In conversation with Fierce Pharma, Anthony Billinger, senior director of Compass Strategy at Veeva Systems, sheds light on the rapidly evolving world of commercial data in life sciences—and why the industry is due for a serious upgrade.
Billinger outlined the mounting challenges stemming from a shift toward specialty drugs, diverse routes of administration, and fragmented distribution channels. With over 75% of new FDA approvals falling into the specialty category, the traditional reliance on retail pharmacy data is becoming increasingly obsolete. “These medications are now flowing through non-retail channels,” Billinger said, pointing to oral oncology drugs as a prime example.
Legacy data models are struggling to keep pace, causing inconsistencies that hinder commercial teams. According to Billinger, over 90% of attendees at a recent Veeva forum reported difficulties reconciling conflicting data sets—a problem that costs companies time and clarity.
To combat this, Veeva introduced its Compass Prescriber and Compass National platforms, designed to offer an integrated, projected view of prescriptions and procedures across more than 4,000 brands. The result? Enhanced visibility at every level—from national to HCP—and streamlined access through an unlimited-use model.
The impact is measurable. One oncology client using Compass uncovered a 30% mis-prioritization rate in their HCP targeting and identified a missed market opportunity worth over $500 million.
Whether you’re in commercial ops, analytics, or product launch planning, Billinger’s insights are a wake-up call to rethink how data is sourced, projected, and applied.
Don’t miss the full conversation to hear how Veeva is transforming data access and strategy in real-time.
Chris Hayden: Welcome, everyone. Thank you for tuning in today. My name is Chris Hayden. I’m a producer here at Fierce. Today, I’m joined by Anthony Billinger, Senior Director, Compass Strategy at Veeva. Anthony, let’s take a moment and let you introduce yourself to our audience.
Anthony Billinger: Sure. Thanks, Chris. Yeah. Anthony Billinger, I’m our Strategy Lead for Compass Prescriber and Compass National. These are our two projected data products within the Veeva Compass Suite. I bring about 15 years of commercial experience in the life science industry. Prior to this most current role, I spent three years within our analytics consulting practice, supporting small biotech to large pharma, really with core commercial. Before joining Veeva, I had about nine years of experience on the industry side, spanning time in the field, doing field sales, key account management, as well as leading commercial operations and sales analytics, and really began my career in the industry, leading both syndicated and custom market research projects.
Chris Hayden: So I love it if you could please share with us the current state of commercial data for life science organizations as you see, and kind of what challenges are they facing given the different trends in the landscape today?
Anthony Billinger: Yes, that’s a great question. The current state of commercial data is really antiquated, right? We’ve seen a couple of trends in the industry landscape that have a significant downstream impact on commercial data. First is the innovation of specialty drugs. Specialty drugs being those medications that treat rare, complex, chronic health conditions typically very pricey as well. In the last several years, this is now three-quarters of FDA approvals. If you take a step back and look at the industry pipeline, about 7,000 products in the pipeline, three out of four of those are specialty products, right? And the influence that has from a commercial data standpoint is these products are not routing through your traditional Walgreens CVS retail pharmacy of old. These are coming through non-retail channels. We see reimbursement now spread across pharmacy benefit as well as medical benefit. Kind of almost double clicking within that is this growth of orally administrative products within complex disease areas We still see IVs and infusionals are often the predominant route of administration. Orals continue to increase. Again, if we look at the industry pipeline, tell us where we’re going continue to head. Taking oncology, for example, about a third of pipeline products for oncology in the industry are orally administered. So again, as we see this continued rise of orals and specialty markets, that distribution model really shifts away from retail channels to heavy non-retail, whether it’s specialty pharmacy, specialty distribution. We even start to see some GPOs contracting for orals in what’s more of a legacy buy and bill model, which just adds further disruption to the data model.
And the third trend that really matters is the continued evolution of mixed routes of administration. First is at a market level. So let’s take multiple myeloma, for example. Orally-administrated products, you have infusions, you have CAR -T, you have subcutaneous injections, right? All of those are coming through different routes or are all different routes of administration, coming through different distribution models, being reimbursed differently. We also, though, see this mix of administration, route of administration, at a product level. So most commonly, this is a product that’s first approved as an IV infusion, and a couple of years down the road, a subcutaneous formulation comes out. So while we have a number of different data products, ultimately, when you look at them from a more holistic level, they’re not aligned. Our insights into non-retail data, such as distributor or sell-in data, that’s provided at an institution level. So it’s unprojected, and it doesn’t provide visibility down to the HCP. Meanwhile, our HCP level projected data, which really should be a gold standard for a lot of our commercial operations and commercial analytic functions, is highly skewed towards those retail pharmacy, which again we just talked about, it is not really where medicine is today and it’s definitely not the future. We actually just had a data and analytics forum on the West Coast a couple of weeks ago Chris, and 90% of the attendees there decided that they’ve experienced either challenges or delays trying to either tie together these different disparate data sets, or actually most of them, the highest-cited hurdle was trying to explain differences in data sets. Hey, why does market share at a national level say X, but when we look at it sub-nationally, it’s Y. We can’t really explain it two different data sets. This has been a big hurdle for the industry.
Chris Hayden: In addition to the changing landscape, which you’ve just expertly laid out, that was fascinating. And the gaps in the current data model that you were talking about, are there other hurdles facing the industry?
Anthony Billinger: I think the other big hurdle is the data access model. So, you know, in addition to what we just discussed, generally speaking, we are working in pretty dynamic environments, right? For example, most data providers are really forcing us to outline a static market basket and then we’re sitting and waiting for data delivery. We’re working in an environment where we have new reimbursement codes coming in or new competitors, or new formulations. Working in this environment where we can’t always anticipate every single question that’s going to come our way. So trying to access data through these static market baskets just doesn’t really support efficient, effective work in that environment. I think about my time in operations and analytics – as a customer you shouldn’t feel nickeled and dimed if you have to make an adjustment to market basket, right? We shouldn’t be waiting weeks or months just to get our original data, right? We shouldn’t be held up weeks or unfortunately months working through contracting or TPAs between our data source and our analytics vendor, right? As a customer, we should feel and actually have control of the data that we’re buying. But again, that legacy data model just has really impinged that.
Chris Hayden: I know, it’s fascinating. It’s changing so quickly. It’s hard to keep up, isn’t it? And have you found that the technology, I mean, is new technology helping or kind of hurting that battle that you’re having?
Anthony Billinger: Yeah, I mean, so new technology from a Veeva standpoint, thinking about modern data and data technology, that’s where Veeva recognized that there are these significant gaps. Both not only in terms of what’s in the data, but also that access model I just talked about. So from a Veeva perspective, what we brought to the market to solve that, it’s kind of two-fold, right? We look to solve that lack of integrated data. So we launched Compass Prescriber and Compass National. These are two projected data assets that are projecting both prescriptions and procedures for over 4,000 brands. The underlying data network, as well, Chris, is – I think multidimensional is maybe the best descriptive word. So we are including those legacy kind of open sources that we’ve seen, but also closed sources. We’ve pulled in the retail and non-retail channels of distribution. And that approach, then, really gives us a complete view of the market. So we talk about those markets where we have orals, we have subcutaneous injections, we have infusions. You’re now able to account for all those routes of administration and all the routes of distribution through this model. So it’s giving you visibility to the full market. And it’s also provided a lot of visibility into commonly blocked drugs and HCOs that are just gaps, again, in the legacy data model. So that’s been a big opportunity and improvement.
And then, building on the idea of the integrated data, we also provide what we call full visibility. So it’s the same standard metrics that you get at a national level, provided at a state level, provided at a zip level, provided at an HCP level. This idea of full visibility means whether a customer is working on a national forecast, if they’re working on field force sizing, trying to understand market share at a geography level, they can really rely on a single source of truth. Going back again to what we’ve heard time and again and that personal experience, trying to explain differences in data, moving as much as we can to one single source of data for these activities, again, whether it’s an HCP level up to a national level. The third piece of that, talking about the data access model as well, we’ve also kind of flipped that model on its head. We operate on an unlimited data access model. So you license data by the brand. As a customer, you have unlimited access to that data to answer questions for the brand.
Chris Hayden: So Anthony, can you give us an example of how some of these capabilities are being used by customers and the benefits and business outcomes they’re achieving?
Anthony Billinger: One of the most common examples is improving HCP prioritization, segmentation, and targeting. So thinking of a customer in the oncology market, again, have a mixed right of infusional products and orals and based on those legacy data models, retail -based projection data provided no value, similarly, that non-retail account level data, just incomplete for them. And also, it’s not providing that prescribing level of visibility. And so they ultimately settled on using a roll-up of raw patient claims data, which I’ve actually seen a number of times – that’s not isolated to this one customer. And I get it, right? As an end user, you can use that patient data. It gives you HCP-level level visibility, it’s giving you visibility into both the medical and the Rx side of the business, but the big problem is that it’s not projected. And so there’s inherent biases in terms of coverage from any data set, right? Any patient data set, and you don’t know what those are. So when we actually re-ran that HCP prioritization with them using Compass Prescriber, what we found was pretty incredible. About 30% of their top targets were mis-prioritized when they were just using the unprojected patient data.
30%. That’s significant, just that by itself, right? And then we started to say, okay, well, what was the impact on market share for these HCPs? So about 25% of those top targets saw market share at an individual HCP level that shifted at least 10% for one product. What that means is that a doctor who had been segmented as a loyalist for product A on the patient data, for example, was actually more of a traditionalist. And then another clinician who we thought was really a traditionalist based again on patient data was actually a biosimilar advocate, right? And so when you considered the combination of the mis-prioritized HCPs as well as the mis-segmented HCPs and look at the go-to -market strategy and where they’re expecting to source business. You actually calculate that missed market opportunity. It’s over $500 million dollars, Chris. I’ll give you one more too, since we’re at it. The customer is moving into a new market. Again, not surprisingly, this market includes orals, injections, and infusional products, kind of same old story at this point. And so this methodology that Compass provides, it allowed them that complete view of the market. Again, all these products, routes of administration. They had actually identified several different segments of HCPs, which could be easily identified through a typing tool in the market. With Prescriber, we could segment essentially the universe of physicians in about an hour. So in this instance, they’re able to, literally in an hour or two, identify and segment those clinicians. And then that allows, because of the ease of that, hey, we’re doing it here at 18 months out. Okay, now we’re 12 months out from launch, let’s run that again and just double check. Hey, have we seen any shifts in these physician segments in the volume of them over the last six months? Okay now we’re six months out, we’re about to launch, we know this competitor just launched, are we starting to see shifts? So that ease of use of it as well allowed them to dynamically measure any changes in segmentation over time and change their go-to market or commercialization strategy. That segmentation provided key inputs into salesforce sizing, territory design, their marketing plan, and even forecasting, updating their forecasting as they moved closer to launch.
Chris Hayden: That’s great. Well, Anthony, thank you so much for joining us today and lending your expertise in your time. We really appreciate it. That was a really good conversation.
Anthony Billinger: Thank you Chris. I appreciate the opportunity.