GENERATING CUSTOMER PROFILES USING TEMPORAL BEHAVIOR MAPS

• US 20150161623A1
• Filed: 12/10/2013
• Published: 06/11/2015
• Est. Priority Date: 12/10/2013
• Status: Active Application

• Current Assignee: Fair Isaac Corporation
• Original Assignee: Fair Isaac Corporation
• Inventors: Rahman, Shafi Ur, Agrawal, Rakhi, Sowani, Amit Kiran, Kaur, Manmeet, Bajaj, Arjun
• Application Number:
  US14/102,374
• Publication Number:
  US 20150161623A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 40/12 Accounting

Abstract

A customer profile is generated for a customer performing one or more transactions using temporal behavior maps in order to determine a score that can be used by an entity to determine offers for the customer. The temporal behavior maps can characterize a purchase behavior of the customer. Related apparatus, systems, techniques and articles are also described.

Claims

1. A method comprising:

receiving, by one or more back-end server computers, transaction data of one or more transactions performed by a customer, each computer comprising memory and at least one data processor;

generating, by the one or more back-end server computers and based on the transaction data, a plurality of temporal behavior maps characterizing purchase behavior of the customer; and

generating, by the one or more back-end server computers, a customer profile of the customer based on the plurality of temporal behavior maps, the customer profile being used by a decision engine to generate a decision for the customer, the decision engine comprising at least one data processor connected to the one or more back-end computers.

2. The method of claim 1, wherein:

the one or more back-end server computers comprise an aggregator module and a generator module that is connected to the aggregator module, the decision engine being connected to the generator module;

the aggregator module receives the transaction data of the one or more transactions performed to generate the plurality of temporal behavior maps; and

the generator module receives the plurality of temporal behavior maps to generate the customer profile.

3. The method of claim 2, wherein the aggregator module receives the transaction data in real-time when the customer performs a new transaction.

4. The method of claim 2, wherein the aggregator module receives the transaction data in a batch comprising a plurality of transactions previously performed by the customer.

5. The method of claim 2, wherein the aggregator module receives the transaction data in batches of continuously updating transactions performed by the customer in real-time.

6. The method of claim 1, wherein the one or more transactions are performed by the customer on a front end client computer connected to the one or more back-end server computers via a communication network.

7. The method of claim 1, wherein the transaction data comprises an identifier of the customer, period of each transaction, one or more products purchased by the customer in each transaction, a price of each purchased product, quantity of products purchased by the customer, transaction type of each transaction performed by the customer, and merchant category code of one or more merchants associated with each transaction.

8. The method of claim 1, wherein the plurality of temporal behavior maps comprise one or more of at least a frequency base map, a recency base map, a price base map, a total products base map, a unique products base map, a visits base map, a stock keeping unit (SKU) base map, and a date base map.

9. The method of claim 8, wherein the frequency base map is represented as a map of {period:

{product1;

frequency, product2;

frequency, . . . }}.

10. The method of claim 8, wherein the recency base map is represented as a map of {product:

{list of periods in which the product is purchased}}.

11. The method of claim 8, wherein the price base map is represented as a map of {period:

total money spent in the time period, . . . }.

12. The method of claim 8, wherein the total products base map is represented as a map of {period:

total number of products purchased, . . . }.

13. The method of claim 8, wherein the unique products base map is represented as a map of {period:

number of unique products purchased, . . . }.

14. The method of claim 8, wherein the visits base map is represented as a map of {period:

total number of customer visits, . . . }.

15. The method of claim 8, wherein the stock keeping unit (SKU) base map is represented as a map of {period:

list of unique SKUs purchases by customer, . . . }.

16. The method of claim 8, wherein the date base map is represented as a map of {period:

date of most recent purchase, . . . }.

17. The method of claim 2, wherein the generator module comprises a frequency generator, a recency generator, and a transaction variables generator.

18. The method of claim 17, wherein:

the frequency generator generates one or more frequency variables based on a frequency base map of the plurality of temporal behavior maps;

the recency generator generates one or more recency variables based on a recency base map; and

the transaction variables generator generates one or more transaction variables based on at least one of a price base map, a total products base map, a unique products base map, a visits base map, a stock keeping unit (SKU) base map, and a date base map of the plurality of temporal behavior maps.

19. The method of claim 18, wherein the customer profile is characterized by at least the one or more frequency variables, the one or more recency variables, and the one or more transaction variables.

20. The method of claim 1, wherein the decision engine is a scoring engine, and the decision is a score generated by the scoring engine.

21. The method of claim 20, wherein the score is used by one of a financial institution and a retailer to make an offer to the customer.

22. The method of claim 21, wherein the offer comprises one of:

an increase in a credit line of a credit card of the customer, and discounts on one or more products.

23. A non-transitory computer program product storing instructions that, when executed by at least one programmable processor forming a part of at least one computing device, cause the at least one programmable processor to perform operations comprising:

receiving a customer profile of a customer characterizing one or more behavioral characteristics of the customer, the customer profile being based on one or more temporal behavior maps characterizing a purchase behavior of the customer over discrete periods of time, the one or more temporal behavior maps being based on one or more transactions performed by the customer; and

generating, based on the customer profile, a decision for the customer for use by an entity to make one or more offers to the customer.

24. The computer program product of claim 23, wherein the entity is one of a financial institution and a retailer.

25. The computer program product of claim 23, wherein the one or more offers comprise one or more of:

an increase in credit line of a credit card of the customer, and discounts on one or more products.

26. A system comprising:

a client computing system comprising at least one hardware data processor and a storage memory device, the client computing system being used by a customer to perform one or more transactions; and

one or more back-end server computers connected to the client computing system via a communication network, the one or more back-end server computers receiving transaction data of the one or more transactions performed on the client computing system, the one or more back-end server computers generating a plurality of temporal behavior maps based on the transaction data, the temporal behavior maps characterizing a purchase behavior of the customer, the one or more back-end server computers generating a customer profile of the customer based on the plurality of temporal behavior maps, the customer profile being used by a decision engine to generate a decision for the customer, the decision engine comprising at least one data processor connected to the one or more back-end computers.

27. The system of claim 26, wherein:

the one or more back-end server computers comprise an aggregator module and a generator module that is connected to the aggregator module, the decision engine being connected to the generator module;

the aggregator module receives the transaction data of the one or more transactions performed to generate the plurality of temporal behavior maps; and

the generator module receives the plurality of temporal behavior maps to generate the customer profile.