- Healthcare & Life Sciences
Scaling multi-touch attribution to optimize pharmaceutical marketing impact
Overview
Improving multi-touch attribution for targeted biopharma marketing
A leading biopharmaceutical company, known for its breakthroughs in innovative treatments, sought to improve its understanding of multi-channel marketing impacts on healthcare providers (HCPs), specifically in driving new-to-brand prescriptions (NBRx). With a vast marketing ecosystem, the company employed multiple touchpoints—including email, digital ads, and in-person events—to reach providers across various stages of the decision journey.
Although they had a proof-of-concept model for multi-touch attribution (MTA), it needed to be scaled and fine-tuned to operate effectively in a production environment. Additionally, the company needed a parameterized solution capable of segmenting MTA results by brand, franchise, and indication. The ultimate objective was to develop a robust and flexible MTA model that could accurately attribute marketing impact and optimize budget allocation to maximize engagement with HCPs.
Effective multi-channel marketing in the pharmaceutical industry is challenging, as each channel and publisher varies in its reach, engagement, and effectiveness. Unlike a single-channel approach, multi-touch attribution must capture how touchpoints interact within complex user journeys. An ideal solution would involve controlled experiments to precisely isolate channel impacts; however, the cost and frequency requirements of such experiments make them impractical for real-world applications. The client needed a more scalable approach that leveraged existing data to measure past performance and generate actionable insights for future marketing decisions.
Our Solution
Designing a scalable and adaptive MTA pipeline
OneSix built a highly parameterized, unit-tested Python package to perform MTA on the client’s diverse marketing initiatives, focusing on measuring individual touchpoint effectiveness across brands and indications. The model’s core function was to predict the probability of an NBRx occurring, based on a combination of control and independent variables derived from the various marketing channels. To further refine the model, OneSix introduced an advanced explainer model that could assign a partial contribution to each control and independent variable, providing a breakdown of the factors driving NBRx outcomes.
The MTA model was designed to address key technical challenges, including calibration to adjust for the sigmoid distortion often seen in probability densities from predictive models. This adjustment was achieved through a custom calibration scheme, which corrected probability distortions to ensure that all variables received a positive partial contribution. The parameterized structure of the model allowed users to modify factors such as study period lengths, feature sets, and segment parameters (e.g., brand or indication) with ease. The package was controlled by a single configuration file, providing a centralized interface for rapid experimentation and model adjustments via a command-line interface.
As a result, the pipeline offered flexibility for experimentation across different market baskets and feature combinations, empowering the client’s data science team to iterate quickly and test various configurations. By providing a modular, scalable, and flexible solution, OneSix’s MTA model allowed for high adaptability, enabling the client to execute MTA analyses on demand and derive actionable insights at a pace previously not possible.
Results
Accelerated data-driven insights and improved marketing allocation
The implementation of this comprehensive MTA pipeline enabled the client to gain a deeper understanding of how different marketing touchpoints contributed to NBRx conversions and overall engagement with HCPs. With OneSix’s solution in place, the company was able to assess the individual and combined impacts of each marketing channel, allowing them to identify high-performing channels and optimize spend allocation with confidence. By analyzing the contributions of different touchpoints within the customer journey, the company could now tailor its marketing strategies to maximize engagement and ROI on specific channels.
The streamlined configuration and command-line interface allowed the client’s data science team to rapidly test hypotheses and iterate on model features, reducing the research cycle and enhancing their agility in responding to market dynamics. Continued collaboration with OneSix provided the company with regular updates and enhancements to the MTA model, enabling ongoing improvements and refinements to its methodology. As a result, the biopharmaceutical company was able to achieve a more precise, data-driven approach to marketing attribution, laying a scalable foundation for sustainable growth and optimized channel investment across its brands and franchises.
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