated from inputs of sample customers from statistical procedures, and an approximation model is created for each of the estimated parameters. The approximation models are applied to transaction data collect for customers on a customer list. For each customer, the transaction data is applied to the approximation models to determine a dropout probability and a transaction rate. A likelihood of repeat purchase is determined for each customer based on the dropout probability and the transaction rate.

Claims

1. A method of determining customers to target during marketing, comprising:

estimating parameters from inputs of sample customers and providing an approximation model for each of the estimated parameters;

receiving transactional data collected for a plurality of customers from a customer list;

for each customer from the customer list;

determining corresponding explanatory variables from the transaction data;

applying the explanatory variables to the approximation models to determine a dropout probability (p) and a transaction rate (λ

); and

determining a likelihood of a repeat purchase during a selected time period based on the dropout probability (p) and a transaction rate (λ

); and

scoring the customers from the customer list based on the likelihood of repeat purchase.

2. The method of claim 1 comprising targeting customers from the customer list determined to have a likelihood of repeating that is higher than a selected threshold amount.

3. The method of claim 1 wherein estimating parameters includes applying a Bayesian Hierarchal model and a Markov chain Monte Carlo algorithm to the inputs of the plurality of sample customers.

4. The method of claim 3 wherein the approximation model includes polynomial regression.

5. The method of claim 1 wherein the exemplary variables include a timing of a first purchase, a timing of a last purchase, and a number of purchases from the first purchase to the last purchase.

6. The method of claim 5 wherein inputs used to estimate the parameters include the exemplary variables.

7. The method of claim 5 wherein interaction variables are applied to the approximation models.

8. The method of claim 1 wherein determining the likelihood of a repeat purchase during the selected time period based on a product of:

a likelihood the customer will remain a customer as determined from the dropout probability (p), and

a likelihood the customer will make a purchase during the selected time period as determined from the transaction rate (λ

).

9. The method of claim 8 where determining the likelihood of a repeat purchase during the selected time period k for each customer j in the customer list is based on:

(1−

p_j)(1−

exp{−

kλ

_j}).

10. The method of claim 1 wherein the approximation model is applied to a plurality of different customer lists and to a plurality of different time periods.

11. A system for determining customers to target during marketing, comprising:

a first module configured to estimate parameters of dropout probability and transaction rate from inputs of sample customers and provide an approximation model for the dropout probability and an approximation model for transaction rate; and

a second module configured to receive transactional data collected for a plurality of customers from a customer list and configured to apply the transactional data to determine a likelihood of a repeat purchase during a selected time period based on the dropout probability and a transaction rate from the approximation models.

12. The system of claim 11 wherein the second module is configured to receive a plurality of different customer lists and a plurality of different time periods to be applied to the approximation models.

13. The system of claim 11 wherein the second module provides a scored customer list.

14. The system of claim 11 wherein the first module estimate parameters of dropout probability and transaction rate from inputs of sample customers based on a Bayesian Hierarchal model and a Markov chain Monte Carlo algorithm.

15. The system of claim 14 wherein the second module generates the approximation models from a polynomial regression of estimates of the parameters.

16. The system of claim 11 wherein the transactional data applied to the approximation model in the second module includes a timing of a first purchase, a timing of a last purchase, and a number of purchases from the first purchase to the last purchase for each customer on the customer list.

17. The system of claim 11 wherein the first and second modules are included as part of cloud computing system.

18. The system of claim 11 wherein the first module estimates parameters of dropout probability and transaction rate simultaneously with the second module applying the transactional data to determine a likelihood of a repeat purchase during a selected time period based on the dropout probability and a transaction rate from the approximation models.

19. A computer readable storage medium storing computer executable instructions for controlling a computing device to perform a process for determining customers to target during marketing, the process comprising:

estimating parameters of dropout probability and transaction rate from inputs of sample customers;

providing an approximation model for the dropout probability and an approximation model for transaction rate;

receiving transactional data collected for a plurality of customers from a customer list; and

applying the transactional data to determine a likelihood of a repeat purchase during a selected time period based on the dropout probability and a transaction rate from the approximation models.

20. The computer readable storage medium of claim 19 wherein applying the transactional data includes, for each customer from the customer list,

determining corresponding explanatory variables from the transaction data, the explanatory variables including,

a timing of a first purchase,

a timing of a last purchase, and

a number of purchases from the first purchase to the last purchase;

using the explanatory variables to determine a dropout probability transaction rate from the approximation models; and

determining a likelihood of a repeat purchase during a selected time period from a product of,

a likelihood the customer will remain a customer as determined from the dropout probability, and

a likelihood the customer will make a purchase during a further period of time as determined from the transaction rate.