AI-Powered Personas and Digital Twins of Customers

To keep up with customer expectations on experience, pharma must move from guesswork to precision in its marketing, transforming customer insights from a bottleneck into a strategic advantage.

Over the weekend, I did an informal survey with folks who hold senior positions in the industry. The question that I asked was “…what are 1-2 big areas that you would like your marketing teams to be better at?” 39/48 people I sent the message to responded. 35 out of those 39 said “data driven decision making”. Although said in various ways, this seemed to be a common theme. Those of you who attended my digital/marketing sessions know the importance I place on understanding customers and using insights from the data that we have about them.

Data and its privacy have always been contentious topics and I believe we prefer to err on the right side of the law, making us over cautious. Hence I say some of us are law firms that also make medicines. While regulations exist, they are meant to act as guardrails, not as Divine Decree. How many of us have challenged them or tried to actively work with other industries and shared lessons with regulators to update rules? Anyway, I digress, as the topic of the post is to see how to use customer data to help them better, than wring hands about how it can’t be done.

The pharmaceutical industry faces a unique challenge in understanding its customers—patients, HCPs, and caregivers—due to complex decision-making processes, regulatory constraints, and fragmented data. Traditional market research methods, such as surveys and focus groups, are slow, expensive, and often fail to capture real-time behavioural insights. To bridge this gap, pharma companies must consider turning to AI-powered personas and Digital Twins of Customers (DToCs). These advanced tools enable teams to simulate and predict customer responses to campaigns, messaging, and channel strategies before execution—and, crucially, evolve based on real-world interactions.

The biggest advantage of AI-powered personas: is that they are beyond static customer profiles. Traditional buyer personas are static, generalized representations of customer segments based on historical data. In contrast, AI-powered personas leverage machine learning (ML) and natural language processing (NLP) to dynamically model customer behaviours, preferences, and decision drivers.

The AI-powered persona rapidly enhance pharma marketing by allowing predictive response testing. AI personae simulate how different patient or HCP segments react to messaging variations, allowing marketers to optimize campaigns before launch. They also allow marketers to optimize channel use by analysing past engagement patterns. AI predicts which channels (email, webinars, video, social media, rep visits) resonate best with each segment. And of course, they allow what is now a must-have, personalization at scale. AI continuously refines personae based on new interactions, ensuring messaging remains relevant as customer behaviour shift.

For example, an AI persona of an oncologist might reveal a preference for peer-reviewed data in emails but engage more with video summaries on LinkedIn. Pharma teams can then tailor their outreach accordingly.

Digital Twins of Customers (DToCs) is a living simulation of customer behaviour. While AI personae represent segments, DToCs are hyper-personalized, data-driven replicas of individual customers. DToCs integrate real-time data—such as rep interactions, prescription patterns, and digital engagement—to create a dynamic, evolving model of each customer. I am sure you have heard of digital twins and its role in pharma manufacturing and research. It’s a lot like that.

DToCs can transform pharma engagement by allowing campaign stress-testing. Some companies test their communication through reps, advisory boards or online A/B tests. This is unfortunately, not a standardised pharma process. For best results, before launching a new drug campaign, pharma teams can run simulations on DToCs to predict adoption rates, messaging effectiveness, and potential objections. This allows a quick and near-accurate assessment of real-time adaptation. As real-world data flows in (e.g., HCPs skipping emails but engaging with webinars), DToCs update, allowing marketers to adjust strategies mid-campaign.

Such data is useless if done for one or two campaigns. The best results appear through longitudinal behaviour tracking, where customer behaviour, data points and changes are tracked over a period of time (ideally 8+ quarters). Unlike one-off surveys, DToCs track evolving preferences, helping brands anticipate churn or identify high-potential leads. For instance, a DToC of a cardiologist might show declining engagement with traditional sales rep visits but increased interaction with AI-powered medical chatbots—prompting a shift in engagement strategy.

The true power of AI personae and DToCs lies in their ability to learn and adapt. By integrating omnichannel engagement data (emails, CRM logs, social media), behavioural triggers (e-prescribing trends, webinar attendance) and other external signals (new clinical guidelines, competitor campaigns), these models refine themselves, ensuring that marketing strategies remain aligned with actual customer behaviour rather than outdated (>1 month old) assumptions.

To keep up with customer expectations on experience, pharma must move from guesswork to precision in its marketing. AI-powered personae and DToCs enable pharma marketers to move from reactive, intuition-based strategies to predictive, data-driven engagement. By stress-testing campaigns in a virtual environment and continuously refining models based on real-world behaviour, companies can:

✔ Reduce wasted spend on ineffective messaging

✔ Improve HCP and patient engagement through hyper-personalization

✔ Stay agile in a rapidly evolving healthcare landscape

As AI and real-world data integration mature, pharma companies that adopt these tools will gain a decisive competitive edge—transforming customer insights from a bottleneck into a strategic advantage.