Personal social networks are considered as one of the most influential sources in shaping a customer's attitudes and behaviors. However, the interactions with friends or colleagues in social networks of individual customers are barely observable in most e-commerce companies. In this paper, we study the problem of customer identification in social networks, i.e., connecting customer accounts at e-commerce sites to the corresponding user accounts in online social networks such as Twitter. Identifying customers in social networks is a crucial prerequisite for many potential marketing applications. These applications, for example, include personalized product recommendation based on social correlations, discovering community of customers, and maximizing product adoption and profits over social networks. We introduce a methodology CSI (Customer-Social Identification) for identifying customers in online social networks effectively by using the basic information of customers, such as username and purchase history. It consists of two key phases. The first phase constructs the features across networks that can be used to compare the similarity between pairs of accounts across networks with different schema (e.g. an e-commerce company and an online social network). The second phase identifies the top-K maximum similar and stable matched pairs of accounts across partially aligned networks. Extensive experiments on real-world datasets show that our CSI model consistently outperforms other commonly-used baselines on customer identification.