Application Of Data Mining In E-Retailing

In this Era of digital Communications and Information, a vast majority of interactions and operations that were, previously, made in person, are now made through the Internet. A specific example of this is the basic economic activity of goods retailing, the simple buy and sell process has been transferred to networks, servers and websites that are able to advertise their products and receive payment for them on line. Internet Retailing Stores owners has to find a way to "Personalized" or to recommend their clients, the products that they are most likely to buy, according to their age, gender, location, social status, among other parameters.This paper shows, using a real example from an E-Retailing Website, on how this recommendation process occurs, starting from the collection of data, such as the customer information provided during registration process; and the products purchased by these customers in a time interval of three years, passing then, through a data cleaning and management step; finalizing in the data input, mining and analysis by the SPSS Statistical tool. The result of all of this process, using Association rules and Time Series Analysis, gives the proper tools and information to the Website administrators to optimize their advertising performance, to make the right decisions related to the future campaigns; and to recommend the most probable products that each of the customers would purchase, depending in their past purchasing behaviours.