---
title: How Fast Can Your Team Actually Build an Audience?
description: Most brand teams wait weeks for a new audience. See how Forian Studios delivers one in minutes, with 300+ propensity models pre-loaded.
image: https://blog.forian.com/hubfs/A%26M%20Blog%202%20(1).png
---

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 Nov 4, 2026, 12:52:58 PM

# How Fast Can Your Team Actually Build an Audience?

<https://blog.forian.com/author/insights>

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### *An audience in minutes. A custom model in hours.*

 

Forian Consumer Studio and Forian HCP Studio are [audience-building platforms](https://blog.forian.com/introducing-forian-consumer-studio-and-forian-hcp-studio) designed to help healthcare marketers reach the right patients and healthcare professionals using real-world claims data. Built on Forian's [CHRONOS](https://forian.com/chronos/) data foundation, the two studios make it easier to build, refine, and activate clinically informed audiences without the traditional time and resources required.

Ask a brand team how long it takes to get a new audience and the answer usually comes back in weeks.

You write the brief and file the request. It goes to an analyst who is already working through three others. A segment comes back, the definition turns out not to be quite what you meant, so you file a revision. Three weeks later you have one audience, and you are committed to it, mostly because there is no time left to try another.

Forian Studios was built to take that constraint out, and it gets removed from three places.

## Nothing to Implement

Most healthcare data engagements start as a project: scoping calls, a data science team, a modeling sprint, and a delivery date somewhere on the far side of the quarter.

Forian Studios arrives pre-loaded. At first login, 300+ clinical propensity models and 950+ consumer attributes are already built and already scored across 250M+ consumer identities. Forian HCP Studio works the same way across 1.9M+ NPIs. Setup takes about as long as provisioning the account, and there is no implementation phase to sit through.

The 300+ models cover real ground, not a sampler. They span therapeutic areas from cardiometabolic and oncology through respiratory, immunology, and behavioral health, and each one covers condition likelihood, treatment stage, and care-seeking behavior. For most briefs, the model you need is already there. For the ones where it is not, see below.

## ![forian-studios-first-login](https://blog.forian.com/hs-fs/hubfs/forian-studios-first-login.jpg?width=732&height=366&name=forian-studios-first-login.jpg)

## Describe the Audience, Get the Count

The second constraint is translation. You know the audience you want, and then you have to express it in whatever the platform will accept.

In both studios, you describe it in plain English. "Adults over 50 with high propensity for a recent Type 2 diabetes diagnosis in the Southeast who have not started a GLP-1." Ask Forian writes the query, runs it against the data, and returns the audience count in the same turn. If the query errors, it corrects itself and runs again. You see the logic it generated and the number it produced together, so you can check one against the other.

Then you iterate. Widen the age band. Tighten the propensity threshold. Add a geography and watch the count move. Each change is a sentence, and each answer comes back in seconds instead of in a follow-up meeting.

You also see where the audience is. A density map renders it by ZIP code as you build, so a national count becomes a picture of concentration, which matters when the media plan is regional and the audience turns out not to be.

The whole loop, from describing the audience to saving it, runs in minutes.

## A Custom Model in Hours

Sometimes the pre-loaded models do not cover what you need: a niche indication, a specific line-of-therapy transition, or a definition of the ideal patient your team has spent years refining.

The studios [build that model with you](https://forian.com/activationandmeasurement.html). You define the target population in plain English against claims, with reference data and AI assistance shaping the definition, and automated model building handles feature selection, training, and validation against the claims-observed cohort. What used to be a data science project scoped in quarters is a working custom model in hours.

Forian HCP Studio does the same from a list you already own. Upload your NPIs and it profiles what those HCPs have in common in the claims, then generates a look-alike audience of prescribers you are not currently reaching.

## What Speed Actually Buys You

Faster turnaround is the obvious benefit. What matters more is how many attempts you get.

When an audience takes three weeks, you get one, and then you defend it for a quarter. When it takes twenty minutes, you can test five cohort definitions in an afternoon, see how each one sizes, compare them, and choose based on the numbers rather than on whoever argued hardest in the planning meeting.

That plays out concretely. A team planning a launch can size the newly diagnosed population, then the treatment-experienced one, then the subset that switched therapies in the last six months, then each of those constrained to their priority DMAs, and have every one of those numbers in front of them before lunch. A definition that survives that exercise is a considered choice. A definition that survives a three-week queue is usually just the one that got requested first.

Being able to test cheaply, early, and before the media budget is committed is what moves campaign performance. Not one model in particular, but the number of times you get to check your thinking.

## Try it on Something Real

Bring one therapeutic area and one campaign objective to a working session, and we will build the cohort live. Most teams leave with a sized, saved audience before the call ends.

It is also the fastest way to learn whether the pre-loaded models already cover your indication, or whether you need a custom one, which is a useful thing to settle before a planning cycle rather than in the middle of one.

[Activation & Measurement](https://blog.forian.com/tag/activation-measurement)

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