Analyzing and Predicting Low-Listenership Trends in a Large-Scale Mobile Health Program: A Preliminary Investigation

Arshika Lalan
Shresth Verma
Kumar Madhu Sudan
Amrita Mahale
Aparna Hegde
The Workshop in Data Science for Social Good, KDD 2023(2023)
Google Scholar


Mobile health programs are becoming an increasingly popular medium for dissemination of health information among beneficiaries in less privileged communities. Kilkari is one of the world’s largest mobile health programs which delivers time sensitive audio-messages to pregnant women and new mothers. We have been collaborating with ARMMAN, a non-profit in India which operates the Kilkari program, to identify bottlenecks to improve the efficiency of the program. In particular, we provide an initial analysis of the trajectories of benefi- ciaries’ interaction with the mHealth program and examine elements of the program that can be potentially enhanced to boost its success. We cluster the cohort into different buckets based on listenership so as to analyze listenership patterns for each group that could help boost program success . We also demonstrate preliminary results on using historical data in a time-series prediction to identify benefi- ciary dropouts and enable NGOs in devising timely interventions to strengthen beneficiary retention.