The Research on Dynamics in Phone Sales Marketing Campaign Based on Machine Learning
Bibliographic record
Abstract
Phone market campaign is one of the selling methods that banks use to attract new term deposits. Identifying attributes of customers is essential to increase the successful rate of a phone marketing campaign. This paper uses a dataset from a Portuguese bank where various features and attributes of customers are summarized. Four methods are used to analyze this issue, firstly decision tree method, then random forest method, K nearest neighbor and lastly logistic regression. After evaluating and reviewing their confusion matrices and generated scores, the decision has been made to choose the model of random forest as it possesses the highest mean of all metrics of classification. In conclusion, the duration of contact, age, how many days have passed since last contact, the month of the contact, and the number of contacts on one customer are deemed as more important than other attributes under a random forest classifier. The findings of this paper implicates that the Portuguese bank needs to focus more of these attributes to obtain a sustainable development.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".