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

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussaKonferenssiartikkeliTieteellinenvertaisarvioitu

5 Sitaatiot (Scopus)
9 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 joulukuuta 20187 joulukuuta 2018

Konferenssi

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

Keywords

  • Clustering
  • RFM model
  • Retailing
  • Segmentation

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