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

    Forskningsoutput: Kapitel i bok/konferenshandlingKonferensbidragVetenskapligPeer review

    11 Citeringar (Scopus)
    147 Nedladdningar (Pure)

    Sammanfattning

    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.

    OriginalspråkOdefinierat/okänt
    Titel på värdpublikation2018 International Conference on Machine Learning and Data Engineering (iCMLDE)
    FörlagIEEE
    Sidor109–116
    ISBN (elektroniskt)978-1-7281-0404-1
    ISBN (tryckt)978-1-7281-0405-8
    DOI
    StatusPublicerad - 2018
    MoE-publikationstypA4 Artikel i en konferenspublikation
    EvenemangInternational Conference on Machine Learning and Data Engineering (iCMLDE) - 2018 International Conference on Machine Learning and Data Engineering (iCMLDE)
    Varaktighet: 3 dec. 20187 dec. 2018

    Konferens

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

    Nyckelord

    • Clustering
    • RFM model
    • Retailing
    • Segmentation

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