An analytical framework based on the recency, frequency, and monetary model and time series clustering techniques for dynamic segmentation

作者：

Highlights：

• A framework that represents each customer behavior as time-series data is proposed.

• Hierarchical, spectral, and k-shape clustering are adapted for time series data.

• An implementation of the proposed framework using real data is conducted.

• Best results are obtained using Complexity-Invariant Distance (CID) measure.

• Visualization of results is provided.

摘要

•A framework that represents each customer behavior as time-series data is proposed.•Hierarchical, spectral, and k-shape clustering are adapted for time series data.•An implementation of the proposed framework using real data is conducted.•Best results are obtained using Complexity-Invariant Distance (CID) measure.•Visualization of results is provided.

论文关键词：Analytical customer relationship management,Customer segmentation,Dynamic customer behavior,Time series clustering,Distance measure

论文评审过程：Received 27 May 2021, Revised 20 September 2021, Accepted 1 December 2021, Available online 22 December 2021, Version of Record 28 December 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.116373