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Aruva, Shiva Prasad (2023) A systematic evaluation of regressions and loss functions for the prediction of monetary value in RFM analysis. Masters thesis, Dublin, National College of Ireland.
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Abstract
RFM, which stands for Recency, Frequency, and Monetary value, is one of the most important methods for market research. It is employed to rank and categorise customers into segments. The traditional ranking of each variable (R, F, and M) is a number from one to five, thus resulting in up to 5*5*5=125 potential customer segments. This research study looks into the reduction of those segments using k-Means and the usage of these clusters for the prediction of monetary value. We take the optimal number of customer segments as a feature for regression. The best loss function and boosting algorithm for the prediction of monetary value is presented. Overall, we show that Extra Trees regression with negative median absolute error loss is the best combination for the prediction of monetary value. By identifying significant trends and distinctive client segments based on their purchase behaviour, the study intends to support targeted marketing initiatives and individualized customer engagement. The suggested approach makes use of the RFM analysis to determine customer scores, then applies the elbow method using k-means clustering to obtain the optimal number of customer clusters, and then use those clusters as a novel feature for the prediction of monetary value. We will show how the monetary value predictions can offer e-commerce enterprises useful insights. Our initial exploration shows encouraging results for the prediction of monetary value using the described methodology.
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email Estrada, Giovani UNSPECIFIED |
| Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Algebra > Algorithms > Computer algorithms H Social Sciences > HF Commerce > Marketing > Consumer Behaviour H Social Sciences > HF Commerce > Marketing |
| Divisions: | School of Computing > Master of Science in Data Analytics |
| Depositing User: | Tamara Malone |
| Date Deposited: | 13 Jan 2025 09:50 |
| Last Modified: | 13 Jan 2025 09:50 |
| URI: | https://norma.ncirl.ie/id/eprint/7310 |
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