An Extended RFM Model for Customer Behaviour and Demographic Analysis in Retail Industry
نویسندگان
چکیده
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 machine learning combination have been widely applied in various areas. Motivations With rapid increase transactional data, can accurately segment customers provide deeper insights into customers’ purchasing behaviour. However, traditional is limited to 3 variables, Recency, Frequency Monetary, without revealing segments based on demographic features. Meanwhile, contribution characteristics strategies extremely important. Methods/Approach article proposed an extended RFMD (D-Demographic) with a behavioural variables. be performed effectively using model, K-Means, K-Prototype algorithms. Results retail dataset, experimental result shows 5 clusters different effectiveness new measured by Adjusted Rand Index Mutual Information. Furthermore, we use Cohort analysis analyse customer retention rates recommend for each segment. Conclusions According evaluation, RMFD was deployed stable results created two clustering Businesses apply this deeply understand behaviour their demographics launch efficient campaigns.
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ژورنال
عنوان ژورنال: Business Systems Research
سال: 2023
ISSN: ['1847-9375', '1847-8344']
DOI: https://doi.org/10.2478/bsrj-2023-0002