Pharma Launch Analytics: Metrics, Data and a 20-Week Build Plan

Pharma launch analytics: the short answer

Pharma launch analytics is the reporting and analysis that tells a brand team, every week from launch day, whether a new product is tracking to its forecast and, if not, exactly where patients are getting stuck. For a specialty or limited-distribution product, that means following each patient from referral through benefit verification, prior authorization and first shipment, not just counting prescriptions. The work has to be built before launch, because the data contracts, definitions and dashboards take months, and the first six months decide most of the outcome.

Most guides on this topic are written by analytics platform vendors and focus on the retail metrics every brand already tracks: new prescriptions, total prescriptions and share. Those matter, but for a specialty launch they can show a brand on plan while shipments run well behind, because a written prescription is not a patient on therapy. The gap sits in the hub and specialty pharmacy data, and that is where launch analytics earns its cost.

This guide covers the metrics that matter at each stage of a launch, which data source answers each one, a week-by-week build plan, a worked example with numbers, how to choose between building, buying and hiring help, the costs we can source, and when you should not buy a launch analytics platform at all. It is written for commercial operations, brand analytics and launch leads at mid-sized pharma and emerging biotech companies.

Why the first six months carry so much weight

Why launch analytics is different from brand reporting

IQVIA's launch excellence research found that the first six months of a launch have a disproportionate influence on later success for at least 80 percent of launches in any country. Deloitte's 2020 study of 149 drugs approved in the US between 2012 and 2017 found the same pattern from another angle: 36 percent missed analyst expectations in year one, and 70 percent of those underperformers kept missing them in years two and three. A slow start rarely recovers on its own.

That changes what reporting has to do. Mature brand reporting looks backwards on a monthly cycle and explains share movement. Launch analytics has three jobs that brand reporting does not:

• It works against a committed plan. Every metric is read against the launch forecast's own assumptions: referrals per week, approval rates, time to first fill, persistence. When actuals diverge, the question is which assumption broke.
• It runs weekly, sometimes daily. A monthly report on a launch tells you about a problem roughly six weeks after it started. By the end of quarter one that is a missed quarter.
• It has to diagnose, not just measure. "Shipments are 25 percent behind" is a status. "Prior authorization denials at two regional plans are driving most of the shortfall" is a decision. Getting from one to the other requires patient-level data joined across sources.

Deloitte's figures also show why specialty brands need this most. Specialty drugs met or beat expectations more often, 72 percent against 48 percent, and averaged more than US$1 billion in three-year revenue, more than double nonspecialty products. The stakes per launch are higher, and the path to therapy has more steps that can fail.

Pharma launch metrics by stage

Pre-launch

Before launch the metrics are about readiness, not performance. Track whether every data feed is contracted, tested and mapped, whether the forecast has been broken into weekly, measurable assumptions, and whether target prescribers and accounts are loaded into CRM with clean identifiers.

Weeks 1 to 12

For a specialty product this is the patient funnel. Measure each step against plan and watch the conversion rates, because volume can look healthy at the top while patients stall in the middle.

Months 4 to 6

Once the early adopters are writing, the question becomes whether adoption is broadening across territories and specialties, and whether patients who start are staying on therapy.

Months 7 to 18

By now the launch has a shape. Analytics shifts towards persistence, source of business and forecast re-basing, using claims data alongside your own specialty pharmacy and hub records.

Which data answers which launch question

Every launch metric depends on a data source, and each source has blind spots. The table maps the common sources to what they answer well and where they fall short. Refresh frequency depends on your contracts, so confirm it with each provider before you design a weekly cadence around it.

The important point for a specialty launch: the first three rows are your own data, they arrive fastest, and they are the only sources that show where an individual patient is stuck. Yet launch dashboards are often built around audits and claims because those are what the platform vendors connect to out of the box. If your product moves through a limited network, start with specialty pharmacy data analytics and add the purchased sources around it.

The second point is definitions. Each specialty pharmacy reports status in its own codes, so "pending", "on hold" and "active" mean different things across partners. Mapping them into one model before launch, as we describe in our guide to specialty pharmacy status codes, is what makes referral to first ship comparable across the network. If you are still choosing a claims source, our Komodo Health vs IQVIA comparison covers how to test both.

How to build launch analytics: a 20-week plan

A worked example: on plan by prescriptions, 27 percent behind on patients

The numbers below are illustrative, built to show the pattern rather than taken from any client. Picture a specialty brand in month three of launch, distributed through four specialty pharmacies, with a forecast of 300 referrals and 165 first shipments for the month.

• Referrals: 310, slightly ahead of plan. A prescription audit would show the brand on track.
• Benefit verification: 279 completed within five days, 90 percent.
• Prior authorization: 230 of the 279 needed one. Of the 200 decided so far, 116 were approved, 58 percent, against 75 percent assumed in the forecast.
• First shipments: 45 patients who needed no prior authorization, plus 75 approved patients shipped so far, gives 120 first shipments against a plan of 165. That is 27 percent behind.

A prescription-based dashboard reports this launch as healthy. A patient funnel built from hub and specialty pharmacy data shows the shortfall, and shows that it sits almost entirely in one step. The next drill-down, denials by payer and denial reason, is where the decision comes from. In this example most denials come from two regional plans and cite missing step therapy documentation. That points to a specific fix: the hub and field reimbursement team give affected practices the documentation those plans require, and the brand team tracks approval rates at those two plans weekly until they recover.

Without patient-level data the team would see the shortfall a month later in shipment totals, with no explanation. With it, they see the cause in week two of the month. This is the core of launch analytics for specialty brands, and the same funnel feeds the patient access KPIs that access and hub teams already track.

Build, buy or hire: how to choose and what it costs

There are three ways to get launch analytics in place, and many companies combine them.

On cost, be careful with any number you are quoted without detail. The launch analytics platforms we reviewed in October 2026 do not publish list prices, and data licences for audits and claims are negotiated by therapy area, history and term. The parts that are public are the BI licences: Microsoft lists Power BI Pro at US$14 per user per month and Power BI Premium Per User at US$24, both paid yearly. A launch dashboard read by 40 people on Pro licences therefore costs US$6,720 a year in licences. The larger costs are the data contracts and the engineering to join them, and our breakdown of Power BI consultant cost covers how build work is usually priced.

A practical rule: whichever route you take, the specialty pharmacy, hub and distributor data will need a proper data layer. Our guide to the pharma commercial data warehouse explains what that layer holds and why it outlasts the launch.

Common launch analytics mistakes

• Starting after launch

  Data contracts, file testing and status mapping take months. Teams that start at launch spend the critical first quarter on plumbing instead of decisions.

• Tracking prescriptions, not patients

  For specialty products a written prescription is the start of the journey. Measure first shipments and time to therapy, or the dashboard will look better than the launch.

• Metrics with no link to the forecast

  If the dashboard does not compare each metric to the forecast assumption behind it, the team cannot tell which assumption broke.

• Mixing sources on one chart

  Audit, claims and specialty pharmacy counts will never match. Label every number with its source and document the bridge between them.

• Unmapped status codes

  Each specialty pharmacy uses its own status codes. Without one patient status model, conversion rates across the network are not comparable.

• Too many dashboards

  A launch needs one weekly view that leadership actually reads, with drill-downs behind it. Twenty tabs that nobody opens slow every decision down.

When you do not need a launch analytics platform

Not every launch needs a new platform or a large build. You can probably manage with less if:

• Your network is small. A product dispensed through one or two specialty pharmacies with a single hub can often run its launch on a clean, automated weekly report from those feeds, plus the audit data you already license.
• It is a line extension. A new indication or formulation of an established brand usually fits into existing reporting with a few new cuts, rather than a separate launch build.
• Volumes are tiny. For an ultra-rare product with a few dozen patients a year, case-level tracking by the hub team may answer more than any dashboard.
• You have no budget for the data. A launch analytics tool without patient-level hub and pharmacy feeds behind it will only report the audit numbers you already have.

In each of these cases, spend the money on getting your own data clean, linked and on time. That foundation serves the launch and every brand review after it.

How Greenwolf helps with launch analytics

Our founder worked on specialty pharma commercial analytics at ZS, including specialty pharmacy and hub data, patient access and claims, and our pharma commercial analytics practice builds launch tracking as part of that work: a launch view that brings early prescribing, specialty pharmacy referrals, access and field activity together from week one. We do not resell data or platforms, so our recommendations follow your questions and your systems.

On a launch we help write the questions and forecast assumptions, specify the specialty pharmacy and hub data feeds, build the data layer and status mapping, and deliver the weekly launch dashboard in the BI tool you already use. For a specialty therapy provider whose identifiers did not line up across sources, clean identifiers and automated pipelines cut manual reporting work by 60 to 80 percent. Testing uses synthetic or de-identified data, and production work runs in your own environment. All of it sits within our wider specialty pharmacy analytics practice.

Questions about pharma launch analytics