Analyzing Target Customer Behavior Using Data Mining Techniques for E- Commerce Data
Journal Title: International Journal of Innovative Research in Computer Science and Technology - Year 2014, Vol 2, Issue 1
Abstract
In this scenario Customer satisfaction is no longer satisfied with a simple listing of marketing contacts, but wants detailed information about Customers past purchase as well as prediction of future purchases. From long period of ago, information of customer is most important for every business. With the help of advance information technology, firms are able to collect and store mountains of data describing infinite offering and different type of customer profile. With the help of this information we are able to find customer needs and wants. Electronic commerce or ecommerce is a term for any type of business, or commercial transaction that involves the transfer of information across the Internet. It covers a range of different types of businesses, from consumer based retail sites, through auction or music sites, to business exchanges trading goods and services between corporations. It is currently one of the most important aspects of the Internet to emerge. Ecommerce allows consumers to electronically exchange goods and services with no barriers of time or distance. Electronic commerce has expanded rapidly over the past five years and is predicted to continue at this rate, or even accelerate. Traditional forecasting methods are no longer suitable for these business situations. For that type of business we are able to use the principles of data mining concept. With the help of data mining principle huge amount of customer information into cluster customer segments by using K-Means algorithm which is used to cluster observations into groups of related observations without any prior knowledge of those relationships and data from web log of various ecommerce websites.
Authors and Affiliations
Dattatray V. Bhate, M. Yaseen Pasha
Keywords
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