Enabling Adaptability in Web Forms Based on User Characteristics Detection Through A/B Testing and Machine Learning

TLDR

The study aims to enhance users’ completion performance of large questionnaires by adapting web forms to individual characteristics. The authors employ machine‑learning models trained on data from over 3,000 prior respondents, cluster users by behavior, and use A/B testing to generate heuristic redirection rules that adapt web‑form versions to each user group. Applying the redirection rules improved questionnaire completion outcomes compared to no adaptation at the initial stage.

Abstract

This paper presents an original study with the aim of improving users' performance in completing large questionnaires through adaptability in web forms. Such adaptability is based on the application of machine-learning procedures and an A/B testing approach. To detect the user preferences, behavior, and the optimal version of the forms for all kinds of users, researchers built predictive models using machine-learning algorithms (trained with data from more than 3000 users who participated previously in the questionnaires), extracting the most relevant factors that describe the models, and clustering the users based on their similar characteristics and these factors. Based on these groups and their performance in the system, the researchers generated heuristic rules between the different versions of the web forms to guide users to the most adequate version (modifying the user interface and user experience) for them. To validate the approach and confirm the improvements, the authors tested these redirection rules on a group of more than 1000 users. The results with this cohort of users were better than those achieved without redirection rules at the initial stage. Besides these promising results, the paper proposes a future study that would enhance the process (or automate it) as well as push its application to other fields.