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

在线营销人员被以低成本为他们的网站收购优质购物者所淹没。到 为了解决这一问题，总部位于加州的Sociable Labs公司提供了一个朋友项目， 电子商务网站，以便通过病毒式增长获得客户。公司的旗舰产品 是S2 S ENGINETM，旨在建立和优化营销人员的购物者到购物者TM渠道。 具体来说，S2 S ENGINETM针对特定的电子商务网站利用现有注册购物者的电子邮件， 他们的社交网络（Twitter和Facebook），以推荐合适的连接到潜在的 购物者。根据这些建议，现有的购物者可以通过邀请新的 客户请注意，新的购物者反过来将开始邀请朋友加入，这些朋友将开始邀请 这样的策略使网站能够以低成本大规模地获得高质量的购物者。 社交实验室的主要目标之一是增加新的购物者的数量， 购物者能够成功转化（即，最大化转化）。为此，该公司希望 通过有效地探索大量数据，提高S2 S ENGINETM的推荐能力 这是不断收集有关客户，以提取有用的模式有关的 转换的最大化。为了实现这一目标，拟议的项目，这是第一次合作， Sociable Labs和蒙特利尔魁北克大学合作，旨在提取最有用的 从客户数据中获取信息，以便更好地了解 客户资料、采购活动和转化率。具体而言，本项目的目标是（1） 详细阐述基于特征的用户简档模型，（2）揭示S2 S之间的任何关联/因果关系 ENGINETM推荐、用户特征和转换，（3）识别和选择用户特征 以最大化转换，以及（4）将所选择的特征结合在推荐技术中， 增强S2 S ENGINETM的推荐功能。

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.