Customer Segmentation in the Field of Marketing

Title

Customer Segmentation in the Field of Marketing

Creator

Thomas J.; Preethi N.

Description

The motive of this work is to classify and categorize customers depending on their familiar traits/characteristics so as to enable a company or a firm to adequately market their products to each category more attractively and competently. It is imperative for a firm to educate themselves with each and every detail about the customer, such as age group, sexuality, social class, purchase pattern etc as it paves way for customer segmentation. Businesses may utilize segmentation to make better use of their marketing resources, get a competitive advantage over competitors, and, most importantly, display a deeper understanding of their consumers' requirements and desires. Customer segmentation, when combined with customer targeting and positioning, creates the foundation for strategic marketing. A manager can find new marketing possibilities and create or adjust the product to satisfy the demands of potential clients using the notion of strategic marketing. The product's quality level determines its position in the market's overall offering. It's a crucial aspect in selecting which market segment a collection will target. The commercial world has gotten more competitive over time, as enterprises like these have to fulfil their consumers' demands and aspirations, attract new customers, and enhance their bottom lines. In this research, I have put the spotlight on the information used by firms for the purpose of customer segmentation in the most valuable manner. In addition to that, I have portrayed different models of customer segmentation and the benefits reaped by a business in implementing them. 2022 IEEE.

Source

4th International Conference on Recent Trends in Computer Science and Technology, ICRTCST 2021 - Proceedings, pp. 401-405.

Date

2022-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Agglomerative algorithm; Business; Characteristics; Customer Segmentation; DBSCAN algorithm; k-Means algorithm; Machine Learning Models; Neural Networks

Coverage

Thomas J., Christ University, Department Of Data Science, India; Preethi N., Christ University, Department Of Data Science, India

Rights

Restricted Access

Relation

ISBN: 978-166546633-2

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICRTCST54752.2022.9781964

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