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

Fahed Yoseph, Markku Heikkilä

Research output: Chapter in Book/Conference proceeding › Conference contribution › Scientific › peer-review

15 Citations (Scopus)

233 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 language	Undefined/Unknown
Title of host publication	2018 International Conference on Machine Learning and Data Engineering (iCMLDE)
Publisher	IEEE
Pages	109–116
ISBN (Electronic)	978-1-7281-0404-1
ISBN (Print)	978-1-7281-0405-8
DOIs	https://doi.org/10.1109/iCMLDE.2018.00029
Publication status	Published - 2018
MoE publication type	A4 Article in a conference publication
Event	International Conference on Machine Learning and Data Engineering (iCMLDE) - 2018 International Conference on Machine Learning and Data Engineering (iCMLDE) Duration: 3 Dec 2018 → 7 Dec 2018

Conference

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

Keywords

Clustering
RFM model
Retailing
Segmentation

Access to Document

10.1109/iCMLDE.2018.00029

Segmenting Retail Customers with Enhanced RFM Data using a Hybrid RegressionClustering Method.pdfAccepted author manuscript, 586 KBLicence: Publisher rights policy

Cite this

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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}",

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AU - Heikkilä, Markku

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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

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

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