DEMOGRAPHIC ANALYSIS USING TIME-BASED CONSUMER TRANSACTION HISTORIES

• US 20100280881A1
• Filed: 05/04/2010
• Published: 11/04/2010
• Est. Priority Date: 05/04/2009
• Status: Abandoned Application

• Current Assignee: Kevin P. Siegel, Patrick Faith, Zhongxiao Jiang
• Original Assignee: Kevin P. Siegel, Patrick Faith, Zhongxiao Jiang
• Inventors: Jiang, Zhongxiao, Faith, Patrick, Siegel, Kevin P.
• Application Number:
  US12/773,766
• Publication Number:
  US 20100280881A1
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: G06Q 20/40 Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
• CPC Class Codes: G06Q 20/4016 involving fraud or risk level assessment in transaction processing
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0204 Market segmentation
• CPC Class Codes: G06Q 30/0205 Location or geographical consideration
• CPC Class Codes: G06Q 30/0224 based on user history
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/12 Accounting

Abstract

Systems, apparatus, and methods for determining groups of similar consumers and for identifying a trend in consumer behavior are provided. Likelihoods of a transaction being initiated at various times by one consumer can be calculated based on previous transactions of the consumer. The likelihoods for different consumers can be used to determine a group of similar consumers as a demographic. The likelihoods of a transaction being initiated at various times by a consumer of a demographic (or other entity) can be used to forecast trends (such as a demand for a product) and make business decisions, such as for marketing campaigns, inventory levels (e.g. at particular stores or for all stores), pricing, and store locations. Such likelihoods when focused to a particular category of transactions can provide even greater accuracy.

Claims

1. A method of identifying a consumer as belonging to a particular demographic, the method comprising:

receiving first data associated with first transactions of a first entity and second data associated with second transactions of one or more second entities;

a computer system identifying one or more first patterns of the first previous transactions and identifying one or more second patterns of the second previous transactions, wherein each pattern includes a plurality of values, with at least two of the values respectively including contributions from transactions corresponding to different time ranges;

comparing the one or more first patterns to the one or more second patterns; and

based on the comparison, determining whether the first entity and the one or more second entities belong to a same demographic.

2. The method of claim 1, wherein the first entity is a first consumer and the one or more second entities are one or more second consumers.

3. The method of claim 2, wherein the first consumer belongs to the same demographic at less than one hundred percent.

4. The method of claim 3, further comprising:

comparing the first patterns to other patterns associated with other demographics; and

determining whether the first consumer belongs to the other demographics.

5. The method of claim 4, further comprising:

defining the first consumer as a linear combination of the patterns of the demographics to which the first consumer belongs.

6. The method of claim 2, further comprising:

sending an incentive for a first transaction to the first consumer, wherein the incentive is sent at a time correlated to when the first transaction is likely for the one or more second consumers; and

wherein determining whether the first entity and the one or more second entities belong to the same demographic includes determining whether the first consumer uses the incentive when the first transaction is likely for the one or more second consumers.

7. The method of claim 1, wherein a value of each pattern corresponds to a likelihood of a transaction occurring within a specified time range.

8. The method of claim 1, wherein identifying one or more patterns of transactions includes:

associating one or more keys with each previous transaction;

correlating pairs of previous transactions, each correlated pair associated with a particular pair of keys; and

for each correlated pair, determining time intervals between the transactions of the correlated pair; and

for each key pair;

tracking numbers of occurrences of correlated pairs having time intervals within specified time ranges, the transactions of the correlated pairs being associated with corresponding keys of the key pair.

9. The method of claim 8, wherein comparing the one or more first patterns to the one or more second patterns includes comparing a portion of first patterns associated with pairs of keys that match with pairs of keys associated with a portion of the second patterns, the method further comprising:

identifying first patterns associated with pairs of keys that do not match with pairs of keys associated with the second patterns,

wherein determining whether the first entity and the one or more second entities belong to a same demographic includes analyzing the non-matching patterns.

10. The method of claim 1, wherein comparing the one or more first patterns to the one or more second patterns includes determining a difference between corresponding values of matching patterns between the first entity and the one or more second entities;

and

comparing a sum of the differences to a threshold value.

11. The method of claim 1, wherein comparing the one or more first patterns to the one or more second patterns includes:

multiplying values of a first pattern with corresponding values of a matching second pattern to obtain a plurality of resulting values; and

analyzing the resulting values to determine whether the first consumer and the one or more second consumers belong to a same demographic.

12. The method of claim 11, wherein analyzing the resulting values is performed with a neural network.

13. A computer program product comprising a tangible computer readable medium storing a plurality of instructions for controlling one or more processors to perform the method of claim 1.

14. A computer system comprising:

one or more processors; and

the computer program product of claim 13.

15. A method of identifying a trend in consumer behavior, the method comprising:

receiving data associated with previous transactions of an entity;

a computer system determining one or more patterns of the previous transactions, wherein each pattern includes a plurality of values, with at least two of the values respectively including contributions from transactions corresponding to different time ranges;

determining likelihoods for an occurrence of a transaction according to the one or more patterns, each likelihood at a respective one of a plurality of different times; and

identifying a trend in occurrences of the transaction based on the likelihoods at the plurality of different times.

16. The method of claim 15, wherein the entity is a group of similar consumers.

17. The method of claim 15, wherein the entity is a merchant or a group of similar merchants.

18. The method of claim 15, further comprising:

selecting one or more relevant patterns from the determined patterns based on a product and/or a merchant associated with the transaction, wherein the likelihoods are determined from the relevant patterns.

19. The method of claim 15, wherein determining the one or more patterns of the previous transactions includes:

associating one or more keys with each previous transaction;

correlating pairs of previous transactions; and

determining time intervals between correlated pairs of previous transactions; and

tracking the occurrences of pairs of correlated previous transactions having time intervals within specified time ranges for a plurality of pairs of keys.

20. The method of claim 15, further comprising:

adjusting one or more characteristics of a product being sold based on the identified trend.

21. The method of claim 20, wherein the characteristic includes a number of items of the product and/or a price of the product.

22. The method of claim 21, wherein the number of items is adjusted by changing inventory levels of the product at a particular location.

23. The method of claim 15, wherein identifying a trend in occurrences of the transaction based on the likelihoods at the plurality of different times includes determining a likelihood above an upper threshold value or below a lower threshold value.

24. The method of claim 15, wherein determining likelihoods for an occurrence of a transaction according to the one or more patterns includes determining a probability distribution as a function of time from the one or more patterns, and wherein the trend is identified from changes in the probability distribution from one time to another.

25. The method of claim 15, wherein determining likelihoods for an occurrence of a transaction according to the one or more patterns includes determining a probability distribution as a function of time from the one or more patterns, the method further comprising:

determining one or more other probability distributions from one or more other patterns associated with the entity, wherein the other probability distributions are determined at calculation times different than the probability distribution;

calculating a difference between the probability distribution and the one or more other probability distributions, wherein the trend is identified based on the difference.

26. A computer program product comprising a tangible computer readable medium storing a plurality of instructions for controlling one or more processors to perform the method of claim 15.

27. A computer system comprising:

one or more processors; and

the computer program product of claim 26.