A Study on Micro-Segmentation of Retail Customers Using K-Means Clustering
Bibliographic record
Abstract
This study aims to identify clients that share similar traits and develop a new micro-segmentation strategy. Drawn from two marketing theories i.e., customer relationship and personalisation and using the Recency Frequency Monetary (RFM) technique, clusters from the K-means technique are created to predict the behaviour of the best and least contributing retail customers. Transactional data was extracted from a Business to Customer (B2C) hyperretail store in India comprising 10, 20, 284 transactions done by 2140 regular customers taking into account their recency, frequency and total spending. Based on RFM metric values across three heterogeneous segments, customers were characterised as toppers, moderated and churners. Analysis reveals that the most valuable customers have RFM scores as HHH (high recency, high frequency and high monetary value). These are the most loyal customers and retailers cannot afford to lose them. In micro-segmentation, stores should also prioritise retaining customers who have a recent shopping experience (medium recency) but do so infrequently, while spending larger sums. This can be achieved through tailored marketing strategies. Implications stand for both offline and online retail businesses to understand customer behaviour and tailor-made marketing strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".