Market Segmentation, Targeting, and Positioning Using Machine Learning

Authors

  • Harish Paruchuri Anthem, Inc.

Keywords:

Big data mining, machine learning, k-Mean algorithm, customer segmentation

Abstract

The occurrence of numerous rivals and industrialists has created a lot of pressure between rivalry companies to go in search of new customers and at the same time working to maintain the existing ones. Owing to this, the necessity for a new customer service policy or strategy becomes essential irrespective of the business size. Moreover, the capability of any company that wants to remain in existence and growth as well needs to understand its clients and also delivers necessary customer support to make available targeted client services and develop branded clients’ service policy. This understanding is conceivable via organized client service. The respective segment devises consumers who share similar market potentials. Big data philosophies and machine learning devise appropriate means of promoting better recognition and approval of computerized client segmentation methods in good turn of outdated business analytics that frequently lead to any meaningful approach that can keep or source for a new customer while the client disreputable is very growing by the day. This study makes use of the k-means clustering algorithm for this determination. The Sklearn public library was established for the k-Means algorithm and the package is competent by means of a 100-model two-parameters dataset generated or collected from the retail trade. Features of an average figure of client buying and average figure of periodic consumers.

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Author Biography

Harish Paruchuri, Anthem, Inc.

Senior AI Engineer, Department of Information Technology, Anthem, Inc., USA

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Published

2019-03-20

How to Cite

Paruchuri, H. (2019). Market Segmentation, Targeting, and Positioning Using Machine Learning . Asian Journal of Applied Science and Engineering, 8(1), 7-14. Retrieved from https://journals.abc.us.org/index.php/ajase/article/view/1193

Issue

Section

Peer-reviewed Article