> ## Documentation Index
> Fetch the complete documentation index at: https://runrehearsals.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Use-Case Playbooks

> End-to-end examples of how teams run simulations for pricing, ad creative, product preference, and willingness-to-pay

Each playbook below follows the same shape: the question you are trying to answer, how you would
set it up, what the output tells you, and the real-world result from validation.

The setup steps in every playbook happen in the product: you describe the study in the
[intake chat](/docs/platform/new-rehearsal), [build and screen the cohort](/docs/platform/building-cohorts),
[upload any stimuli](/docs/platform/content-and-data), then
[launch and read the report](/docs/platform/running-a-rehearsal).

## Playbook 1: Should we change our pricing?

**Question:** If we raise the price or add a cheaper tier, how many customers stay, switch, or
churn?

<Steps>
  <Step title="Screen for the right population" icon="filter">
    Add a screening question so you only interview people the decision affects, such as "Do you
    personally pay for this subscription?" Getting this wrong is the most common way a pricing
    study goes sideways.
  </Step>

  <Step title="Describe the change neutrally" icon="pen">
    Give replicas the scenario (current price, the new options) without editorializing. Avoid
    lines like "unfortunately we have to raise prices."
  </Step>

  <Step title="Offer symmetric choices" icon="scale-balanced">
    Present accept, downgrade, and cancel as balanced options, and ask for the reasoning behind
    the choice.
  </Step>
</Steps>

**What the output tells you:** the predicted distribution across choices, plus the reasoning.
Usually it comes down to a mix of inertia, value perception, and individual budget constraints.

<Info>
  Validated: on the Disney+ 2022 price increase, replicas predicted the accept/switch/cancel
  distribution with 91% accuracy and screened the paying population within 4.3% of ground truth.
  They correctly captured that about 94% of subscribers simply absorbed the increase.
</Info>

## Playbook 2: Which ad creative should we run?

**Question:** Of two or more creatives, which will actually perform?

<Steps>
  <Step title="Target your real audience" icon="users-viewfinder">
    Build a cohort that matches who the ad is for, for example "women 22 to 35 earning \$100K+".
  </Step>

  <Step title="Show the creatives as stimuli" icon="image">
    Upload the ads and let the researcher present them during the interview. Use the Compare Ad
    Creatives template.
  </Step>

  <Step title="Ask reaction-first questions" icon="comment">
    Get unaided reactions ("what would you do after seeing this?") before any forced comparison,
    and never name the weakness you suspect.
  </Step>
</Steps>

**What the output tells you:** which creative wins, by how much, and the real engagement drivers
behind it. These are often things that surface-level best practices miss.

<Info>
  Validated: across near-identical ad pairs, replicas picked the higher-reach ad 89% of the time
  versus 65% for a general AI. In a paid-ads case study with Mera NYC, replicas predicted the winner
  72% to 28%, matching the real result: the winning ad earned a much higher return on ad spend. The
  general AI predicted the opposite winner.
</Info>

## Playbook 3: Which product will the market choose?

**Question:** Given a lineup of options at different price points, what does the market pick?

<Steps>
  <Step title="Use a representative cohort" icon="chart-pie">
    For market-share questions, weight the cohort to your target population so the distribution is
    meaningful.
  </Step>

  <Step title="Lay out the full lineup" icon="list">
    Present each option and its trade-offs, then ask which they would choose and what drove it:
    price, features, brand, or social signaling.
  </Step>
</Steps>

**What the output tells you:** the predicted share for each option, and the decision drivers that
separate them.

<Info>
  Validated: on the iPhone 17 lineup, replicas matched real Q4 2025 sales distribution with 97.6%
  accuracy, reaching the same conclusion through simulated individual decisions. That held up even
  against models that had market-share data in their training set.
</Info>

## Playbook 4: What is this worth to customers?

**Question:** What will people actually pay?

<Steps>
  <Step title="Use the Test Pricing template" icon="tag">
    It asks a proven set of pricing questions that find the range people will pay, instead of the
    leading "how much would you pay?"
  </Step>

  <Step title="Anchor to a real product" icon="anchor">
    Ask about a specific item the replica was already considering, and keep explicit prices out of
    the question text.
  </Step>
</Steps>

**What the output tells you:** the price range people will actually pay, and the point where it
tips into too expensive.

<Info>
  Validated: across about 300 products (\$20 to \$2,200), replicas predicted what people would pay
  about as closely as that same person would land on a different day.
</Info>

## Before You Launch Any of These

<CardGroup cols={2}>
  <Card title="Write neutral questions" icon="circle-question" href="/docs/methodology/asking-good-questions">
    Framing distorts results more for AI than for humans. Get this right first.
  </Card>

  <Card title="Plan how you'll read it" icon="magnifying-glass-chart" href="/docs/methodology/interpreting-results">
    Know what a surprising result means before you see one.
  </Card>
</CardGroup>