Adaptive User Journeys in Pharma E-Commerce with Reinforcement Learning: Insights from SwipeRx
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917750211870720 |
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| author | del Río, Ana Fernández Leong, Michael Brennan Saraiva, Paulo Nazarov, Ivan Rastogi, Aditya Hassan, Moiz Tang, Dexian Periáñez, África |
| author_facet | del Río, Ana Fernández Leong, Michael Brennan Saraiva, Paulo Nazarov, Ivan Rastogi, Aditya Hassan, Moiz Tang, Dexian Periáñez, África |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_08024 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Adaptive User Journeys in Pharma E-Commerce with Reinforcement Learning: Insights from SwipeRx del Río, Ana Fernández Leong, Michael Brennan Saraiva, Paulo Nazarov, Ivan Rastogi, Aditya Hassan, Moiz Tang, Dexian Periáñez, África Machine Learning Artificial Intelligence 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. |
| title | Adaptive User Journeys in Pharma E-Commerce with Reinforcement Learning: Insights from SwipeRx |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2408.08024 |