Using RFM model to help mobile phone dealer to find their target customers in China
Bibliographic record
Abstract
Target customer positioning is the first step for enterprises to develop market strategy. After the completion of user positioning, through the analysis of specific groups, we can accurately know the consumption habits and thinking process of users, which will bring great help to the precision marketing of businesses and reduce operating costs. This paper focuses on using RFM model to help Chinese mobile phone dealer to find their target customers and create a portrait of the target consumers. This paper analyses 263352 different customers with 10 different brands and discusses the valuable cell phone consumers. The result shows Guangdong, Shanghai and Beijing have the highest sales, volume, and number of customers. The top five mobile phone sales are Samsung, Apple, Xiaomi, Huawei and OPPO, among which Samsung accounts for more than half of the share, and Apple accounts for about a quarter. The analysis found that the purchase of Samsung and Apple phones by all age groups and by men and women was relatively average. It was also found that Guangdong, Shanghai, and Beijing accounted for more than 50% of the sales. The percentage important value customers accounts for about 50% of the important customers, which is higher than the average percentage of other e-business products. Despite the sluggish sales figures, mobile e-commerce is still an industry with great potential.
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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 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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".