Analysis for Customer Lifetime Value Categorization with RFM Model

Siti Monalisa · Putri Nadya · Rice Novita

56 citations0/0 files storedProcedia Computer Science

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Customer churn and segmentation Customer Service Quality and Loyalty Data Mining and Machine Learning Applications

Abstract

The same treatment of all customers will cause customers who are not so valuable will be value destroyer rather than a value creator. Giving discounts and promos to all customers has not provided benefits to a company in retaining customers. These two things are currently being experienced by LWC Company so that changes are needed in evaluating the strategy taken to keep the relationship with customers, especially potential customers, by forming customer portfolios. Customers portfolios can be analyzed from customer transactions. With customer transactions data, the company can find out which potential customers and the customers who do give less value for the company. The way to analyze customer transaction data is by clustering customers with Fuzzy C-means algorithm by using RFM model. Forming the number of clusters is validated by PCI model and ranking is done by multiplying AHP weight to find the life value of the customer so that it can be known which customer group gives high value to the company. The result of this research is that the customers of LWC Company categorized as superstar customer, typical customer and dormant customer. Based on the portfolio, LWC Company can carry out strategy in managing their customer according to the type of the portfolio.

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References

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