Abstract: A method and associated system of cross-retail marketing based on analysis of multichannel clickstream data that comprises a client application capturing, aggregating, and analyzing multiple clickstreams of a user. These clickstreams may be captured from multiple unrelated or competing sales or distribution channels and from multiple electronic platforms. The analysis may use methods of artificial intelligence, text analytics, semantic analytics, or other analytical methods to infer characteristics of the user, of the user's online commercial behavior and other commercial activities, and of products or services that the user may be interested in purchasing. The output of this analysis is forwarded to other channels or platforms visited by the user in order to allow those other channels or platforms to perform targeted commercial marketing functions related to the user's prior activities. In preferred embodiments, this method may be require an active consent or other authorization from the user.

Abstract: A method, system, and computer program product for assisting buying decisions using customer behavior analysis are provided in the illustrative embodiments. Product information comprising a set of product attributes is received about a grouping of products. Customer behavior information about a behavior of a customer is received from which a set of customer buying behavior factors is extracted. A customer buying behavior factor comprises an inferred preference of the customer for buying a product from the grouping of products. A weight is assigned to a customer buying behavior factor. A set of weighted customer buying behavior factors is mapped to a subset of the product attributes. At least one product is selected from the grouping of products such that the at least one product includes a subset of product attributes, and an overall weighted score of the at least one product exceeds a threshold.