How a data-driven model is transforming the way healthcare professionals (HCPs) are approached
Over time, many healthcare companies develop complex and often fragmented system and data landscapes. Typically, CRM systems, digital interactions, and sales operations run in parallel, each with their own objectives, processes, and KPIs. As a result, relevant information on healthcare professionals (HCPs) is scattered across silos, making it difficult to integrate and rarely actionable for targeted engagement.
This fragmentation presents a major challenge for both marketing and sales teams. Without a unified view of HCP behavior, digital touchpoints, and channel preferences, it becomes difficult to personalize communication, prioritize efforts, or allocate budgets efficiently.
A leading healthcare corporation set out to change this. Drawing on our consulting expertise, we developed a data-driven CRM segmentation model that consolidates existing data sources and uncovers relevant behavioral patterns. This model enables scalable, strategic HCP marketing aligned with individual relevance, timing, and preferred channels. The result: more accurate target group profiles, a significantly more personalized approach, and a solid foundation for sustainable, cross-channel engagement.
A healthcare corporation faced the challenge of targeting healthcare professionals (HCPs) more effectively in an increasingly digitalized and complex environment. Field sales activities and digital efforts needed to be integrated more consistently. Existing data needed to be utilized more effectively. However, the relevant information was spread across various systems and difficult to use for a coordinated approach.
The project focused on developing a scalable, behavior-based segmentation model that could be used across channels. Mail campaigns, sales activities, and event invitations were integrated. Based on a modernized RFM (Recency, Frequency, Monetary) model and using machine learning, digital interactions and preferences were systematically fed into the segmentation logic.
An interactive dashboard clearly displays the results and enables marketing, sales, and CRM to plan and implement their activities more accurately. Segment information, channel preferences, and response behavior are centrally available, facilitating daily work with data-based decision-making.
02 Objectives
PROJECT 1
Consolidating data and making it actionable
The CRM and interaction data from multiple systems was consolidated, cleaned, and prepared for analysis. Key attributes such as digital activity, contact frequency, and cross-channel response behavior were identified. A central data platform (including Snowflake) was established to provide a consistent foundation for the subsequent segmentation logic.
PROJECT 2
Develop and operationalize the segmentation model
Based on the cleansed data, a behavior-based segmentation model was developed. The model was enriched by an extended RFM model and digital KPIs, enabling a cluster structure suitable for cross-channel use, from business planning to campaign execution. The segments were then made directly actionable for marketing and sales in daily operations by being visualized in an interactive dashboard.
Image 1: Elbow method for determining the number of clusters
Image 2: Example cluster plot with 4 different segments
Distributed systems and inconsistent data structures
CRM data, digital touchpoints, event data, and field force information were stored in isolated systems with widely varying data quality and lacking standardization. Therefore, it was not possible to obtain a consolidated view of HCP interactions.
Solution:
To establish an integrated data foundation, relevant data points were identified, harmonized, and consolidated within a centralized Snowflake platform. This was built on a standardized KPI model that is flexibly expandable and structures both historical and new data sources.
No segmentation logic and limited target group steering
Marketing and sales teams were not using a systematic approach to target group differentiation. HCP engagement was primarily experience-driven, which limited the company’s ability to provide customized experiences across channels and allocate budgets with precision.
Solution:
A cluster model was developed based on behavior-related metrics. This model allows for the clear delineation of segment profiles. These profiles were interpreted in collaboration with business units, translated into concrete use cases, and visualized in a dashboard for CRM, marketing, and sales teams.
Data privacy and regulatory requirements
The use of personal data in healthcare is subject to stringent legal requirements. In particular, GDPR regulations and industry-specific advertising restrictions had to be considered when segmenting and communicating with healthcare professionals.
Solution:
In close collaboration with legal and compliance teams, all data processing steps were reviewed and documented. The segmentation model was designed to operate on aggregated, pseudonymized data and integrate compliantly into existing systems. Additionally, a governance framework was established to legally oversee future use cases.
Establishment of the use of analytical models
Although substantial amounts of data and relevant analyses were available, their systematic application in operational processes had not yet been fully established. The challenge was to transform segment-based insights into concrete takeaways, such as effective campaign planning or field personnel management strategies.
Solution:
Segment data was regularly fed back into the CRM via the loop system and enriched to enable direct operational use by the sales team. Practical training, guided pilot projects, and interactive dashboards supported the integration of analytical models into daily workflows. The emphasis was on tangible use cases to facilitate data-driven decision-making and ensure its integration across teams.
“We didn’t just build a model, we collaborated closely with the team to ensure its effectiveness in real-world applications. Many colleagues only realized the potential of data-driven approaches in target group analysis when the segments became visible in the dashboard.”
Florian Timmler, Junior Data Visualization Specialist
Hopmann Marketing Analytics