Adaptive User Journeys in Pharma E-Commerce with Reinforcement Learning: Insights from SwipeRx

TL;DR

This paper presents a reinforcement learning platform that personalizes user experiences in healthcare apps, demonstrated through a case study with SwipeRx, leading to improved engagement and healthcare outcomes.

Contribution

It introduces a novel RL framework for adaptive user journeys in pharma e-commerce, specifically tailored for healthcare digital tools like SwipeRx.

Findings

Significant increase in basket size through personalized recommendations

Effective real-time adaptation to pharmacy behavior

Scalable approach for healthcare supply chain and patient care improvements

Abstract

This paper introduces a reinforcement learning (RL) platform that enhances end-to-end user journeys in healthcare digital tools through personalization. We explore a case study with SwipeRx, the most popular all-in-one app for pharmacists in Southeast Asia, demonstrating how the platform can be used to personalize and adapt user experiences. Our RL framework is tested through a series of experiments with product recommendations tailored to each pharmacy based on real-time information on their purchasing history and in-app engagement, showing a significant increase in basket size. By integrating adaptive interventions into existing mobile health solutions and enriching user journeys, our platform offers a scalable solution to improve pharmaceutical supply chain management, health worker capacity building, and clinical decision and patient care, ultimately contributing to better healthcare outcomes.