Machine Learning (ML) Driven Segmentation & Targeting Of Physicians

    Machine Learning (ML) Driven Segmentation & Targeting Of Physicians

    5 mins read

    With the increase in the number of touchpoints for a Healthcare Physician (HCP), it has become more important for pharmaceutical companies to personalize marketing efforts at the HCP level. Segmentation forms the backbone of customized marketing and is a critical step in converting brand strategy into actionable tactics. A robust segmentation process enables effective targeting – a potential group of customers receiving communication through preferred channels from the pharmaceutical companies.

    A robust segmentation process enables effective targeting – a potential group of customers receiving communication through preferred channels from the pharmaceutical companies.

    How To VAlidate a Segmentation Process

    Before we look at traditional approaches and how ML-aided solutions can help in improved segmentation & targeting, let us look at how one can measure the advantages of a segmentation process. Following are the key factors that can be used to validate a segmentation process:

    • Meaningfulness: The segmentation process needs to be aligned with the brand strategy. For this reason, one-size-fits-all would not be the right approach, and the segmentation should be driven by brand-specific imperatives.
    • Robustness and stability: Since an HCP's behavior can gradually shift over time, segmentation needs to be updated cyclically to align with recent changes. An excellent segmentation method should not be susceptible to only a few of the underlying factors. Imagine how a sales representative would react if the tactics for a specific type of salesforce driven promotion for an HCP changes drastically every trimester!
    • Ease of understanding: When the brand manager and sales team can intuitively align with the segmentation method, the motivation to use the recommendation based on the segmentation process will be high. For example, a ""weighted average segmentation index"" created using multiple metrics could be very confusing for the sales team to understand and lead to less importance being given to recommendations provided.
    • Actionability: The segmentation process should allow for both strategic decision-making and designing of actionable tactics. An ideal method will not just answer, "this is how the different segments look," but also, "this is what can be done for each segment and subgroups within the segment."

    One of the most common approaches involves looking at the historical prescription by the HCP, for the brand and the market, and determining segments based on past prescription behavior. Though this is still a valid and widely used approach, it lacks a few key elements:


    What is the expected behavior
    in the future?


    What causes some of the HCPs to behave differently than others?


    How to inform segmentation with external factors like the current COVID-19 pandemic?

    An insight into the answers to these questions can help the pharma companies make far more informed decisions on designing marketing tactics based on segmentation. In other enhanced approaches, additional elements, such as historic patient visits, HCP demographic information, etc. are included in the segmentation process. Yet, they are only focused on historical behavior and do not provide much help in customized tactic design and targeting.

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    ML for Segmentation & Targeting

    ML methods provide a potential solution for the missing elements in the segmentation & targeting process. Augmenting some aspects of the traditional approach with an ML-driven solution provides an enhanced framework for segmentation that meets the validation criteria described previously. One such solution, described in the subsequent sections, involves estimating a valuation score for each of the HCPs to be considered in the segmentation process and using the score to define value-based segments.

    Supervised ML models provide HCP-specific estimates of next opportunity based on a multitude of predictors, such as writing behavior, HCP demographics, historical promotions, and the impact of external events.

    Unlike historical approaches of creating multiple scores based only on the historical data and combining them to get a single valuation/potential score, the enhanced approach incorporates the predicted future potential. Supervised ML models provide HCP-specific estimates of next opportunity based on a multitude of predictors, such as writing behavior, HCP demographics, historical promotions, the impact of external events (for example, incorporating the effects of COVID-19 through the change in patient inflow, affordability, and insurance provider mix). The estimated opportunity, when combined with old prescriptions, provides a holistic estimate of the HCP's overall potential. For a new drug launch with minimal history to build ML models, lookalike patient identification algorithms are used as an alternative for quantifying future potential.

    This valuation score is used to arrive at HCP segments. A higher value segment thus derived will not only include the HCPs who have prescribed the brand heavily in the past but also include medium volume prescribers who have a high probability of increased brand writing in the future. A potential target list for a specific promotion channel is identified by estimating the break-even valuation score given the cost of promotion.

    With the recent advancement towards increasing "explainability" of ML models, we now have algorithms that help in understanding which predictors and to what extent do they influence the predicted potential of an HCP being higher or lower. We use these explainer algorithms along with the predictive ML models built previously to identified HCP-specific drivers of brand writing. These key drivers are then used to create micro-clusters within each of the already identified segments. The HCPs within each micro-cluster are motivated by similar factors, and micro-cluster level tactics can be designed to communicate with them.

    At Axtria, we have helped one of the top 10 US-based pharmaceutical companies to enhance their segmentation process using the above approach and to define an efficient targeting plan customized for each of the various promotion channels available for the brand. Subsequently, a promotion response assessment was integrated into the solution to provide a recommendation on the optimal level of promotion for each channel.

    Having defined a broader approach for ML-driven segmentation & targeting, let us see how this approach fares on the segmentation validation criteria described previously. 

    • Though the underlying ML models built to estimate future potential can be complex, the final valuation score, which is a combination of historical writing and estimated future potential, is very simple and an easy to understand metric. 
    • The predictive ML models can be defined to align with brand strategy. For example, if a brand is focusing on new patients, one can build models that estimate the future new prescriptions by the HCP. In a different case, a brand can have first-line adoption as the principal source of future business. In this case, the ML models can be designed to estimate first-line drug prescriptions by the HCP in the future.
    • Since the approach anchors the valuation to historical performance, fluctuations in predicted values due to model error are subdued, making the whole process robust and stable when used cyclically.
    • Finally, the process allows defining actions at both segment and sub-segment (cluster) levels. The micro-clusters created based on identified vital drivers allow creating cluster-specific tactics. For example, brand managers can look at broader segments based on valuation scores when a decision is made on the allocation of promotion spend for a channel. Whereas customized messaging plans for HCPs can be designed using cluster-specific identified drivers.

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    Successful implementation of this segmentation approach would also include training the internal brand and sales team on the key features of the new approach. Also, it is recommended to assess the performance of the new approach in initial cycles of implementation – one such way is to design test and control studies. A salient feature of the new approach is that it provides relevant solutions that anchor to the same segmentation schema for both sales and marketing teams and hence helps to bridge a common gap present at many organizations.

    foreign-linkLearn More About Axtria's Segmentation & Targeting Capabilities

    Chandresh Awadhwal
    Written By:
    Chandresh Kumar Awadhwal
    Chandresh has around 11 years of industry experience across business analytics, strategy and operations. He has worked across multiple pharmaceutical clients and developed deep expertise in the application of machine learning for sales & marketing analytics, drug sample optimization, health economics and outcomes research and RWE data analytics. He holds an MBA from the Indian School of Business, Hyderabad specializing in Marketing.