Determining targeted incentives based on consumer transaction history

• US 9,727,868 B2
• Filed: 05/04/2010
• Issued: 08/08/2017
• Est. Priority Date: 05/04/2009
• Status: Active Grant

• Current Assignee: Visa International Service Association (Visa, Inc.)
• Original Assignee: Visa International Service Association (Visa, Inc.)
• Inventors: Faith, Patrick, Siegel, Kevin P.
• Primary Examiner(s): Fleischer, Mark A
• Application Number:
  US12/773,765
• Publication Number:
  US 20100280880A1
• Time in Patent Office: 2,653 Days
• Field of Search: 705 731, 705 1453, 705 1425, 705 1413, 705 1466, 705 141, 705 729, 705 1441, 705 1449, 705 1452, 705 1458
• 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 incentives based on consumer history. When, how, and to whom incentives are sent can be determined. For example, an incentive can be sent to a consumer to encourage a transaction at a time when the particular consumer is predisposed to initiate the transaction. Also, an incentive for a transaction can be sent to a consumer when that transaction has a high likelihood of leading to other transactions. An incentive can also be sent after a consumer initiates a transaction that is known to not have many subsequent transaction correlated to it.

Claims

1. A method of providing an incentive to a consumer, the method comprising:

receiving data corresponding to a plurality of previous transactions associated with the consumer, the previous transactions occurring prior to the data being received;

a computer system determining one or more patterns of the previous transactions, wherein the determining of the one or more patterns of the previous transactions includes;

associating one or more keys with each previous transaction, each of the one or more keys corresponding to a particular type of 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 in a table, the transactions of the correlated pairs being associated with corresponding keys of the key pair;

based on the determined patterns of the previous transactions, determining a likelihood for a future transaction having a particular key at a plurality of future times, each likelihood being determined by evaluating the number of occurrences of the previous transactions for the respective time in one or more tables corresponding to the particular key at a different time;

for each of the plurality of future times, comparing the likelihood to a first threshold;

based on the comparisons of the likelihoods at the plurality of future times to the first threshold, predicting a time window when the consumer is likely to initiate the future transaction by identifying when the likelihoods are above the first threshold, wherein a duration of the time window is variable, with a start of the time window based on when the likelihood at a first future time rises above the first threshold and an end of the time window based on when the likelihood at a second future time falls below the first threshold; and

sending an incentive associated with the future transaction to the consumer such that the consumer receives the incentive at a time correlated with the predicted time window.

2. The method of claim 1, further comprising:

specifying specific values for categories associated with the future transaction, wherein the time window is when the consumer is likely to initiate the future transaction having the specified values.

3. The method of claim 1, wherein the time window is not of a predetermined duration.

4. The method of claim 1, wherein the previous transactions are associated with the consumer when they are initiated by one or more other consumers identified as having data that matches data of the consumer.

5. The method of claim 4, wherein the time window when the consumer is likely to initiate the future transaction is based on a likelihood of the one or more other consumers initiating the future transaction.

6. The method of claim 1, wherein the computer system determining a pattern of the previous transactions includes tracking a plurality of likelihood values of the future transaction, each likelihood value corresponding to a different frequency for the occurrence of the future transaction.

7. The method of claim 1, wherein the start of the time window is at a specified amount of time relative to the first future time when the likelihood is above the first threshold.

8. The method of claim 1, wherein predicting the time window further includes identifying a future time when the likelihood is below a second threshold and above the first threshold.

9. The method of claim 1, further comprising:

predicting a merchant at which the future transaction is to occur, wherein the incentive is for a first product that is available at the merchant, wherein the first product is a different product than a product predicted for the future transaction.

10. The method of claim 1, wherein the consumer receives the incentive during the predicted time window.

11. The method of claim 1, wherein the incentive is sent at a predetermined time before the predicted time window.

12. The method of claim 1, wherein the incentive is valid only during the predicted time window.

13. The method of claim 1, further comprising:

after predicting a time window when the consumer is likely to initiate a future transaction, determining a cost associated with the future transaction or a plurality of incentives;

determining an incentive to send based on an analysis of a likelihood of the future transaction and the cost.

14. The method of claim 1, wherein sending the incentive for the transaction to the consumer includes sending an electronic message to the consumer, wherein the electronic message is selected from the group consisting of an SMS message, an MMS message, and an e-mail.

15. The method of claim 1, further comprising:

determining a peak time of the plurality of future times where the likelihood is a maximum, wherein the time window is correlated to the peak time.

16. The method of claim 15, wherein the time window is centered around the peak time.

17. A computer product comprising a non-transitory computer readable medium storing a plurality of instructions for controlling one or more processors to provide an incentive to a consumer, the instructions comprising:

receiving data corresponding to a plurality of previous transactions associated with the consumer, the previous transactions occurring prior to the data being received;

a computer system determining one or more patterns of the previous transactions, wherein the determining of the one or more patterns of the previous transactions includes;

associating one or more keys with each previous transaction, each of the one or more keys corresponding to a particular type of 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 in a table, the transactions of the correlated pairs being associated with corresponding keys of the key pair;

based on the determined patterns of the previous transactions, determining a likelihood for a future transaction having a particular key at a plurality of future times, each likelihood being determined by evaluating the number of occurrences of the previous transactions for the respective time in one or more tables corresponding to the particular key a probability function at a different time;

for each of the plurality of future times, comparing the likelihood to a first threshold;

based on the comparisons of the likelihoods at the plurality of future times to the first threshold, predicting a time window when the consumer is likely to initiate the future transaction by identifying when the likelihoods are above the first threshold, wherein a duration of the time window is variable, with a start of the time window based on when the likelihood at a first future time rises above the first threshold and an end of the time window based on when the likelihood at a second future time falls below the first threshold; and

sending an incentive associated with the future transaction to the consumer such that the consumer receives the incentive at a time correlated with the predicted time window.

18. The computer product of claim 17, further comprising:

one or more processors.

19. A method of providing an incentive to a consumer, the method comprising:

receiving data corresponding to a plurality of previous transactions associated with the consumer, the previous transactions occurring prior to the data being received;

determining, by a computer system, one or more patterns of the previous transactions, wherein determining one or more patterns of the previous transactions includes;

associating one or more keys with each previous transaction, each of the one or more keys corresponding to a particular type of transaction, and

correlating a first group of previous transactions to previous transactions of a first type associated with a first key, the transactions of the first group occurring after the respective transactions of the first type, each correlated pair of transactions associated with a particular key pair;

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

for each key pair, tracking numbers of occurrences of correlated transactions having time intervals within specified time ranges in a table;

determining, by the computer system, a likelihood of any transaction within a time window occurring after a transaction of the first type initiated by the consumer, the likelihood being determined by evaluating the number of occurrences of correlated transactions having time intervals within the time window in one or more tables corresponding to the first key; and

sending, to the consumer, an incentive for a future transaction of the first type based on the likelihood being greater than a threshold.

20. The method of claim 19, wherein a transaction of the first type is for a specific merchant.

21. The method of claim 19,

wherein the incentive is sent when the number of correlated transactions having time intervals within a specified range is above a threshold.

22. The method of claim 19, further comprising;

increasing a value when a correlated transaction occurs after a transaction of the first type, the value indicating the likelihood of any transaction within the time window occurring after the transaction of the first type initiated by the consumer.

23. The method of claim 22, further comprising:

decreasing the value when a decline of a correlated transaction occurs after a transaction of the first type.

24. The method of claim 22, further comprising:

decreasing the value when a correlated transaction does not occur after a transaction of the first type during each of one or more time periods.

25. The method of claim 22, wherein the value is an imaginary part of a complex number, wherein the real part determines a likelihood of a transaction of the first type occurring.

26. The method of claim 25, wherein the value is associated with a particular time of a transaction of the first type occurring, and wherein the incentive is sent at a time correlated with the particular time.

27. The method of claim 26, wherein the correlated transactions are correlated specifically to a transaction of the first type occurring at the particular time.

28. A method of providing an incentive to a consumer, the method comprising:

receiving data corresponding to a plurality of previous transactions associated with the consumer, the previous transactions occurring prior to the data being received;

determining, by a computer system, one or more patterns of the previous transactions, wherein determining one or more patterns of the previous transactions includes;

associating one or more keys with each previous transaction, each of the one or more keys corresponding to a particular type of transaction, and

correlating a first group of previous transactions to previous transactions of a first type associated with a first key, the transactions of the first group occurring after the respective transactions of the first type, each correlated pair of transactions associated with a particular key pair;

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

for each key pair, tracking numbers of occurrences of correlated transactions having time intervals within specified time ranges in a table;

determining, by the computer system, an amount of transactions that are correlated to transactions of the first type initiated by the consumer and that occur after the transactions of the first type, the amount of transactions determined based on the number of occurrences of correlated transactions in one or more tables corresponding to the first key; and

after a transaction of the first transaction type occurs, sending an incentive for any transaction based on the amount of transactions that are correlated to the first transaction type being below a threshold.

29. The method of claim 28, wherein the incentive is for a merchant near the merchant for the transaction of the first type.

30. The method of claim 28, wherein the incentive is for a future transaction that is likely to occur after a transaction of the first transaction type by a group of consumers that are identified as having data that matches data of the consumer.

31. The method of claim 30, further comprising:

determining a time window relative to a transaction of the first transaction type in which the future transaction is likely for the group of consumers by identifying when a likelihood of the future transaction is above a threshold, wherein the incentive is sent at a time that is correlated to the time window relative to the occurrence of the transaction of the first transaction type by the consumer.