Winston Francois builds revenue operations for biotech and pharma companies where sales, market access, and medical affairs each own a slice of the commercial relationship, and gross-to-net makes the top-line number lie. We embed as your fractional RevOps team and build a forecast leadership can actually trust.
The Problem
Three Teams Touch Every HCP Relationship, and None of Them See the Whole Picture
Sales logs a call in the CRM, an MSL logs a scientific exchange in Veeva, and market access tracks a formulary conversation in a spreadsheet nobody else opens. Each team can tell you what happened in their lane, but nobody can answer a basic question: is this account actually progressing toward a prescription, or are three people independently convinced they are the one moving it forward. Forecasting off any single system undercounts the relationship and overcounts the certainty.
Your Revenue Curve Is Shaped by Payer Decisions, Not Sales Effort
A standard commercial forecast assumes more calls and more reps move the number. In biotech and pharma, a launch can stall for six months waiting on a P&T committee decision that has nothing to do with how hard the field team is working. RevOps built on a generic sales-activity model reads a formulary delay as a sales execution problem, sends the wrong intervention, and burns credibility with the board when the fix does not move the number.
Gross-to-Net Turns a Clean Revenue Number Into a Guess
Rebates, chargebacks, 340B pricing, and wholesaler fees sit between the invoice price and what actually lands. Finance builds gross-to-net in one model, commercial builds a pipeline forecast in another, and the two rarely reconcile because they were built by different teams on different assumptions about timing. Leadership ends up choosing which number to believe in a board meeting, which is not a position anyone wants to be in.
Specialty Pharmacy and Distributor Sell-Through Data Arrives Weeks Late and Does Not Match Your CRM
Direct sales activity gets logged the day it happens. Specialty pharmacy dispense data and distributor sell-through numbers show up on a lag, in a different format, at a different level of granularity, from a different vendor portal. RevOps teams either wait for the lagging data and forecast blind in the meantime, or forecast off CRM activity alone and get surprised every quarter when the sell-through numbers land.
Every Tool Change Has to Clear Compliance Before It Touches Commercial Data
A RevOps fix that would take a week at a standard SaaS company – a new field in the CRM, a changed attribution rule, a new integration – has to clear IT and compliance review first, because the data underneath touches regulated HCP interactions. Teams either avoid fixing broken pipeline logic because the review cycle is not worth it, or they route routine RevOps work through a validation process built for clinical systems, and the backlog never clears.
The Forecast Model Cannot Survive a New CFO or a Board Question
Forecast logic gets built in a spreadsheet by whoever owned RevOps at the time, with assumptions embedded in cell formulas nobody documented. When a new CFO or a board member asks how a number was derived, the honest answer is often that the person who built the model left the company and nobody fully understands the pipeline stage weightings anymore. That is not a minor inconvenience in a regulated, investor-scrutinized business – it is a credibility problem the moment it surfaces.
How We Help
We start with a data audit, not a forecasting workshop.
From the audit we build a single source of truth for the commercial pipeline – one data model where a sales touch, an MSL interaction, and a market access milestone all attach to the same account record instead of living in three disconnected systems.
Next we rebuild the forecast model itself around what actually drives biotech and pharma revenue: payer coverage milestones, formulary timing, and launch curve stage, not generic sales-stage probabilities borrowed from a SaaS template.
On the gross-to-net side, we build the bridge between the commercial pipeline forecast and finance's gross-to-net model so both teams are working off the same assumptions about rebate timing, chargeback lag, and 340B mix.
We also build the reconciliation layer for specialty pharmacy and distributor sell-through data – a defined process for how lagging sell-through numbers get matched against direct sales activity and folded into the forecast once they land, instead of treated as a surprise every reporting cycle.
Every change we make is scoped against compliance requirements up front, so IT and QA see exactly what does and does not touch regulated data before we build it.
We work embedded, not project-and-hand-off. The person who built your forecast logic is the person your finance team calls when the board asks a question about how a number was derived, because a forecast model nobody can explain is worse than no forecast model.
What we deliver
- Cross-system pipeline audit mapping sales, MSL, and market access records to a single account view
- Unified commercial data model reconciling CRM, Veeva or equivalent MSL system, and market access tracking
- Forecast model weighted to payer coverage and formulary milestones instead of generic sales-stage probability
- Gross-to-net reconciliation bridge aligning commercial forecast with finance's rebate, chargeback, and 340B assumptions
- Specialty pharmacy and distributor sell-through reconciliation process with defined lag handling
- Documented forecast logic and stage definitions that survive a CFO transition or a board question
- Compliance-scoped change process for future RevOps builds against regulated commercial data
A biotech forecast that treats a stalled P&T committee decision the same as a stalled sales rep will always be wrong in the same direction, and no amount of CRM hygiene fixes a forecast model built on the wrong driver.
Our Methodology
We run in 90-day sprints. Days 1-15 are the data audit: pulling records out of every system that touches the commercial pipeline and mapping where the same account exists in conflicting states across sales, MSL, and market access. Days 16-45 are the build: the unified account model, the reworked forecast logic, and the gross-to-net reconciliation bridge with finance. Days 46-75 are instrumentation, run against a live forecasting cycle rather than a backtested model, so we see where the new logic holds up against a real board reporting deadline. Days 76-90 are handoff and documentation, including the compliance sign-off trail for anything that touched regulated data.
This is different from a traditional RevOps consulting engagement in one specific way: we do not deliver a forecasting framework and leave finance to implement it. We build the actual data model and forecast logic inside your existing systems, reconcile it against your gross-to-net numbers with your finance team in the room, and stay embedded through at least one full forecasting cycle so the model gets pressure-tested before we step back.
How We Work
The first 30 days are diagnostic. We embed with commercial, finance, and IT to pull the real state of your pipeline data across every system that touches an account, and come back with a prioritized reconciliation plan instead of a slide deck restating problems you already know about.
Days 31-60 are build. We are inside your CRM and forecast model directly, working alongside whoever currently owns RevOps and finance's gross-to-net process, not handing off a spec and checking in monthly. Weekly working sessions replace status calls, because a status call does not reconcile a broken account record.
By day 90 the forecast model is live and has run through at least one real reporting cycle, not a simulation. From there we typically stay on as a fractional team, weekly during the first full quarter after handoff and biweekly once the model has proven stable, so you have direct access to the people who built the logic instead of a rotating account team relaying questions.
If your biotech or pharma company needs a forecast leadership can defend in a board meeting, we should talk.
- One reconciled account view across sales, MSL, and market access
- Forecast logic weighted to payer and formulary milestones instead of generic sales stages
- Commercial forecast and gross-to-net numbers that match without a manual reconciliation exercise
- A defined process for folding lagging specialty pharmacy and distributor data into the forecast
- Forecast documentation that survives a CFO transition or a board question
- Fewer RevOps fixes stuck in a compliance backlog built for clinical systems
If your biotech & pharma company needs revenue operations leadership, we should talk.
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Frequently asked questions
How do you reconcile sales, MSL, and market access data when each team uses a different system?
We build a unified account model that pulls records from the CRM, Veeva or your MSL system of record, and market access tracking into one reconciled view, rather than asking any team to change how they log activity day to day. The reconciliation happens in the data layer, matching accounts across systems by the fields that actually identify the same HCP or institution, not by hoping the naming conventions already line up.
Why does a standard sales-stage forecast model not work for biotech and pharma?
Standard forecast models assume deal progression is driven by sales activity, and weight pipeline stages accordingly. In biotech and pharma, a deal can sit fully engaged and stalled for months waiting on a payer coverage decision or a formulary review that has nothing to do with sales effort.
How do you handle gross-to-net when it does not match the commercial pipeline forecast?
We build a reconciliation bridge between the commercial forecast and finance's gross-to-net model so both are working from the same assumptions about rebate timing, chargeback lag, and 340B mix. The goal is not a perfect single number, since gross-to-net always carries some estimation, but a documented gap that both teams understand and agree on instead of two forecasts nobody can reconcile.
What happens when specialty pharmacy or distributor sell-through data arrives weeks after direct sales activity?
We build a defined reconciliation process that treats the lag as an expected part of the forecast cycle rather than a surprise. That means forecasting on direct activity and known patterns in the interim, then folding sell-through data in as it lands with a clear method for adjusting prior projections instead of quietly revising the number and hoping nobody asks why it moved.
Does this work have to go through the same validation process as our clinical systems?
No, and treating it that way is exactly what stalls most RevOps fixes in this industry. We scope every change against what actually touches regulated data before we build it.
What does a revenue operations engagement cost?
Engagements are scoped fractional, typically in the range of $12K-$30K per month depending on how many systems need reconciliation and how involved the gross-to-net bridge work is with finance. That covers an embedded team working inside your actual pipeline and forecast model, not a fixed deliverable list, because the audit phase usually reveals where the real cost sits.
Is this a fit for a company that has not built a formal RevOps function yet?
Yes, and it is often more straightforward than fixing a RevOps setup that already has years of undocumented spreadsheet logic in it. Companies at Series A or B that have been forecasting off a founder's spreadsheet or a sales leader's gut sense get a real data model and forecast process built from a clean slate, instead of untangling assumptions nobody wrote down.
