HCP Segmentation: Strategy, Challenges, & Future

HCP Segmentation: Enhancing HCP Outreach, Overcoming Challenges, and The Prospects

HCP segmentation has been playing a wide role since the advent of Pharma 4.0. It divides HCPs into clusters or groups based on their uniqueness, prescribing behaviors, working principles, and so on. And it will grow in this industry since it has diverse applications. Some of them are identifying target consumers and sales efforts, building customized communication strategies, and driving flawless HCP outreach.

The two generic strategies in HCP segmentation are (a) grade-based strategy and (b) Smart algorithm (AI/ML-based approach).

In grading-based approach, it involves assigning grades to varied characteristics and features of HCPs (like HCP’s uniqueness, prescribing volume, practice setting, etc) and then positioning them into segments (such as low value or high value, etc) based on their total score.

In terms of the ‘smart algorithm’ based approach, uses statistical models to group HCPs into categories based on many associated predictors.

Transformation in Strategies of HCP Segmentation Over the Years

In recent years, with the boom of cloud storage technologies, AI, and big data tools, strategies for HCP segmentation have shifted from a mere grade-based model to flawless machine learning algorithms.

Some of the significant drawbacks of utilizing a simple grade-based traditional approach are that it can be time-consuming and resource-exhaustive to grow and sustain the essential grade systems. Moreover, an inherent bias creeps up into allocating grades to varied characteristics in the scoring model that assumes that the greatest prescribing HCPs are the ultimate and most profitable to follow. However, if all products follow the highest prescribing HCPs, this segment will become the most competitive and minimize the relevance. Finally, this strategy may not reveal how convoluted it is for HCPs to make decisions and prescribe medicines.

A better methodology to group and target high-value HCPs is to amalgamate various data sources, such as EHRs and claims, call activity, etc., and further build clustering models to pinpoint the highest-value HCPs that the pharma companies should target. This records the multi-dimensionality of HCP prescribing behavior. With an ML model like that, pharma companies can also recognize the key drivers influencing HCPs to be put in a high and low-value segment.

Few Key Queries to Address when Crafting an HCP Segmentation Module to Maximize Outreach

What are the attributes to be used to group the HCPs?

These could involve (a) demographic factors- uniqueness, practice setting, and location; (b) psychographic factors such as values, attitudes, and lifestyles (c) Analytics and digital data like interactions with digital platforms and resources.

Who are the appropriate HCPs for the segmentation?

These will rely on the particular therapies or products being marketed, as well as the overall goals of the segmentation effort.

How will the segmentation be strategized? Will it have relied on an easy and inaccurate scoring system, an AI/ML model, or will it incorporate a more qualitative approach? How will the nomenclature of the segment be defined and carried out?

The segments to be implemented, how will they be utilized? Will they be used to customize marketing and sales efforts, build educational resources and programs, or other motives?

Regarding the maintenance and updates of segmentation over time, how will that be carried out?

As HCPs’ preferences and needs change from time to time, it will be critical to review and update the segmentation to ensure that it remains relevant and accurate.

Win Over the Challenges in HCP Segmentation

HCP segmentation renders itself vital for the pharma market, more now than ever. However, it comes with certain hurdles. Overcoming these challenges is crucial for pharma companies to realize the full impact of segmentation and subsequent engagement efforts.

Privacy and Compliance- Pharma companies must adhere to strict regulations like HIPAA, GDPR, and industry-specific standards. Ensuring compliance includes fetching and storing data meticulously while maintaining transparency about its usage and managing HCP agreements.

To navigate these hurdles, companies must obtain transparent clearance from HCPs for data collection and utilization. They must prioritize strong data encryption, secure storage, and anonymization methodologies for safeguarding confidential or private information.

By incorporating privacy management software and setting up compliance monitoring teams, pharma enterprises can maintain adherence to regulatory and data privacy requirements while enabling accuracy in HCP segmentation.

Data Silos- One of the key challenges is the presence of data silos within pharma companies. Marketing, sales, and medical teams often operate separately from each other, leading to fragmented and incomplete data.

It is noted that disconnected data is incapable of providing a comprehensive view of HCPs, resulting in incomplete or imprecise segmentation.

The contemporary data approach needs to integrate data across departments to build a unified picture of HCP touchpoints. Embracing Customer Data Platforms (CDPs) or incorporating CRM systems with analytics tools can centralize data efficiently and leverage seamless data exchange. Periodic cross-functional meetings and data-sharing protocols can further encourage collaboration and break data silo barriers.

Gaps when rendering Technology- Several enterprises rely on outdated systems that cannot efficiently handle or analyze the vast datasets generated from multiple touchpoints. These outdated systems often cannot work in synergies, rendering it challenging to build a holistic HCP profile or deliver any actionable insights from the data.

Non-integrated platforms or legacy systems can make data processing complicated, making leveraging and analyzing data inefficient and hampering the segmentation processes. An absence of flawless integration can also lead to data silos, which presents challenges associated with inconsistent or incomplete datasets.

Pharma needs to meet these hurdles by integrating advanced analytics tools, AI-driven platforms, and cloud-based solutions that offer seamless data integration amongst all systems and sectors. It is also crucial to train employees in advanced technologies that ensure the impactful usage of these systems.

The Future Prospects Calls for Dynamic Segmentation

The definition of pharma analytics has changed with the rapid advancements in data volume and processing potential. Processing and storing gigabytes of data within minutes or seconds has laid the foundation for running convoluted algorithms that identify the best promotional sequences or journeys for particular HCPs and comprehend their channel preferences for content HCP engagement. This transformation calls for segmentation and targeting capabilities to be more agile and customer-centric.

Here comes the role of dynamic segmentation- a transformation automated through AI and utilizing all available datasets, including patient data, NPP data, speaker programs, switch behavior, payer data, and demographic data. This strategy allows for periodic analysis, enabling a more precise and customized targeting strategy for each HCP.

Some pharma companies have already started implementing dynamic segmentation and the best approaches towards it are- (a) top-segment targeting based on recent sales performance (b) traditional modeling where statistical models are leveraged to foresee the future prescription potential (c) machine learning models powered with varied dimensions and ensemble ML tactics to identify prescription tendency with better accuracy (d) Focused segmentation where profitable targeting is carried out based on prediction and HCP switching behavior among segments.

Conclusion

As HCP segmentation becomes a mainstream strategy toward better patient outcomes in the pharma industry, enterprises need to figure out how to address the unique challenges. While the solutions are laid down in this article, it is evident that data-driven segmentation is the future.

Unless and until the pharma companies start to invest in tools and leverage employee training, there is a high chance that the industry will struggle to keep up with the expectations of Pharma 5.0.