<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec><h3>Background</h3>
<p>Customer segmentation has become one of the most innovative ways which help businesses adopt appropriate marketing campaigns and reach targeted customers. The RFM model and machine learning combination have been widely applied in various areas.</p>
</sec>
<sec><h3>Motivations</h3>
<p>With the rapid increase of transactional data, the RFM model can accurately segment customers and provide deeper insights into customers’ purchasing behaviour. However, the traditional RFM model is limited to 3 variables, Recency, Frequency and Monetary, without revealing segments based on demographic features. Meanwhile, the contribution of demographic characteristics to marketing strategies is extremely important.</p>
</sec>
<sec><h3>Methods/Approach</h3>
<p>The article proposed an extended RFMD model (D-Demographic) with a combination of behavioural and demographic variables. Customer segmentation can be performed effectively using the RFMD model, K-Means, and K-Prototype algorithms.</p>
</sec>
<sec><h3>Results</h3>
<p>The extended model is applied to the retail dataset, and the experimental result shows 5 clusters with different features. The effectiveness of the new model is measured by the Adjusted Rand Index and Adjusted Mutual Information. Furthermore, we use Cohort analysis to analyse customer retention rates and recommend marketing strategies for each segment.</p>
</sec>
<sec><h3>Conclusions</h3>
<p>According to the evaluation, the proposed RMFD model was deployed with stable results created by two clustering algorithms. Businesses can apply this model to deeply understand customer behaviour with their demographics and launch efficient campaigns.</p>
</sec>
</abstract>

Background
Customer segmentation has become one of the most innovative ways which help businesses adopt appropriate marketing campaigns and reach targeted customers. The RFM model and machine learning combination have been widely applied in various areas.

Motivations
With the rapid increase of transactional data, the RFM model can accurately segment customers and provide deeper insights into customers’ purchasing behaviour. However, the traditional RFM model is limited to 3 variables, Recency, Frequency and Monetary, without revealing segments based on demographic features. Meanwhile, the contribution of demographic characteristics to marketing strategies is extremely important.

Methods/Approach
The article proposed an extended RFMD model (D-Demographic) with a combination of behavioural and demographic variables. Customer segmentation can be performed effectively using the RFMD model, K-Means, and K-Prototype algorithms.

Results
The extended model is applied to the retail dataset, and the experimental result shows 5 clusters with different features. The effectiveness of the new model is measured by the Adjusted Rand Index and Adjusted Mutual Information. Furthermore, we use Cohort analysis to analyse customer retention rates and recommend marketing strategies for each segment.

Conclusions
According to the evaluation, the proposed RMFD model was deployed with stable results created by two clustering algorithms. Businesses can apply this model to deeply understand customer behaviour with their demographics and launch efficient campaigns.

An Extended RFM Model for Customer Behaviour and Demographic Analysis in Retail Industry

University of Economics and Law, Ho Chi Minh City, Vietnam, Vietnam National University

Business Systems Research Journal

This work is licensed under the Creative Commons Attribution 4.0 International License.

Background
Customer segmentation has become one of the most innovative ways which help businesses adopt appropriate marketing campaigns and reach targeted...

An Extended RFM Model for Customer Behaviour and Demographic Analysis in Retail Industry

Publicado en línea: 21 sept 2023

Páginas: 26 - 53

Recibido: 25 dic 2022

Aceptado: 23 jul 2023

DOI: https://doi.org/10.2478/bsrj-2023-0002

Palabras clave
Customer segmentation, RFMD model, K-Means, One hot encoding, K-Prototypes, Cohort analysis, machine learning

© 2023 Thanh Ho et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

An Extended RFM Model for Customer Behaviour and Demographic Analysis in Retail Industry

Publicado en línea: 21 sept 2023

Páginas: 26 - 53

Recibido: 25 dic 2022

Aceptado: 23 jul 2023

DOI: https://doi.org/10.2478/bsrj-2023-0002

Palabras claveCustomer segmentation, RFMD model, K-Means, One hot encoding, K-Prototypes, Cohort analysis, machine learning

© 2023 Thanh Ho et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
Customer segmentation, RFMD model, K-Means, One hot encoding, K-Prototypes, Cohort analysis, machine learning