The Impact of Personalization in Web-Based Credit Card Applications
Master's Thesis · ~88 pages · English
Abstract
This thesis investigates how personalization features in credit card application workflows affect user outcomes through rigorous experimentation. The study employs a randomized controlled trial spanning 12 months with 50,000+ participants, comparing standard versus personalized application flows. Primary metrics include completion rates, time-to-completion, and 30-day activation rates, with secondary measures tracking user satisfaction and support interactions.
1. Introduction
Digital financial services increasingly leverage personalization to enhance customer experience. Credit card application processes, traditionally characterized by standardized forms and generic messaging, represent significant opportunities for conversion optimization.
This research examines whether tailoring application workflows to individual user characteristics—including browsing history, demographic signals, and behavioral patterns—improves key business metrics while maintaining regulatory compliance.
2. Research Questions
RQ1: Does personalization increase application completion rates compared to standard workflows?
RQ2: How does personalization affect completion speed and user friction metrics?
RQ3: Are treatment effects consistent across demographic segments (age, income, credit score)?
RQ4: What long-term impacts does personalization have on card activation and subsequent usage?
3. Methodology
The study employs a randomized controlled trial design with N=50,000+ participants stratified by age, income, and credit score. Analysis includes intention-to-treat and per-protocol approaches with Benjamini-Hochberg multiple testing correction.
Heterogeneous treatment effects are estimated using causal forests, enabling identification of user segments where personalization delivers maximum impact.
References
- [1]Athey, S., & Wager, S. (2018). Estimation and Inference of Heterogeneous Treatment Effects using Random Forests. JASA, 113(523), 1228-1242.
- [2]Kohavi, R., & Longbotham, R. (2017). Online Controlled Experiments and A/B Testing. Encyclopedia of Machine Learning.
- [3]Lambrecht, A., & Tucker, C. (2013). When Does Retargeting Work? Information Specificity in Online Advertising. Journal of Marketing Research, 50(5), 561-576.
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