Improving relationship channels in the pharmaceutical industry with machine learning - Is it possible to develop a model to suggest an individual mix of channels for each physician?

Aug 3, 2026

The pharmaceutical industry drives longer, better lives through scientific research and modern medications. Face-to-face interactions between sales representatives and physicians are widely considered central to effective and efficient product promotion. With physicians as the primary target audience, marketing efforts aim to influence prescribing behavior. This study investigates whether machine learning can identify the most effective combination of physician communication channels, such as webinars, email engagement, social media, and in-person interactions, using three datasets covering channel interactions, prescription outcomes, and physician demographics. Results show machine learning can enhance CRM planning, and the classifier chain offers the simplest and most effective approach for predicting channel mix.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages55

Reading Time2 h 19 min

AccessFree to download

Statement of Integrity

Abstract

1. Introduction

1.1. Contextualization

1.2. Study goals

1.3. Relevance of the study

2. Literature review

2.1. HCPS relationship channels

2.2. Machine learning

Improving relationship channels in the pharmaceutical industry with machine learning Is it possible to develop a model(s) based on machine learning, capable to suggest a mix of channels for each physician individually? Reinaldo dos Santos Barros Master Thesis presented as partial requirement for obtaining a Master’s Degree in Information Management NOVA Information Management School Instituto Superior de Estatística e Gestão de Informação Universidade Nova de Lisboa NOVA Information Management School Instituto Superior de Estatística e Gestão de Informação Universidade Nova de Lisboa Improving relationship channels in the pharmaceutical industry with machine learning Is it possible to develop a model(s) based on machine learning, capable to suggest a mix of channels for each physician individually? by Reinaldo dos Santos Barros Master Thesis presented as partial requirement for obtaining the Master’s degree in Information Management, with a specialization in Business Intelligence. Supervised by Carina Isabel Andrade Albuquerque, PhD, NOVA Information Management School João Pedro Martins Ribeiro da Fonseca, PhD, NOVA Information Management School De STATEMENT OF INTEGRITY I hereby declare having conducted this academic work with integrity. I confirm that I have not used plagiarism, any form of undue use of information or falsification of results along the process leading to its elaboration. I further declare that I have fully acknowledged the Rules of Conduct and Code of Honor from the NOVA Information Management School. 13 de dezembro de 2024 Reinaldo dos Santos Barros ABSTRACT The pharmaceutical industry is crucial in modern society, helping people live longer and better through scientific research and modern medications. In this industry, there is a concept that face-to-face interactions between a sales representative and a physician are more effective and efficient in promoting products. It is important to note that in this industry, the target customers are physicians, and all the marketing efforts are focused on convincing them to prescribe their medications. This study explores whether machine learning can help determine the most effective combination of communication channels. These channels may include webinars, email engagement, social media, or in-person interactions. Can machine learning models suggest the optimal combination of physician communication channels? This study uses three datasets. The first one provides interactions among physicians and different communication channels. The second one shares the prescription data from physicians, while the third one offers basic demographic information from physicians. The study discovered that machine learning could enhance CRM planning by applying digital channels to improve communication with physicians. It identified the classifier chain as the simplest and most effective model for predicting the mix of communication channels. Different machine learning methods and algorithms were applied to understand their use from different perspectives. Other algorithms that could be utilized in future research are also highlighted, revealing alternative approaches to addressing the issue to propose a combination of communication channels. KEYWORDS Machine Learning; Supervised learning; Classifier Chain; Binary Relevance; Communication Channels Sustainable Development Goals (SDG): TABLE OF CONTENTS Statement of Integrity ................................................................................................................ ii Abstract ..................................................................................................................................... iii List of [Figures............................................................................................................................. vi](Figures....................................................................................

FAQs

What is the main goal of the study on physician communication channels?

To determine whether machine learning can suggest an effective mix of communication channels for each physician individually.

Which communication channels are considered in the research?

The study includes options such as webinars, email engagement, social media, and in-person interactions.

What datasets are used to build and evaluate the models?

Three datasets are used: interactions among physicians and channels, prescription data from physicians, and basic demographic information about physicians.

Aug 3, 2026

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