Ecommerce transactions are no longer the new thing. Many people are shopping with ecommerce and many companies using
ecommerce to promote and to sell their product. Because of that, an overloading information appears on customer side.
Overloading information occurs when customer gets much information about the product then feel confused. Personalization
will become a solution to overloading problem. On marketing, personalization technique can be used to get potential
customers in a case to boost sales. The potential customer obtains from customer segmentation or market segmentation. This
paper will review customer segmentation from data, methods and process from customer segmentation research. Data for
customer segmentation is divided into internal data and external data. Customer profile, purchase history as internal data and
server log, cookies, survey data as external data. This data can be processed using one of several methods: Business Rule,
Magento, Customer Profiling, Quantile Membership, RFM Cell Classification Grouping, Supervised Clustering, Customer
Likeness Clustering, Purchase Affinity Clustering and Unsupervised Clustering. In this paper that methods classified into
Simple technique, RFM technique, Target technique, and Unsupervised technique and the process was generalized become
determine business objective, collect data, data preparation, analyze variable, data processing, and performance evaluation.
Customer behavior in accessing ecommerce, when viewing a product on ecommerce are recorded in server log with time.
Duration when seeing the product can be used as customer interest in the product so that it can be used as a variable in
customer segmentation.
Keywords: Ecommerce, Customer Segmentation, Personalization