BUSINESS SALES ANALYSIS AND CUSTOMER RETENTION

Volume: 6    Issue: 1

Year of Publication: 2019

Pages: (5-10)

Authors: Ankit Chaplot, Ayush Tiwari, Gaurav Bisaria, Mustafa Raj

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Abstract

This project proposes a method that predicts customer value by focusing on purchasing behaviour. The method generates a relevance model for purchase days and amount in each period between customer value and purchasing histories beforehand based on a consumer panel survey. We have adopted the random forest method to generate the prediction model. The proposed method facilitates the provisioning of smart customer management to each customer according to level such as suggesting products or services. The problem faced by the company is how to determine potential customers and apply CRM (Customer Relationship Management) in order to perform the right marketing strategy, so it can bring benefits to the company. This research aims to perform clustering and profiling customer by using the model of Recency Frequency and Monetary (RFM) to provide customer relationship management (CRM). The method used in this study consists of four steps: data mining from transaction history data of customer sales, data mining modeling using RFM and customer classification with decision tree, determination of customer loyalty level and recommendation of customer relationship management (CRM).

References

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Keywords

Transaction Data Mining; RFM; Clustering; Kmeans; Decision Tree; Profiling Customers; CRM.