Customer Engagement with Promotional Incentives in Subscription-Based Businesses
Bibliographic record
Abstract
Customer engagement remains a critical challenge in retail management, particularly within subscription-based business models.While companies employ various promotional incentives to drive engagement and enhance customer lifetime value, the effectiveness and long-term impact of these strategies require further investigation.Subscription services, especially in the food sector, have experienced significant growth in recent years, making it essential to understand how different promotion framings influence customer behavior over time.This research seeks to contribute to this understanding through randomized field experiments and prescriptive analytical models, analyzing both the immediate and long-term effects of promotional strategies.The first study introduces the experimental design and examines the immediate impact of various promotional approaches.It provides an in-depth analysis of the experiment, which investigates how different incentive structures affect customer engagement and basket size.This analysis also explores variations in promotional effectiveness across distinct customer segments through heterogeneity analysis.The second study shifts focus to long-term engagement and the effects of delayed rewards.By incorporating post-experiment data, this section evaluates how promotions influence customer retention and lifetime value.It also presents findings from employing modelfree analysis and statistical techniques, including difference-in-differences, to measure the sustained impact of promotions and their interactions with customer behavior over time.The third study develops an analytical model for optimizing promotional strategies, integrating insights from the previous experiments.This prescriptive model provides i actionable business recommendations, identifying the most effective promotion types, frequencies, target customer segments, and budget allocation strategies.By offering a structured approach to maximizing promotional effectiveness, the model aids managerial decision-making in balancing short-term engagement with long-term customer value.Through this structured analysis, the thesis aims to enhance the understanding of promotional effectiveness in subscription-based business models while providing practical managerial insights for improving customer engagement strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".