AI-Powered Personalization in SuperApps: International Case Studies on User Engagement
Bibliographic record
Abstract
The proliferation of SuperApps—integrated digital platforms offering a suite of services such as messaging, e-commerce, payments, and transportation—has redefined how users interact with technology. Central to the success of these platforms is artificial intelligence (AI)-powered personalization, which enables highly adaptive, user-centric experiences. This research investigates the mechanisms, strategies, and outcomes of AI-driven personalization within SuperApps and its influence on user engagement. Drawing upon a multidisciplinary literature review and international case studies from industries such as retail, finance, and telecommunications, this paper examines how AI techniques—including machine learning, deep learning, and natural language processing—facilitate real-time data processing, predictive modeling, and content adaptation. The findings highlight that personalization significantly enhances key engagement metrics, such as click-through rates, conversion rates, and user retention. Case examples from organizations such as H&M, Kanetix, and the Bank of Montreal illustrate how ethical AI implementation, cross-channel integration, and explainable recommendation systems contribute to improved consumer trust and operational performance. The study also addresses critical challenges, including data privacy, algorithmic bias, and the tension between personalization and user autonomy. Recommendations are proposed for mitigating these risks through responsible AI governance, privacy-by-design frameworks, and transparency-enhancing practices. By synthesizing practical and academic insights, this paper contributes to the emerging body of knowledge on AI integration in digital ecosystems. It offers actionable strategies for developers, marketers, and policymakers to harness AI responsibly in SuperApps. Furthermore, it outlines future research directions in explainable AI, behavioral economics, and multimodal personalization, ultimately aiming to shape more inclusive, secure, and engaging digital platforms globally.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".