Research on Customer Life-cycle Value Analysis and Refined Operations Based on UCI Online Retail Data-set
Bibliographic record
Abstract
In non-contractual e-commerce, sustaining growth depends on predicting future customer value and targeting actions that generate incremental profits. Previous research on customer lifetime value (CLV, hereafter LTV), customer churn, and the identification of high-value customers has provided valuable insights. However, most studies have explored these tasks in isolation, emphasized accuracy over profitability, or relied on proprietary data. People propose a reproducible end-to-end framework that unifies preprocessing, feature construction, temporal out-of-time (OOT) validation, model interpretability, and ROI-based evaluation. Using the UCI Online Retail dataset (2010–2011; 541,909 transactions), people construct RFM and behavioral features, apply a 70/30 customer-level split with an OOT holdout, and tackle three predictive tasks: six-month LTV regression, Top-25% high-LTV classification, and 90-day churn prediction. Performance is evaluated with RMSE/MAE/MAPE for regression and ROC-AUC/PR-AUC/Brier/calibration for classification; imbalance is managed via class weighting and SMOTE within folds. Tree-based models consistently outperform linear/logistic baselines; XGBoost achieves the best high-LTV performance (OOT AUC = 0.921; ΔAUC = +0.047 vs. logistic; 95% CI [0.025, 0.069]; p < 0.01; DeLong). SHAP identifies recency, frequency, and volatility as dominant drivers. ROI simulations show that prediction-driven targeting improves incremental margin by 11.3% at the operational threshold and remains robust under ±10% cost perturbations. By aligning statistical rigor with profit relevance, the study provides a reproducible blueprint for non-contractual retail and actionable guidance for segmentation, budget allocation, and retention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".