Marketers are increasingly making the case for utilizing AI and machine learning to achieve better audience segmentation and targeting. This #TrendTalks panel discussion, sponsored by Boundless Life Sciences Group, focused on how pharma marketers are securing leadership support of data-driven efforts and leveraging data and analytics in their marketing strategies.

Panelists admitted that, while they see tremendous opportunities for using tools such as AI and predictive modeling in their practices, their efforts are often met with resistance in a traditionally risk-averse industry.

“The analytic model of an organization often determines openness to innovation/risk of piloting an AI initiative,” said Sonja (Sparkle) Fisher, associate director, U.S. patient marketing at Vertex Pharmaceuticals (formerly). “You have to find a way to justify it and get buy-in. You need to be able to prove that this fits a business need, that doing it in a data-driven way sets expectations of what the output is going to be.”

“Especially when it comes down to the dollars, you have to start small. There needs to be a track record of proven success,” added Kelly Tullo, omnichannel marketing director at AstraZeneca.

Securing support for data-driven projects is just the first step. Panelists agreed that one of the biggest hurdles to using data analytics tools successfully is the ability to tap into a large and robust database that can provide fodder for actionable insights. The integrity of the data set is critical. “Historically, data sets can have massive gaps, specifically from a diversity perspective,” said Sandy Sexton, senior director, Dupixent consumer marketing at Regeneron.

Even when data does exist, internal constraints and data silos can also be an obstacle to accessing the information. Forging external partnerships with data sources has helped several panelists access data that has led to meaningful patient solutions.

“Our analytics teams know our data really well,” said Claire Phillips, marketing director, anti-infectives, GSK. “In a partnership, they can level up that data and play an important role in identifying the best inputs for the objective without having to build the programming that spits it out,”

“Organizations that are smart enough to keep in-house IT and form external partnerships to learn from have done much better than those that outsource,” added Fisher “Tech is still trying to figure out how to help pharma and pharma is still trying to figure out how to truly leverage data.”

A highly successful partnership with Walgreens allowed Fisher to build a predictive model based on insights culled from the retailer’s massive database. “Even I was shocked at the level of insights and data patients were sharing with Walgreens regarding comorbidities and engagement,” she said. “Leveraging that information to target communities in-store as well as online gave us  extensive visibility of the opportunity for our initiative. It also turned the tide on leadership buy-in.” Fisher is pursuing partnerships for additional pilot programs to target communications and education programs designed to empower patient groups.

Panelists agreed that while analytics can help direct messaging, field reps remain an essential source of information. “Field reps understand our customers better than the machine understands the customer, but the machine can give them insights or suggestions,” noted Tullo. “AI and machine learning can arm us with data reps never had before about their customers. It’s been great from an insight perspective. We can give recommendations and reminders about key messaging and triggers.”

“Aside from the field, another way we’re using AI is to get a more robust and faster understanding of our customers,” said Phillips. “There’s the idea that segmentation is attitudinal and you can only get it through qualitative, in-person interviews. There are other ways we can look at segmentation. We need to gain as much real-world insight as we can.”

Marketers are also using AI tools to better zero in on specific patient groups and target messaging more effectively. “We’re hearing about the importance of segmentation and customization and where the patient might be along those journeys. We’re trying to build solutions that harness generative AI as a tool to get there,”[1]  said panel co-moderator Francesco Lucarelli, chief commercial officer, Boundless Life Sciences Group.

The team Sexton works with is using machine learning to pinpoint the right day and time the majority of patients are opening  email and to test control subject lines for effectiveness.

AI can also be used to better recruit and monitor diverse patient populations. “One of the challenges with recruitment in under-served communities is [getting the] tools that the patient population needs for follow-up monitoring. Using AI for the end goal to help a patient population that stands to benefit most from drugs in development is an interesting opportunity,” noted Sexton.

The experts on this panel felt that rather than replace people, generative AI provides an opportunity to increase human productivity, to augment the marketer.

Phillips noted that lift from non-personal omnichannel marketing efforts are most significant when an assigned rep is part of the effort. “It’s optimization and supplementation, but not replacement,” said Lucarelli.