Abstract: A system and method for scalable, adaptive, real-time generation of personalized offers is disclosed. A profile of a user is generated, the profile being a summarized representation of historical behavior of the user, the profile containing recursively updated variables. The profile is updated for each new transaction and/or a time dependent event, the new transaction and/or time dependent event including purchase transaction data, user, item hierarchy, and offer data. A affinity scores is generated for the user based on the updated profile, and for each new transaction, one or more offers are generated for the user based on the updated user profile and the affinity scores.

Abstract: Data for a plurality of entities that can be offered a plurality of products can be obtained. The data can include categorical data and numeric data. Based on business constraints, some of all of the data can be selected. The selected data can be converted to another set of numeric data, wherein the categorical values are converted to numeric values. Dimensions of the converted data can be reduced to generate another set of data. Based on this another set of data, clusters of entities can be formed. The products can be grouped by assigning a unique product identifier of each product to a corresponding cluster. This grouping of products can be used by a predictive model to predict a likelihood of an entity to purchase a particular product in a future time period. Related methods, apparatus, systems, techniques and articles are also described.

Abstract: The current subject matter relates to generation of relevant real-time offers based on global positioning system (GPS) data of an individual. A mobile device of an individual can record the GPS data of the individual. The mobile device can be connected to a central system. The central system can receive the recorded GPS data. The central system can predict, by using a trained predictive model and based on transaction history of the individual and the GPS data, categories of likely purchases by the individual. The central system can generate or reproduce offers from merchants of the predicted categories that are located within a threshold distance from a current location of the individual. The central system can send the generated offers to the mobile device that can display the generated offers in real-time. Other applications can include improving relevance of batch offers and/or real-time offers based on a recent purchase trigger.

Abstract: A system and method for scalable, adaptive, real-time generation of personalized offers is disclosed. A profile of a user is generated, the profile being a summarized representation of historical behavior of the user, the profile containing recursively updated variables. The profile is updated for each new transaction and/or a time dependent event, the new transaction and/or time dependent event including purchase transaction data, user, item hierarchy, and offer data. A affinity scores is generated for the user based on the updated profile, and for each new transaction, one or more offers are generated for the user based on the updated user profile and the affinity scores.

Abstract: Data for a plurality of entities that can be offered a plurality of products can be obtained. The data can include categorical data and numeric data. Based on business constraints, some of all of the data can be selected. The selected data can be converted to another set of numeric data, wherein the categorical values are converted to numeric values. Dimensions of the converted data can be reduced to generate another set of data. Based on this another set of data, clusters of entities can be formed. The products can be grouped by assigning a unique product identifier of each product to a corresponding cluster. This grouping of products can be used by a predictive model to predict a likelihood of an entity to purchase a particular product in a future time period. Related methods, apparatus, systems, techniques and articles are also described.

Abstract: Profiles characterizing each of a plurality of consumers are received. Thereafter, each profile is associated with one of a plurality of customer segments (e.g., matched pairs, etc.). Thereafter, a coupon effectiveness index is determined for each of the plurality of consumers for an offering based on the associated customer segment. The coupon effectiveness indices model characterizes causal effects estimates determined using historical data of purchases of individuals having varying coupon treatments for the offering. Subsequently, provision of at least a portion of the determined coupon effectiveness indices is initiated. Related apparatus, systems, techniques and articles are also described.

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.

Abstract: The current subject matter relates to generation of relevant real-time offers based on global positioning system (GPS) data of an individual. A mobile device of an individual can record the GPS data of the individual. The mobile device can be connected to a central system. The central system can receive the recorded GPS data. The central system can predict, by using a trained predictive model and based on transaction history of the individual and the GPS data, categories of likely purchases by the individual. The central system can generate or reproduce offers from merchants of the predicted categories that are located within a threshold distance from a current location of the individual. The central system can send the generated offers to the mobile device that can display the generated offers in real-time. Other applications can include improving relevance of batch offers and/or real-time offers based on a recent purchase trigger.

Abstract: The current subject matter describes a generation of a score based on an enhanced market basket analysis (eMBA). An eMBA model can receive historical data characterizing historical purchases of a plurality of products over a specified time-period. In response, the eMBA model can generate baskets, which can include data that is causal and predictive. The generated baskets can be provided as an input to a group generator. The group generator can then generate product groups and confidence values. The product groups and confidence values can be provided to a score generator. In run-time, the score generator can receive current product data, and in return, can use the product groups and confidence values to generate a score. The score can characterize a likelihood of a purchase of the product by a corresponding customer associated with the product group. Related methods, apparatuses, systems, techniques and articles are also described.

Abstract: Profiles characterizing each of a plurality of consumers are received. Thereafter, each profile is associated with one of a plurality of customer segments (e.g., matched pairs, etc.). Thereafter, a coupon effectiveness index is determined for each of the plurality of consumers for an offering based on the associated customer segment. The coupon effectiveness indices model characterizes causal effects estimates determined using historical data of purchases of individuals having varying coupon treatments for the offering. Subsequently, provision of at least a portion of the determined coupon effectiveness indices is initiated. Related apparatus, systems, techniques and articles are also described.

Abstract: The current subject matter provides the ability to infer a richer customer profile using clickstream data obtained in connection with the traversal of a website by a customer. In some cases, this clickstream data is used in connection with in-store point of sale data and inputted into a Time to Event scorecard model in order to identify transactions (e.g., offerings, campaigns, etc.) to be initiated. Related apparatus, systems, techniques and articles are also described.

Abstract: The current subject matter provides the ability to infer a richer customer profile using purchase transaction data in conjunction with various hierarchical groupings of products as well as an ability to characterize products such that they can be used to enrich customer profiles. Related apparatus, systems, techniques and articles are also described.