SPEAKERS:
- Chris Binder, Associate Principal, Market Access and Transformative Solutions, CustomerInsights.AI
- Jon Knowles, Customer Engagement Director, Market Access and Transformative Solutions, CustomerInsights.AI
KEY TAKEAWAYS:
· Financial viability and commercial feasibility are analytically distinct tests that current contracting methodology treats as identical
· A signed payer deal revealed 50% of projected opportunity required reaching prescribers nobody was calling on
· The COE's hardest challenge is organizational access to sensitive data, not analytical sophistication
· Attribution scoring—not prescriber lists—is the tactical entry point for winning cross-functional cooperation.
· The minimum viable version of this blueprint is a single pre-deal feasibility checkpoint, not a full organizational build.
The hundred-million-dollar assumption
"A lot of times the contracting teams will do a top-down analysis...if I improve access over here, I'm going to get the market share I have over there and that's it. A hundred-million-dollar decision gets made."
Chris Binder's description of standard payer contracting methodology is not a critique of analytical rigor. It's a description of how the industry actually works. A contracting team benchmarks an analog payer, models the share improvement a better formulary position should produce, calculates the rebate level at which the deal remains profitable, and signs. Math confirms the decision. Nobody asks whether the commercial organization can physically deliver the patient volume the math requires.
That omission has a specific shape. Binder framed it in terms any contracting leader would recognize: "If we have to go from getting 1 out of every 10 new patients in the market to getting 8 out of every 10 new patients in the market, it might be financially viable, but it's not commercially feasible."
Financial viability and commercial feasibility sound like synonyms. They are not. One is a modeling exercise. The other is an operational test. Pharma's standard payer contracting methodology performs the first and skips the second—routinely, at nine-figure scale, without anyone treating the gap as a problem worth solving.
The test that doesn't exist
The standard contracting analysis is not wrong. It answers the question it asks. The issue is what it doesn't ask.
The financial model calculates whether a rebate level, applied to a projected share improvement at a given price, produces acceptable net revenue. That is a necessary input to any decision. What doesn't calculate is the operational requirement embedded in the share projection: how many new patients must the commercial organization acquire, at what rate, from what prescriber base, to make the projected share real?
Binder put the analytical distinction precisely: "That initial analog top-down analysis is focused on financial viability...Then we want to ask: is it commercially feasible? What has to happen for that to be true? How many new patients do we have to add to yield that many TRxs to get that market share and is that realistic?" The conversion from share target to required new patient acquisition isn't a refinement of the existing model. It's a different category of analysis entirely, one that connects payer deal parameters to field execution capacity for the first time.
Why doesn't this analysis exist in most organizations? Because it requires data that lives in separate functions. Binder's observation that "this should be a continuous set of analytics-in the current model; a lot of teams are doing these in silos" identifies the structural mechanism. Contracting teams confirm financial viability using payer and rebate data. Commercial teams plan targeting using prescription data. Neither team has reason to cross-check its assumptions against the others, because neither team is accountable for the gap between them.
The consequence of that silo structure landed in a specific case study. "We did an analysis where we found out after a deal had been signed that only half of the opportunity was sitting with called-on prescribers." Half. After signing. The deal passed every financial test the contracting team applied. It failed the commercial feasibility test that nobody ran—meaning the pull-through strategy required reaching a prescriber population that existing field coverage didn't include, through non-personal promotion channels that hadn't been resourced. The vulnerability profile here is not uniform across the industry. Companies with concentrated payer portfolios, where a single formulary decision represents outsized revenue exposure, carry the most unhedged risk. So do products in competitive therapeutic categories where new patient acquisition is a zero-sum contest among several promoted brands. Organizations where contracting and commercial targeting report through separate leadership chains face the highest structural barrier to running the feasibility test, because the data required to do it is politically distributed across functions that share no accountability for the answer.
The political problem wearing an analytics costume
"Those pricing and contracting teams can hold things close to the vest. Right. So how do we augment and get that seat?"
Jon Knowles' question is the one that determines whether the Market Access Analytics COE blueprint is adoptable or merely aspirational. The analytical case for feasibility testing is straightforward. The organizational case for building the function that performs it is not—because the data required to do the analysis is actively guarded by the teams whose decisions the analysis would scrutinize. This is not unusual behavior. Contracting teams manage competitively sensitive rebate structures, pricing thresholds, and deal terms that carry legal and strategic exposure. Data protectiveness is not dysfunction. It's appropriate stewardship of information that could damage the organization if mishandled. The problem is that appropriate protectiveness, applied uniformly, prevents the cross-functional analysis that would make contracting decisions more accurate.
The COE's answer to this problem is structural. Rather than asserting organizational mandate—which invites resistance—the model is built around augmentation. "It's about augmenting their work. It's not about telling them how to do their job. It's not about publishing this information across the organization. It's about bringing additional insight to those contracting teams." The COE doesn't extract data from contracting teams and publish it elsewhere. It brings analytical capacity to those teams, in service of their decisions, in exchange for the access that makes the analysis possible. A function that begins by demonstrating value to data-holding teams earns access that a function asserting organizational authority does not. The same principle applies when the COE extends its work into commercial targeting. Commercial teams that own targeting are accustomed to receiving prescriber lists-and tired of them. The tactical entry point is not another list. "Teams that own targeting commercial teams kind of get sick of having more spreadsheets of prescriber lists sent to them. But they like attribution." Attribution scoring tells a commercial team which of their existing efforts are working, which prescribers are responding, and where resources are producing returns. It enhances the commercial team's own performance narrative rather than competing with it. That is the analytical currency that buys cross-functional access.
The pattern generalizes beyond this specific COE context. Real-world evidence teams, medical affairs analytics functions, and commercial operations groups all face versions of the same dynamic: the data required to perform cross-functional analysis is held by functions that have no direct incentive to share it. The organizations where these functions succeed are consistently the ones that began by making the data-holding team look better, not the ones that began by asserting that cross-functional visibility was strategically necessary. Most COE failures are not analytical failures. They are access failures that present as analytical ones.
Discover more on this topic at Pharma Pharma USA 2027 (March 10-11, Philadelphia) - North America's largest cross-functional pharma gathering. Explore the agenda here.
The thread nobody maintains
Pre-deal, the COE performs the feasibility test: converting the share projection into required new patients, mapping that requirement against current prescriber coverage, and identifying the gap. That gap becomes the literal input for pull-through strategy—which prescribers need to be added to the call plan, which requires non-personal promotion, which represent opportunity the deal's financial model assumed but the commercial organization cannot reach. Post-deal, the same patient volume assumptions become the benchmark against which actual performance is evaluated. The data translation required to maintain that thread is the COE's technical core. As Binder described it, the COE is "positioned uniquely to be able to crosswalk that analysis from let's say PBM portal data across a payer spine, connecting to formulary policy information, connecting to third-party prescription data like IQVIA and Symphony." Contracting teams have the PBM and formulary data. Commercial teams have the Rx data. Neither function routinely traverses both, which means the pre-deal patient volume assumption and the post-deal performance measurement are built from different data sources that nobody has reconciled.
The jurisdictional complexity of that reconciliation surfaced in real time during the presentation. As the conversation moved from payer contracting into commercial targeting, Knowles flagged the boundary explicitly: "Now we're dealing with the commercial teams, right? We're looking at targeting specifically. We can get into some drift here." That acknowledgment of potential drift—the sense that the COE is crossing into territory that belongs to someone else—is not a technical problem. It's the organizational tension the COE is specifically designed to navigate. “This is a team that can play a central role in enabling cross-functional collaboration, cross-functional coordination, breaking down those silos and ensuring a single source of truth when it comes to market access." The aspiration is clear. What the blueprint requires is an organizational position with enough credibility across contracting, commercial, and analytics functions to hold the thread without any single function claiming ownership of it.
The organizational design problem disguised as an analytics problem
The Market Access Analytics COE, as Binder and Knowles describe it, is not primarily an analytics function. It is an organizational design intervention. The constituent analyses—feasibility testing, prescriber coverage mapping, payer data crosswalks, attribution modeling—exist in some form across commercial and contracting teams already. The COE's value is not performing analyses that nobody else can perform. It's occupying a structural position that gives it permission to connect analyses that currently terminate inside functional silos. That distinction reframes the mandate question. Binder's enumeration of the COE's potential scope is extensive: "Within Market Access alone, there's a lot of different directions this team can get pulled in...They can support deal analytics on the payer side, they can support GPO rebate contracting on the provider account side. There's access strategy that needs analytics, there's payer account teams, there's 340B leakage, there's specialty pharmacy performance, there's a whole bunch of different things that this team can do." That breadth is simultaneously the justification for building the COE and the primary operational threat to it. A team pulled across six sub-functions without prioritization defaults to the most urgent request rather than the most strategically consequential analysis. Scope without governance produces a shared services function, not a connective tissue function.
The minimum viable version of this blueprint does not require building a COE. It requires a single pre-deal analytical checkpoint: before any payer deal is signed, convert the projected market share improvement into required new patients, map that requirement against current prescriber coverage, and calculate what percentage of the deal's projected value depends on prescribers the commercial organization currently reaches. That number—the coverage ratio—is the feasibility test in its simplest form. The diagnostic question is direct: for your last major payer deal, can you state what percentage of the projected incremental volume required reaching prescribers outside your current call plan? If that number doesn't exist, the deal case and the pull-through strategy were built from different data, and nobody reconciled them. The COE is one way to fix that. A mandatory pre-deal checkpoint is another. What is not an option, at nine-figure decision scale, is continuing to treat the question as one that doesn't need an answer.
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Discover more on this topic at Pharma Pharma USA 2027 (March 10-11, Philadelphia) - North America's largest cross-functional pharma gathering. Explore the agenda here.
