Customer segmentation based on customer lifetime value using big data analytics

Syarifah Fatimah Fitria, Daniel Tumpal H. Aruan

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


Companies spend money to acquire new customers instead of maintaining current customers. RFM is an approach that is usually used to determine the effectiveness of customer behavior, while Customer Lifetime Value (CLV) is one of the indicators to predict how valuable a customer will be to a company in the future. This research focuses on customer segmentation based on CLV in order to create a customer profile that a company can use to provide appropriate treatment for each customer. In order to process the data, this research used RFM segmentation as the first step and followed with KMeans clustering to better interpret the customer data. The results showed five different types of customer profiles based on RFM and K-Means calculations. Each cluster has different characteristics that can be used by the company to define a better strategy in order to approach their customers. The analysis includes a comparison between the customer groups when a company conducts customer segmentation using CLV and without it. By providing the right treatment for profitable customers, a company can form an effective and targeted strategy for the future.

Original languageEnglish
Title of host publicationBusiness and Management Issues in the Global and Digital Era
Subtitle of host publicationIndonesian Perspectives
PublisherNova Science Publishers, Inc.
Number of pages19
ISBN (Electronic)9781536165302
ISBN (Print)9781536162752
Publication statusPublished - 11 Nov 2019


  • Big data analytics
  • CLV
  • Customer profiling
  • Customer segmentation
  • K-means algorithm
  • RFM


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