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

Fahed Yoseph; Markku Heikkilä

doi:10.1109/iCMLDE.2018.00029

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

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussa › Konferenssiartikkeli › Tieteellinen › vertaisarvioitu

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.

Alkuperäiskieli	Ei tiedossa
Otsikko	2018 International Conference on Machine Learning and Data Engineering (iCMLDE)
Kustantaja	IEEE
Sivut	109–116
ISBN (elektroninen)	978-1-7281-0404-1
ISBN (painettu)	978-1-7281-0405-8
DOI - pysyväislinkit	https://doi.org/10.1109/iCMLDE.2018.00029
Tila	Julkaistu - 2018
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisuussa
Tapahtuma	International Conference on Machine Learning and Data Engineering (iCMLDE) - 2018 International Conference on Machine Learning and Data Engineering (iCMLDE) Kesto: 3 jouluk. 2018 → 7 jouluk. 2018

Konferenssi

Konferenssi	International Conference on Machine Learning and Data Engineering (iCMLDE)
Ajanjakso	03/12/18 → 07/12/18

Keywords

Clustering
RFM model
Retailing
Segmentation

Pääsy asiakirjaan

10.1109/iCMLDE.2018.00029

Segmenting Retail Customers with Enhanced RFM Data using a Hybrid RegressionClustering Method.pdfHyväksytty kirjoittajan käsikirjoitus, 586 KBLisenssi: Publisher rights policy

Viittausmuodot

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title = "Segmenting retail customers with an enhanced RFM and a hybrid regression/clustering method",

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.",

keywords = "Clustering, RFM model, Retailing, Segmentation, Clustering, RFM model, Retailing, Segmentation, Clustering, RFM model, Retailing, Segmentation",

author = "Fahed Yoseph and Markku Heikkil{\"a}",

year = "2018",

doi = "10.1109/iCMLDE.2018.00029",

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isbn = "978-1-7281-0405-8",

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AU - Yoseph, Fahed

AU - Heikkilä, Markku

PY - 2018

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N2 - 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.

AB - 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.

KW - Clustering

KW - RFM model

KW - Retailing

KW - Segmentation

KW - Clustering

KW - RFM model

KW - Retailing

KW - Segmentation

KW - Clustering

KW - RFM model

KW - Retailing

KW - Segmentation

U2 - 10.1109/iCMLDE.2018.00029

DO - 10.1109/iCMLDE.2018.00029

M3 - Konferensbidrag

SN - 978-1-7281-0405-8

SP - 109

EP - 116

BT - 2018 International Conference on Machine Learning and Data Engineering (iCMLDE)

PB - IEEE

Y2 - 3 December 2018 through 7 December 2018

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