Segmenting retail customers with an enhanced RFM and a hybrid regression/clustering method

Fahed Yoseph, Markku Heikkilä

    Research output: Chapter in Book/Conference proceedingConference contributionScientificpeer-review

    13 Citations (Scopus)
    214 Downloads (Pure)

    Abstract

    Targeted marketing strategies use market segmentation for insight into the heterogeneity of the customer purchase lifecycle, but this often ignores the evolution of customer behavior over time causing retailers focus on unprofitable customers. By pairing the Recency, Frequency, Monetary scores with the Customer Lifetime Value model to segment customers of a medium-sized clothing and fashion accessory retailer in Kuwait. A modified regression algorithm investigates the customer purchase curve gaining knowledge from point-of-sales data to help the retailer make informed decisions. Clustering is by K-means and by Expectation Maximization. Cluster quality analysis reveals the former outperforms the latter to discover and suggest relevant market segments and appropriate marketing strategies.

    Original languageUndefined/Unknown
    Title of host publication2018 International Conference on Machine Learning and Data Engineering (iCMLDE)
    PublisherIEEE
    Pages109–116
    ISBN (Electronic)978-1-7281-0404-1
    ISBN (Print)978-1-7281-0405-8
    DOIs
    Publication statusPublished - 2018
    MoE publication typeA4 Article in a conference publication
    EventInternational Conference on Machine Learning and Data Engineering (iCMLDE) - 2018 International Conference on Machine Learning and Data Engineering (iCMLDE)
    Duration: 3 Dec 20187 Dec 2018

    Conference

    ConferenceInternational Conference on Machine Learning and Data Engineering (iCMLDE)
    Period03/12/1807/12/18

    Keywords

    • Clustering
    • RFM model
    • Retailing
    • Segmentation

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