Customer Segmentation and Buyer Targeting Approach
Yogesh Jadhav1, Deepa Parasar2
1Prof. Yogesh Jadhav Assistant Professor at Amity School of Engineering and Technology, Amity University Mumbai India.
2Dr. Deepa Parasar Associate Professor at Amity School of Engineering and Technology, Amity University Mumbai India.
Manuscript received on October 06, 2020. | Revised Manuscript received on October 25, 2020. | Manuscript published on November 30, 2020. | PP: 295-303 | Volume-9 Issue-4, November 2020. | Retrieval Number: 100.1/ijrte.D5013119420 | DOI: 10.35940/ijrte.D5013.119420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: K-Means, customer segmentation, Singular Value Decomposition, cold start problem.