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

    Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussaKonferenssiartikkeliTieteellinenvertaisarvioitu

    12 Sitaatiot (Scopus)
    148 Lataukset (Pure)

    Abstrakti

    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.

    AlkuperäiskieliEi tiedossa
    Otsikko2018 International Conference on Machine Learning and Data Engineering (iCMLDE)
    KustantajaIEEE
    Sivut109–116
    ISBN (elektroninen)978-1-7281-0404-1
    ISBN (painettu)978-1-7281-0405-8
    DOI - pysyväislinkit
    TilaJulkaistu - 2018
    OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
    TapahtumaInternational Conference on Machine Learning and Data Engineering (iCMLDE) - 2018 International Conference on Machine Learning and Data Engineering (iCMLDE)
    Kesto: 3 jouluk. 20187 jouluk. 2018

    Konferenssi

    KonferenssiInternational Conference on Machine Learning and Data Engineering (iCMLDE)
    Ajanjakso03/12/1807/12/18

    Keywords

    • Clustering
    • RFM model
    • Retailing
    • Segmentation

    Viittausmuodot