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Enhancing customer repurchase prediction: Integrating classification algorithms with RFM analysis for precision and actionable insights

Rakesh Verma et al.

IIMB Management Review2025https://doi.org/10.1016/j.iimb.2025.100574article
ABDC C
Weight
0.46

What the paper says

Accurate prediction of customer repurchase behavior is vital for businesses aiming to boost customer retention. This study introduces an advanced approach that merges classification algorithms with RFM analysis, a widely adopted framework in customer relationship management. The proposed models utilize RFM scores as input features to categorize customers into likely or unlikely repurchasers. The developed models demonstrate strong accuracy (74%) and performance on an online UK retail store dataset, showcasing their efficacy in identifying customers likely to make future purchases. Furthermore, a feature importance analysis identifies key RFM dimensions influencing repurchase behavior, empowering businesses to tailor targeted marketing strategies.

4 citations

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https://doi.org/https://doi.org/10.1016/j.iimb.2025.100574

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@article{rakesh2025,
  title        = {{Enhancing customer repurchase prediction: Integrating classification algorithms with RFM analysis for precision and actionable insights}},
  author       = {Rakesh Verma et al.},
  journal      = {IIMB Management Review},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1016/j.iimb.2025.100574},
}

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Enhancing customer repurchase prediction: Integrating classification algorithms with RFM analysis for precision and actionable insights

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Evidence weight

0.46

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.37 × 0.4 = 0.15
M · momentum0.60 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.