Behavioral Segmentation with Product Estimation using K-Means Clustering and Seasonal ARIMA

Eshaan Saraf · Snehal Pradhan · S. Sountharrajan

13 citations0/0 files storedInternational Conference on Trends in Electronics and Informatics (ICOEI)

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Customer churn and segmentation Consumer Market Behavior and Pricing Data Mining and Machine Learning Applications

Abstract

Definite purchasing patterns, usage, interests, engagement level, and responses emphasize the consumer's needs and want. Relating to these behavioral patterns, supermarkets can generate the right experience for their buyers, thus accounting for their interaction with the companies in real-time. The proposed methodology will allow the supermarket and associated brands to have an even better qualitative customer-centric approach, a key outcome of which is an increase in their CLV (or Customer Lifetime Value). The framework consists of an unsupervised Machine Learning technique of K-means clustering applied on a supermarket sales dataset. The resultant clusters will depict distinct segments of customers grouped on behavioral patterns extending the conventional RFM analysis approach. Further, the Seasonal ARIMA method models the time-series data to estimate the sales of top-selling products. It is a suggested approach to maintain and manage the supermarket-base more effectively along with a satisfied customer base.

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2021Integrated Churn Prediction and Customer Segmentation Framework for Telco Business 2020Clustering optimization in RFM analysis Based on k-Means 2020A Comparative Study and Analysis of Time Series Forecasting Techniques 2021Customer Segmentation Using Machine Learning 2021An Extended Regularized K-Means Clustering Approach for High-Dimensional Customer Segmentation With Correlated Variables 2020Customer Segmentation using Machine Learnin 2019RFM Based Market Segmentation Approach Using Advanced K-means and Agglomerative Clustering: A Comparative Study 2021Clustering and Classification Based on Distributed Automatic Feature Engineering for Customer Segmentation

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