Personalised, timely healthcare professional (HCP) engagement is known to improve the overall customer experience, thereby contributing to enhanced patient outcomes. Changes in the post-pandemic world have introduced a variety of channels, with an acceleration of the digital transformation. Additionally, the pharmaceutical sector has become more competitive, posing challenges in meeting sales projections¹. To drive brand awareness and prescription behaviour, it is critical to have a relentless focus on driving quality personalised engagements with the highest-value customers. Achieving this requires organisations to continually invest in developing a deeper understanding of their customers. This includes periodic segmentation refreshes of their customer lists and more sophisticated context-driven engagements powered by integrated data and an insights ecosystem.

Traditional segmentation (i.e., conventional Segmentation &Targeting), a fundamental tool used by companies to develop an overall engagement strategy, relies on a straightforward approach of using a static target lists of HCPs. Various factors pertaining to HCPs, such as the patient load and disease and patient attributes, are commonly used to segment the static list. An IQVIA analysis found that up to 40% of HCPs change segments within six months, making this time- and resource-intensive exercise less effective in addressing changing market needs. Traditional segmentation's inability to adapt quickly to market factors, shifting demographics, and policy changes can leave organisations lagging. Moreover, its broad categorisation risks oversimplifying complex patient and provider needs, potentially missing targeted engagement opportunities. The IQVIA team observed that traditional methods are still widely used in the Asia-Pacific region. However, there is a growing trend of adopting “contextual recommendations” driven by artificial intelligence (AI) and machine learning technologies to maintain a competitive edge and reduce manual effort required for data collection and analysis.

At the heart of dynamic segmentation lies its unparalleled ability to define and finetune customer segmentation on demand. This method stands out by ensuring that customer groups are not only identified based on static attributes but are also continuously updated to reflect their evolving needs and behaviours. It is about keeping an organisation's understanding of its customer base as current and relevant as possible, allowing for strategies and offerings that resonate deeply and personally with each segment. Through dynamic segmentation, businesses gain the agility to adapt their customer engagement tactics in real time, ensuring they dynamically address market needs. This approach is crucial for maintaining a competitive edge and customer satisfaction in the fast-evolving healthcare sector.

Rather than viewing traditional and dynamic segmentation as mutually exclusive, there is a compelling case for leveraging traditional segmentation information as a foundation. The core principles underlying traditional segmentation, such as identifying distinguishable segments within a market based on certain criteria, remain vital. However, instead of applying these principles in a static manner, dynamic segmentation overlays them with real-time data and analytics. This approach enhances the segmentation process, making it more adaptive and responsive to the evolving healthcare landscape. By understanding that the roots of dynamic segmentation are firmly planted in traditional concepts, organisations can appreciate how this evolved approach allows for more nuanced and effective engagement strategies, without necessitating simultaneous investment in both methods.

It is evident that the role of segmentation in shaping customer-centric strategies is an initial step on this journey towards customer centricity in the evolving digital engagement era. Organisations must embrace continuous adaptation, leveraging the latest technology to stay ahead of the curve. The following are considerations for organisations assessing this path:

1. Establish a clear set of initiatives aligned with the organisation’s customer engagement strategy: Determine the right set of initiatives to operationalise the strategy, including an iterative model that allows for continuous learning.
2. Define a foundational data ecosystem: Identifying data/attributes necessary to enhance customer engagement and define an ecosystem to collect and process these data regularly.
3. Assess and embed AI: Start or continue to mature the overall AI ecosystem to optimise segmentation to anticipate customers’ needs based on defined data ecosystem that considers customer feedback as well. This paves the way for precisely timing customer engagement, leading to very effective engagement.
4. Integrate with operational systems: Ensure insights from segmentation are integrated with operational systems such as Customer relationship management (CRM), providing a feedback loop.
5. Develop a clear measurement framework: Establish clear key performance indicator (KPIs) for measuring customer engagement outcomes and periodically review them to refine initiatives.
6. Understand process impact and plan for change management: Success hinges on embedding a culture that is aligned with initiatives across all levels of the organisation. Plan adequate support for teams to adapt to new ways of leveraging data and insights.

Dynamic segmentation is not just a strategy but a commitment to evolve with the market, ensuring relevance and competitiveness in a rapidly changing landscape. The question now is not if but how swiftly organisations can integrate this approach to unlock its full potential.

IQVIA has helped companies along this maturity journey. Let us connect to further explore this subject.