At Fierce Pharma Week, I had the opportunity to talk about one of my favorite topics: how predictive AI in pharma marketing can help us make smarter decisions about who to reach, when to reach them and where our media dollars can have the greatest impact.
My premise was simple: every media dollar is a bet.
And that means that whenever we choose an audience, channel or moment to deliver a message, we’re making both a bet and therefore a prediction about what will happen next. Pharma marketers have gotten much better at placing those bets. We have richer real-world data, more sophisticated audience targeting, better buying technology and stronger measurement.
But there’s a catch: even the smartest activation technology can only optimize against the information we give it. Feed a DSP a weak audience hypothesis, and it may simply become very efficient at buying against a weak prediction.
That’s why I think one of the biggest opportunities for AI in healthcare marketing happens before the media buy.
Predictive AI Can Move Targeting from Relevance to Opportunity
Traditional pharma audience targeting often relies on proxies: HCP specialty, historical prescribing, demographics, geography, searches and other intent signals. Those inputs aren’t useless. But they can tell us that someone is possibly relevant without telling us whether there is an actual clinical opportunity right now.
A patient might fit an audience definition for months. An HCP might remain on a target list for years. The meaningful window (the point when a patient is approaching brand eligibility and an HCP may soon be making a treatment decision) can be much narrower.
That’s where predictive analytics can change the question. Instead of starting with, “Which doctors are most likely to prescribe?” we can ask:
Which patients are approaching a brand-relevant point in their treatment journey, and which HCPs are likely to be caring for them when it happens?
Better HCP Targeting Starts with Their Patients
I used a Jenga analogy on stage because, well, I like analogies, and I think Jenga might be a top 5 physical board game of all time; I’d have it over twister and lawn darts.
Traditional HCP targeting often looks for the easiest block to move: Which provider is most likely to change prescribing behavior?
But prescribing propensity is only part of the equation. An HCP who is theoretically persuadable isn’t particularly useful if they aren’t seeing relevant patients. That’s why a smarter HCP targeting strategy adds another dimension: Which providers are both receptive to the message AND likely to see brand-eligible patients in the near future? And for that, AI is the skeleton key.
In other words, start with the patient opportunity, then find the provider. This shifts targeting from historical behavior alone toward real clinical opportunity.
"Prescribing propensity is only part of the equation. An HCP who is theoretically persuadable isn't particularly useful if they aren't seeing relevant patients. That's why a smarter HCP targeting strategy adds another dimension: Which providers are both receptive to the message and likely to see brand-eligible patients in the near future? And for that, we HAVE TO use AI."
Mike Rousselle, Chief AI Officer, OptimzieRx
Synchronize HCP and DTC Marketing Around the Same Decision
Using real-world data and AI, marketers can identify patterns in the patient journey, predict upcoming care windows and connect those opportunities to treating HCPs. And once we understand the patient opportunity, something else becomes obvious: HCP and DTC marketing shouldn’t behave like two unrelated media plans.
Patients and providers certainly don’t experience healthcare that way. They’re participating in the same treatment decision.
Predictive AI in pharma marketing makes it possible to coordinate engagement around that shared moment; for example, building patient awareness ahead of an anticipated appointment while delivering relevant information to the HCP around the same care window.
Which leads to one of the questions I think pharma marketers should be asking more often:
Are we creating demand where we can fulfill it?
Generating patient awareness without preparing the provider can leave intent stranded. Surrounding HCPs with messages without engaging relevant patients leaves another part of the opportunity untouched.
We’ve seen the impact of this synchronization in real-world campaigns. In one case study I shared, HCP activation and DTC activation each independently influenced behavior. But patients who received DTC messaging and subsequently saw an HCP who had also been reached were 6.1 times more likely to receive a brand prescription than the comparison group.
That multiplicative effect is borne out in every single campaign like this that we’ve run! And that tells us that it’s not a fluke, and it’s a big enough impact that if you’re NOT utilizing this strategy, you’re undoubtedly leaving money on the table.
From Predictive AI to a Behavioral Intelligence Flywheel
As successful as this program was, there’s another point I want to emphasize beyond just the script impact: the opportunity to build a smarter marketing system.
When we can predict an opportunity, activate against it, then measure what happens, we learn from the outcome: did our marketing deliver the desired impact?
But what if we feed that learning back into our marketing system to make the next prediction smarter? That one step starts to move us from predictive AI that makes one media bet better, to behavioral flywheel that learns and improves over time.
This is where predictive AI in pharma marketing gets especially powerful. Real-world data like claims and EHR helps us predict our best bets. Those predictions inform our decisions. Our decisions create measurable outcomes. The outcomes become new intelligence that helps us make even better bets in the future.
When we think beyond a single campaign, and use predictive AI to build learning systems, it unlocks the next frontier of innovation. Because every media dollar is still a bet. And now we have the opportunity is to make it a better one.
Prescribing propensity is only part of the equation. An HCP who is theoretically persuadable isn’t particularly useful if they aren’t seeing relevant patients. That’s why a smarter HCP targeting strategy adds another dimension: Which providers are both receptive to the message and likely to see brand-eligible patients in the near future? And for that, we HAVE TO use AI.
