Customer Segmentation for Targeted Campaigns Using RFM Analysis and K-Means Clustering
Bibliographic record
Abstract
Customer segmentation is critical for tailoring marketing campaigns to specific consumer needs, enhancing engagement, and maximizing business profitability. This study integrates Recency-Frequency (RF) analysis with the K-Means clustering algorithm to classify customers of "Ramallah Outfit" into distinct behavioral segments. The Monetary (M) component was excluded because spending levels can sometimes be misleading high or low expenditures do not always reflect true loyalty or engagement. While monetary data may indicate how much a customer spends, it does not necessarily correlate with the frequency or recency of purchases, which are more reliable indicators of customer behavior and engagement. Excluding the Monetary component allows for a clearer focus on customer retention and loyalty. A high spender might make only occasional large purchases, whereas a customer with frequent smaller purchases could be more engaged and loyal. By focusing on Recency and Frequency, the analysis more accurately captures ongoing engagement, providing actionable insights for marketing. Using a unique first-hand dataset, the model achieved a clustering accuracy of 99.47%, effectively segmenting customers into actionable clusters: Gold (34.4%), Bronze (35.2%), and Silver (30.4%). The results demonstrate the effectiveness of this refined approach in optimizing marketing efforts and fostering customer loyalty.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".