Mining Online Customers' Data to Increase Shopper-to-Shopper Engine Recommendation Capabilities

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Online marketers are overwhelmed by the acquisition of quality shoppers at low cost for their Websites. To address this problem, Sociable Labs, a Montreal-based company, offers refer-a-friend programs for e-commerce websites in order to acquire customers through viral growth. The flagship product of the company is S2S ENGINETM which aims to build and optimize a marketer's shopper-to-shopperTM channel. Specifically, S2S ENGINETM exploits, for a specific e-commerce site, existing registered shoppers' emails and their social networks (Twitter and Facebook) in order to recommend the right connections to potential shoppers. Based on these recommendations, existing shoppers can start the referral process by inviting new customers. Note that new shoppers in turn will start inviting friends to join, and those friends will start inviting their friends, etc. Such a strategy allows the website to acquire quality shoppers at scale and with a low cost. One of the main objectives of Sociable Labs is to increase the number of new shoppers that each existing shopper is able to successfully convert (that is, maximize conversion). To this end, the company wants to increase S2S ENGINETM's recommendations capabilities by effectively exploring the huge amount of data that is continuously being gathered about customers in order to extract useful patterns pertaining to the maximization of conversion. To achieve this, the proposed project, which represent the first collaboration between Sociable Labs and the Université du Québec à Montréal, aims at extracting the most useful information from customers' data in order to get a better understanding of the intrinsic relationships between a customer's profile, purchasing activities and the conversion rate. Specifically, the goal of this project is to (1) elaborate a feature-based user profile model, (2) reveal any associative/causal relationship between an S2S ENGINETM recommendation, users' features, and conversion, (3) identify and select users' features pertaining to maximization of conversion, and (4) incorporate the selected features in a recommendation technique to increase S2S ENGINETM's recommendation capabilities.