Case Study: Enhancing Customer Trust Through AI-Driven Post-Purchase Management
Bibliographic record
Abstract
Artificial Intelligence (AI), broadly defined as computational systems capable of performing tasks that typically require human intelligence, has advanced rapidly in recent years. A particularly transformative development is the rise of agentic AI, autonomous, goal-driven, and adaptive systems capable of reasoning and acting independently in dynamic environments. These technologies are becoming powerful drivers of innovation in the e-commerce and retail sectors, fundamentally reshaping how businesses engage with consumers and manage operations. One critical area in retail is customer satisfaction and brand trust, where online reviews serve as a key post-purchase feedback channel. Despite a benchmark maximum response rate of 69%, at least 30-40% of reviews remain unanswered, negatively impacting customer satisfaction and trust. In this paper, we propose an AI-driven solution designed to enhance the speed, efficiency, and quality of responses to negative customer reviews. Our approach outperforms current manual, automated, and semi-automated methods, offering a scalable and effective tool for improving customer engagement and brand perception. This solution is intended to assist product quality and customer experience teams, not to replace them, by augmenting their capabilities with intelligent automation.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".