A Machine Learning Approach to Segment the Customers of Online Sales Data for Better and Efficient Marketing Purposes

Abstract

The Internet is becoming huge and is used by a more diverse audience every day. The amount of data gathered from the platform through different online lead companies are gargantuan so it needs to be maintained and segregated in order to extract meaningful data from it. A lot of companies have started to gather customer data through their own platform or through various vendors who sell it to sales companies/organizations/individuals for some profit. Sometimes these data are large and scattered enough to even confuse big sales organizations. In order for better and more effective marketing of these sales data, We propose to use four machine learning clustering algorithms(K-Means, Agglomerative, Mean-Shift and DBSCAN) in order to find customer segments based on the data provided. Based on this segmented customer group, we can be able to find a pattern and decide which customer group is better for which business.

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