Segmentation of business saving customer to improve average balance based on structural equation modeling (SEM) and recency, frequency, monetary (RFM): Case study Bank XYZ in Indonesia
Bibliographic record
Abstract
As business players, entrepreneurs certainly need bank products and supports that provide fast and easy services with wide-spread network in Indonesia. RFM is a segmentation method based on past data and create an index on a client where the high loyalty and assume the behaviour of customers in the index will be the same in the future. Certainly, customers with high RFM score were customers who become the target of the Bank because these customers have high loyalty and valuable for the Bank. In this study, segmentation performed based on transactions which affect the increase in average balance using Structural Equation Model (SEM). The objects of RFM segmentation is to identify the customer in order to build a marketing strategy for each segment with different levels of loyalty. As the segmentation results we found three driver categories, High Recency, Middle Recency and Low Recency customer category. High Recency is considered Active customer where campaign category can be cross/up-selling and promotional accordingly with their Frequency and Monetary. Middle Recency category is considered Risky customer where campaign category can be retention program accordingly with their Frequency and Monetary. Last, Low Recency is considered Churn customer where campaign category is reactivation
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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 | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".