Enrichment of affiliate marketing using K-means clustering algorithm for early stage affiliates

Abstract

At present, most people like to buy things online (i.e.) through e-commerce websites. People consider the advantages in online shopping like reduction in travel cost, time-consumption and offers, etc. Even though these advantages paves the way for e-commerce growth, it is not sufficient. The constant parameter that supports the growth of an e-commerce company is marketing. Marketing plays a major role in a product or idea promotion in both offline and online business. Suppose if the availability of the product belongs to a particular region, then direct marketing is more efficient for the product to meet its success. But shopping websites like Amazon, Flip kart, Snap deal etc., cannot implement direct marketing because those companies' boundaries are not limited to a particular region. In this case, affiliate marketing comes into the role which acts as an intermediate between the consumer and e-commerce companies. Affiliate marketing is one in which a person uses their marketing strategy to promote the e-commerce products in social network by becoming an affiliate to that company. Affiliates will be allocated with a unique referral link. If a person buys a product using that reference link then a commission will be paid to those affiliates by the respective company. In this paper, the K-Means Clustering algorithm is going to be implemented on a dataset with the help of a Pyspark environment that helps the affiliates for their marketing to reach the right customer with the right product.