SYSTEMS AND METHODS FOR RECOMMENDATION OF ELECTRONIC OFFERS

1 . A data processing system for managing electronic offers, the system comprising:

a first logic module adapted to receive a set of recommendation reference data comprising a plurality of items, the plurality of items including at least one product or service, and at least one offer;

a second logic module adapted to determine a set of association scores for the plurality of items based on the recommendation reference data; and

a third logic module adapted to select a recommended offer based on at least one association score.

2 . The data processing system of claim 1 , wherein the recommended offer is distributed in response to a request for a receipt.

3 . The data processing system of claim 1 , wherein the recommended offer is included in a transaction receipt transmitted to a customer data processing system.

4 . The data processing system of claim 3 , wherein the customer data processing system is at least one of: a desktop computer, laptop computer, netbook, electronic notebook, ultra mobile personal computer (UMPC), electronic tablet, client computing device, client terminal, client console, mobile telephone, smartphone, wearable computer, head-mounted computer, or personal digital assistant.

5 . The data processing system of claim 1 , wherein the recommendation reference data includes at least one of: a transaction log, a shopping list, a web clickstream for particular consumer entities, an offer clickstream for particular consumer entities, an offer impression log, an offer activation log, an offer redemption log, or a consumer behavioral log.

6 . The data processing system of claim 1 , wherein the determination of the set of association scores includes the computation of at least one metric, the metric being based on a set of itemsets, each itemset comprising at least two items, each of the at least two items item being any one of: a product, a service, or an offer.

7 . The data processing system of claim 6 , wherein each itemset comprises a plurality of items that co-occur in at least one of: a transaction log, a shopping list, a web clickstream for particular consumer entities, an offer clickstream for particular consumer entities, an offer impression log, an offer activation log, an offer redemption log, or a consumer behavioral log.

8 . The data processing system of claim 1 , wherein at least one association score is weighted.

9 . The data processing system of claim 8 , wherein at least one weight is adapted to increase offer-related revenue for an offer distributor, to increase an offer discount for a customer, or to increase a profit margin of a retailer or of an offer provider.

10 . The data processing system of claim 8 , wherein at least one weight is modified based on feedback from an offer redemption, an offer activation, or a purchase.

11 . The data processing system of claim 8 , wherein at least one weight is based on contextual transaction data or a historical transaction record.

12 . The data processing system of claim 11 , wherein the contextual transaction data or historical transaction record includes a location of a customer, a location of a transaction, an activity of a customer, or a time when a transaction takes place.

13 . The data processing system of claim 1 , wherein the selection of the recommended offer includes identifying a set of related items based on association scores.

14 . The data processing system of claim 13 , wherein the selection of the recommended offer further includes ranking the related items based on association scores.

15 . The data processing system of claim 1 , wherein the at least one association score is, or includes a universal scoring function.

16 . The data processing system of claim 15 , wherein the universal scoring function computes a universal score for an offer, wherein the universal score reflects one or more of: estimated revenue per offer activation, estimated revenue per offer impression, or estimated revenue per offer redemption.

17 . The data processing system of claim 15 , wherein the universal scoring function computes a universal score for an offer, wherein the universal score is global for all consumers, is relative to a particular consumer, or is relative to a group of consumers.

18 . The data processing system of claim 15 , wherein the universal scoring function is based on a comparison of the historical performance of an offer to average offer performance across a plurality of offers.

19 . The data processing system of claim 15 , wherein the universal scoring function is designed to maintain a predetermined distribution rate for an offer during a given period of time.

20 . The data processing system of claim 15 , wherein the universal scoring function is further adjusted to artificially increase the recommendation ranking of a particular offer.

21 . The data processing system of claim 1 , wherein the selection of the recommended offer includes applying a set of business rules.

22 . The data processing system of claim 1 , wherein the selection of the recommended offer includes at least one of: customer clustering and macro-personalization or recommendations based on customer demographics and transaction data, tailoring recommendations to users having similar behavior using collaborative filtering techniques, weighing recommendations based on seasonality or recency, or predictive filtering to anticipate a future customer purchase based on a historical purchase.

23 . The data processing system of claim 1 , wherein the selection of the recommended offer includes determining a list of offer recommendations on a periodic basis.

24 . The data processing system of claim 1 , wherein at least one association score is an item-to-item correlation score, offer-to-item correlation score, consumer-centric correlation score, item-to-event correlation score, event-to-event correlation score, consumer-to-event correlation score, or measure of the frequency of co-occurrence of a set of items.

25 . The data processing system of claim 1 , wherein the data processing system is further adapted to recommend a product or service.

26 . A method for managing electronic offers, the method comprising:

receiving a set of recommendation reference data comprising a plurality of items, the plurality of items including at least one product or service, and at least one offer;

determining a set of association scores for the plurality of items based on the recommendation reference data; and

selecting a recommended offer based on at least one association score;

wherein the method is performed by one or more computing devices.

27 . The method of claim 26 , further comprising including the recommended offer in a transaction receipt transmitted to a customer data processing system.

28 . The method of claim 26 , wherein the determination of the set of association scores includes the computation of at least one metric, the metric being based on a set of itemsets, each itemset comprising at least two items, each of the at least two items item being any one of: a product, a service, or an offer.

29 . The method of claim 28 , wherein each itemset comprises a plurality of items that co-occur in at least one of: a transaction log, a shopping list, a web clickstream for particular consumer entities, an offer clickstream for particular consumer entities, an offer impression log, an offer activation log, an offer redemption log, or a consumer behavioral log.

30 . One or more computer readable media storing program instructions adapted to manage electronic offers, wherein execution of the program instructions by a data processing system causes:

receiving a set of recommendation reference data comprising a plurality of items, the plurality of items including at least one product or service, and at least one offer;

determining a set of association scores for the plurality of items based on the recommendation reference data; and

selecting a recommended offer based on at least one association score.