Sales Territory Mapping Explained 

causaLens x Syneos Health: Using AI to modernise Territory Planning for Commercial Pharma Teams. Speakers: Oliver Matthews (causaLens), Tim Jones, SVP Commercial Excellence and Business Intelligence (Syneos Health), Brian Davinski, Senior Director of Strategic Operations (Syneos Health).

Oliver Matthews: Welcome everyone, and thanks again for taking the time. This is the first webinar in a series on how causaLens and Syneos Health are partnering to deploy AI in commercial pharma operations. My name's Oli Matthews. I'm part of the life sciences go-to-market team at causaLens, and I'm joined by Tim Jones and Brian Davinski from Syneos Health. Tim and Brian, if you could do some quick intros, that'd be great.

Tim Jones: Thanks, Oliver. I'm Tim Jones, Senior Vice President of Commercial Excellence and Business Intelligence. I run a lot of the strategy, analytics and targeting teams, including field technology and incentive comp. I spend a lot of time working with our customers to translate their business challenges and solve them with technology. Happy to be here today.

Brian Davinski: Thanks. Good to be here with everyone as well. Brian Davinski, Senior Director of Strategic Operations. I lead the targeting and alignment function at Syneos Health, and I work closely with Tim to bring the technology and tools he develops to our customers.

Oliver Matthews: Great. Over the next 20 minutes or so, we want to move beyond the general conversation about AI and focus on what adoption looks like in practice: why Syneos Health decided to invest in AI and Digital Workers, what that investment means for the pharma clients they serve, and how AI is beginning to change the way commercial teams operate. We'll use targeting and territory alignment as our worked example, an area where decisions have traditionally required significant time, manual analysis and specialist input. We'll explore how Syneos Health and causaLens are turning that into a production AI capability, and share learnings about data, governance and adoption, while keeping human expertise at the centre of the process. Tim, let's start at the beginning. Before we get into the technology, what was happening in the market and inside Syneos Health that made investing in AI a strategic priority?

Tim Jones: Thanks, Oliver. The pressure point wasn't really that we saw AI coming and becoming popular. It was that commercial operations in general was becoming extremely complex and very fast-moving, and the amount of data we needed to make decisions was just increasing. Traditional ways of working weren't going to be enough. On top of that, we were asking ourselves more complex questions, and our customers were asking smarter questions about where they need to focus, which customers to prioritise, how to adapt their territories, how to optimise channels, and in general, how to make better, faster decisions with more confidence.

Internally, we were seeing the same problem. Extremely talented people doing really high-value work, but a lot of their time was spent assembling data, running manual analysis, reconciling assumptions and creating point-in-time solutions. By the time we gave this to our customers, it could be a week or two weeks later, and that solution may not even be relevant anymore, or may have missed some nuance. Things were moving incredibly fast. We saw the opportunity to use AI and Digital Workers to compress a lot of that data assembly and analysis.

But for us, it didn't start with the AI. We didn't create technology for its own sake. We first looked at what AI tools we actually needed, and what data we needed to make sure those tools were doing what we needed at a very complex level. So we started investing heavily in our data. We created proprietary data sets that fuel this process, and then used that to create technology that props up our people. Our people are the ones who know the story and the detail. They can connect what they're seeing in the data and the answers they're getting from AI tools to their customer strategy. So it became a more scalable commercial operating model, not just creating AI for the sake of AI.

Oliver Matthews: Awesome. So it sounds like the internal teams are very supported by AI, it's helping them be more efficient, and you're building it into the operating model within Syneos. Thinking about how the client experiences this, what might feel different in the customer experience as AI becomes more integrated into the way you work?

Tim Jones: It's funny, I was talking to a customer the other day, walking through how we use our channel propensity data and our mindset engine data around messaging, how we bring it all together and look at targeting in a different way. At the end, his response was, "Amazing. So I have to forget everything I've learned over the last 30 years." He was joking, and it got a big laugh. But the change here isn't that we forget everything we've learned over the years. It's how we use these tools to evolve the way we think, and pressure test what we've learned in the past to see what still works and what needs to change.

That's the difference when you work with a company like Syneos. We're going to move faster, we're going to think about things a little differently, and we want to share those experiences and best practices with our customers. Sometimes that means challenging preconceived notions. It doesn't mean our customers will always follow everything we see, but we'll always provide a view: this is what's going on right now, here's what other people in the industry are doing, here's what we think based on our experience. And we can do that a lot faster.

Previously, if you asked us for a targeting exercise, it may have taken a week or two to go through the entire process because of the amount of analysis needed. Now we can talk about these things in the moment using these tools. With alignment, it's: how do we make an investment right now, instead of "let me get back to you in a week"? That reduction in cycle time and improvement in consistency is super important. One of the things I love most is the ability to run multiple scenarios to see which will be best, rather than working on one scenario for a week, deciding it isn't the result you need, and going back to try something different. Now it's simultaneous scenario creation, so you know you're getting to decisions faster. That's one of the greatest things we've been able to do, and it drives a lot of our decision making right now.

Oliver Matthews: Amazing. So speed is one of the key aspects: moving very fast, and being able to look at a lot of data and use it in a variety of ways. A lot of the conversation about AI in commercial pharma is around the field, such as copilots for reps, next best action and content generation. These are all valuable, but part of the engagement between causaLens and Syneos has been thinking about how the opportunity is broader than the field. Could you walk us through where else you're thinking about applying AI within Syneos?

Tim Jones: The field use cases are extremely important, but they're only one part of the commercial value chain. If AI is only applied at the rep level, you may help one individual have a better interaction with a customer, but you're probably missing the larger upstream decision opportunities. Think about using AI from the beginning for message planning, forecast planning and deployment planning, all the size and structure work we're talking about today. Even things like creating AI personas of your potential customers and putting content in front of them to see what they might say about it. That's becoming a really popular way to message test.

There's so much you can do with AI, and that's before we talk about operations: how you run a project, how IC works, creating dashboards to the same degree you're used to with Power BI. AI is surrounding the commercial operations programmes so that everything moves faster, not just the reps. That's where AI becomes more than a productivity tool. It becomes the commercial design engine. The real value comes when insights are connected to those workflows. From a targeting perspective, those decisions inform everything you do, connecting the pre-planning activities to what we expect to happen in the field. And then there's a whole other conversation about how AI helps you understand the interactions that are happening, and creates a learning loop back into the process so you make better decisions.

Oliver Matthews: Thanks for sharing, Tim. causaLens is excited to be helping Syneos build out this commercial engine, with AI solutions across commercial pharma. Switching gear a little, let's go into one of the applications more specifically. Targeting and alignment is the area we're focusing on today. Brian, we know precision targeting has changed quite a lot over the past few years. What's actually different now in the data and methods compared with a few years ago?

Brian Davinski: Thank you. Tim talked about the investment and commitment to data that Syneos has made over the past several years. That allows us to be more sophisticated in how we target HCPs than was traditionally possible. Traditionally, targeting was very volume-based. You targeted HCPs on patient volume, maybe prescription volume, layered in a little market access data, and for years that's how the pharma industry did its targeting. With this data, we can access many more data points and know so much more about each HCP. For instance, we now have propensity data that tells us which channel of promotion influences each HCP, whether that's face-to-face, digital or virtual. We also have insight into whether an HCP is an early adopter, whether they're a brand loyalist, and whether their practice is growing. All these data points let us be more sophisticated and intentional in how we target HCPs.

Oliver Matthews: It's super valuable to target HCPs in a more sophisticated way, and we're seeing a lot of benefit from it. Thinking about the broader territory alignment process, where else do you see AI accelerating things and providing value?

Brian Davinski: That's now making its way into the territory alignment process, which has traditionally been very manual and time-consuming. I see three main benefits. The first is efficiency. The targeting and alignment professionals on our team have years of experience, and this lets them be more efficient. An alignment that might have taken hours, a day, or even multiple days for a very large alignment can now be done in minutes, or at most hours, because the tool gets you 75 to 80% of the way there. The analyst then uses their expertise to fill in the gaps, rather than doing all the manual data entry just to get to a national map.

The second is territory optimisation. Not only can analysts create territories more quickly, they can create territories that are better optimised and better at reaching HCPs, because the AI helps them come up with ideas they might not always come up with on their own. The third is flexibility. Our customers move very quickly, and this lets us move with them and make changes on the fly. Not just changes on the edges of territories, which we could do before, but changes to the entire national map. If the customer wants an adjustment, we can adjust with them in a matter of minutes, not days.

Oliver Matthews: That acceleration seems like a big win for commercial leaders. When they bring AI into their targeting and alignment process, does it help them move beyond using it around major launches, so it becomes part of an ongoing operating model?

Brian Davinski: Yes, absolutely. At Syneos, we see this as the way forward, replacing older and, honestly, increasingly outdated mapping software and tools. I wouldn't think of it as something you only use for a launch or a particular situation. It becomes the way of doing business going forward.

Oliver Matthews: We've talked a lot about AI, and it's been very valuable within this process. But Syneos is filled with targeting and alignment experts, and we want to make sure their expertise stays at the heart of the process. Where do you see the human analyst playing a role in this new AI-driven world?

Brian Davinski: I love this question, because there's a lot of thought about AI out there. You hear rumours that it's replacing people. In our view, it's enhancing our professionals and allowing them to reach their full potential and do the things they're so good at. They have years of experience and know things about the geography, like the traffic patterns in certain territories, or that a territory can't be built a certain way because there's a bridge you have to account for. Before, they couldn't put their full focus on that, because they were manually doing a lot of data entry and trying to keep their heads above water just getting the alignment created. You couldn't harness all their native genius and creativity. Now, with the AI making them more efficient, they can. Humans working with AI to make the humans better, and getting the most out of our people: to me, that's the perfect combination.

Tim Jones: I'd add that every strategy requires judgement, and AI isn't going to be able to make that call. It can give you a lot of assumptions and recommendations, but that's where the human comes into play: understanding the client's strategy, pressure testing those assumptions, identifying what's a real risk and what's only a perceived risk, and explaining the trade-offs. That interaction isn't something AI can do for you, not in a real way. You wouldn't just ask ChatGPT, "How do I take my drug to market?" and follow what it says. There has to be expertise. There are things only a human can do, and if you can eliminate the tasks they were previously doing, like the data work, they can spend more time on strategy. That's where the most value lies.

Oliver Matthews: So it's AI-driven, but human-led, that really brings the value out of these solutions. On a practical note, if a client wanted to adopt this approach with Syneos, the thought is often, "We're not ready for AI," or "We need to do something first." How quickly could they expect to see something useful?

Brian Davinski: They could see something useful right away. Syneos has been partnering with causaLens and working hard to build this. It's something we've built, have ready, and will be introducing to the market in the coming weeks. Of course, it has to be right for the customer and their business, the right situation and application. But we have a tool we're excited about, and we're ready to partner with our customers to bring it to market and deliver value.

Tim Jones: It's the classic buy versus build. When a customer wants to launch a new product, they don't need to be worrying about this kind of technology. They need to worry about the success of their brand. By working with Syneos, they get the expertise and tools that help them do that. All they really need to bring is the right questions and strategic understanding, helping us understand their goal. When they wake up in the morning and look out the window, what do they see? If they can translate that, we can help with the tools we already have in place, and they don't have to worry about the technology or AI adoption in their own teams. They can focus on their strategy and how to mobilise their entire team towards it. That's one of the greatest strengths of working with Syneos: it takes away the tech readiness question. How do I create adoption? You don't have to, because we've already adopted these tools, and we can be that partner for you.

Oliver Matthews: So it's about helping customers stay focused on the questions that matter for their business, and offloading the tech adoption burden to Syneos.

Tim Jones: And because of the initial investment we made in data, if you're a new launch and maybe don't have any data, perhaps it's your first product coming to market, Syneos has the data to help you. You don't have to figure out where the data's coming from or what you need. You might have some initial data sets, and we can use ours to enhance them. Especially in targeting and alignment, where you might not have experience going to market with these types of customers, we can bring that experience to you and help with a lot of the targeting work Brian was talking about.

Oliver Matthews: Very exciting. We're running up to time, so thank you both for running us through all of that. We're very aware of the work going on at Syneos, and we're excited to be partnering with you on the journey to an AI-enabled commercial function. I'd love one takeaway from each of you for the audience.

Tim Jones: The lesson we've learned ourselves, and that we're trying to bring to our partners, is that AI only really creates value when it's connected to a real decision or workflow that helps us be better. Technology is exciting, but the impact comes from using it to help commercial teams make better choices, faster, with more confidence. That's the real takeaway when it comes to the AI tools out there today.

Brian Davinski: I'd add to what Tim said: AI and the human element together are what will drive us forward and add value for companies, their employees, our customers, our customers' customers, the HCPs, and ultimately the patients, which is what we do all this for.

Oliver Matthews: Amazing. Thank you very much, Tim and Brian, and thanks to everyone for joining us. If you'd like to learn more about AI for targeting and alignment, or any other part of commercial pharma operations, feel free to reach out to any of us and we'll be happy to help or point you in the right direction. Stay tuned: we'll be sharing more about our AI commercial transformation with Syneos, on programmatic media, in a few weeks' time. Thank you very much, everyone.