A travel customer segmentation method based on improved RFM and k-means++

Shaodong Huang, Sheng Qin, Xiaoxiao Jiang*, Yi Cao

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

1 Citation (Scopus)

Abstract

Customer segmentation is an important approach for customer relationship management, in which many methods are achieved by the Recency, Frequency and Monetary model(RFM) and clustering techniques. However, most methods based on the Recency, Frequency and Monetary model do not consider customer loyalty. In addition, these methods need to use all the historical data when updating the clustering, which has high data storage requirements. In this paper, a clustering method with a time window is proposed to solve these problems. The proposed method is divided into a feature selection stage and a clustering stage. In the feature selection stage, an important factor is considered in an improved Recency, Frequency and Monetary model, called the Length, Recency, Frequency and Monetary model(LRFM). In the clustering stage, a sliding time window is added to intercept the most recent data before the clustering. The proposed method differs from many other methods in that the model takes into consideration a new feature Length to identify customers more accurately, and uses the sliding time window to reduce data storage requirements. Based on the proposed method, the travel customer value analysis is explored on real customer anonymous transaction data. The experimental results show that the proposed method can classify travel customers into different groups effectively. The proposed method has a better clustering performance compared to other baseline algorithms.

Original languageEnglish
Title of host publicationCSSE 2022 - 2022 5th International Conference on Computer Science and Software Engineering
Subtitle of host publicationConference Proceedings
PublisherAssociation for Computing Machinery
Pages436-440
Number of pages5
ISBN (Electronic)9781450397780
DOIs
Publication statusPublished - 21 Oct 2022
Externally publishedYes
Event5th International Conference on Computer Science and Software Engineering, CSSE 2022 - Guilin, China
Duration: 21 Oct 202223 Oct 2022

Publication series

NameACM International Conference Proceeding Series

Conference

Conference5th International Conference on Computer Science and Software Engineering, CSSE 2022
Country/TerritoryChina
CityGuilin
Period21/10/2223/10/22

Keywords

  • Clustering
  • Customer Segmentation
  • Value analysis

Fingerprint

Dive into the research topics of 'A travel customer segmentation method based on improved RFM and k-means++'. Together they form a unique fingerprint.

Cite this