Enhancing customer segmentation: RFM analysis and K-Means clustering implementation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In today's highly competitive landscape, businesses across various sectors, including corporations, retail, and banking, are striving to attract and retain customers. Customer segmentation has emerged as a vital strategy in this endeavor, enabling organizations to identify and cater to distinct customer groups. This study leverages the RFM (Recency, Frequency, Monetary) model to segment customers based on their purchasing behavior. Initially, an RFM analysis is conducted to quantify customer value, which is then further analyzed using the k-means clustering algorithm to identify distinct customer segments. By understanding and targeting these segments, businesses can enhance customer relationships, improve marketing strategies, and foster organizational resilience. This approach not only aids in acquiring new customers but also in retaining existing ones, ultimately contributing to sustained competitive advantage.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 it