Authors : Sangeetha K; Lauvanya R; Dharshini I; Amizhthini A

Volume/Issue : Volume 9 - 2024, Issue 4 - April

Google Scholar : https://tinyurl.com/ycxvzy4x

Scribd : https://tinyurl.com/5c9t9dcm

DOI : https://doi.org/10.38124/ijisrt/IJISRT24APR900

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Customer categorization is an essential strategy for companies seeking to maximize their advertising spend. Businesses can boost client involvement and sales rates of conversion substantially by identifying specific customer segments, tailoring products or services to their preferences, minimizing the hassle of irrelevant advertisements, and increasing customer satisfaction, resulting in improved long-term interactions with clients. This paper presents a classification model that uses Keras and support vector machine stacked classification passed on to a meta-learner to predict the customer segment, and RFM analysis is performed to identify the customer segment. This focused strategy lowers marketing costs and boosts income, increasing the business's efficiency. Temporal mining helps us predict the next purchase of a customer using a time series model.

Keywords : Customer Classification, RFM Analysis, Keras And Support Vector Machine .

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