November 30, 2020 Journal article Open Access

Yogesh Jadhav; Deepa Parasar

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    <subfield code="a">K-Means, customer segmentation, Singular Value Decomposition, cold start problem.</subfield>
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    <subfield code="u">Associate Professor at Amity School of Engineering  and Technology, Amity University Mumbai.</subfield>
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    <subfield code="p">International Journal of Recent Technology and Engineering (IJRTE)</subfield>
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    <subfield code="u">Assistant Professor at Amity School of  Engineering and Technology, Amity University Mumbai.</subfield>
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    <subfield code="a">Customer Segmentation and Buyer Targeting  Approach</subfield>
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    <subfield code="a">&lt;p&gt;Nowadays, maintaining customer loyalty and attention span of the customers are major challenges faced by the retail industry. The availability of varied options in the market for similar purposes increases the competition between organizations, to market their product, tremendously. This leads to the need for reinforcement of marketing strategies from time to time. With the advancement of technology, this can be made possible. This paper proposes a systematic approach for targeting customers and providing maximum profit to the organizations. An important initial step is to analyze the data of sales acquired from the purchase history and determine the parameters that have the maximum correlation. We focus on the parameters recency and frequency of the purchases made by the customers to perform clustering. Based on respective clusters, proper resources can be channeled towards profitable customers using machine learning algorithms. This paper also deals with the draw- backs of the recommender system like cold start problem, sparsity, etc and how they can be overcome. K-Means clustering is used for customer segmentation and Singular Value Decomposition is used for providing appropriate recommendations to the customers.&lt;/p&gt;</subfield>
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