Run a Pricing Study

Test willingness to pay and packaging with synthetic respondents before you commit.

Pricing decisions are high-stakes and hard to validate. Traditional methods — surveys, focus groups, price testing — take weeks and require large sample sizes to be meaningful.

With iMario, you can simulate how different customer segments react to your pricing before you commit to anything.

When to Use This

You're launching a new product or tier and want to pressure-test your price point
You're considering a price increase and need to understand potential pushback
You want to compare how different segments — enterprise vs. SMB, US vs. international — perceive the same pricing

Step 1: Build Your Audience

Use Audience Mode to generate a group of synthetic individuals that represent your target buyers.

Write a brief that reflects who actually makes or influences the purchasing decision. For example:

"Five procurement managers at mid-sized SaaS companies in the US, with annual software budgets between $50K–$200K, who evaluate tools based on ROI and ease of implementation."

Aim for 5–20 individuals to surface meaningful variation. If you want to compare segments, create two separate audiences — for example, SMB buyers vs. enterprise buyers — and run them in parallel using a multi-flow task.

Step 2: Configure Your Audience Node

In the Audience Node, select the individuals you generated in Step 1.

If you're running a multi-flow task to compare segments — for example, SMB buyers vs. enterprise buyers — assign a different audience to each flow. Keep everything else identical across flows so the results are directly comparable.

Step 3: Set Up Your Discussion Guide

In the Content Node, select Discussion Guide and structure your questions in three layers:

Anchor their current reality first

"What do you currently pay for tools in this category?"
"How do you typically evaluate whether a tool is worth the price?"

Then introduce your pricing

"If this product were priced at $X per month, what would be your first reaction?"
"At what price would this feel like a good deal? At what price would you start to question the quality?"

Then probe the edges

"What would need to be true for you to justify this to your team or finance?"
"Is there a pricing model — per seat, usage-based, flat fee — that would work better for how you operate?"

This structure mirrors a real pricing conversation, which produces more grounded responses than asking about price directly.

Step 4: Configure Your Report

In the Output Node, click Report #1 to open the configuration panel.

Report Length: Standard Report — 9–15 pages of balanced depth. A pricing study generates enough nuance across segments that an Executive Brief would compress too much, but a Deep Dive is rarely necessary at this stage.

Report Tone: Consulting — findings are framed as decisions, not observations. The right tone when results are going to a team that needs to act on them.

Insight Depth: Full Chain — Findings + Insights + Recommendations. For pricing research, raw observations aren't enough. You need to know what the resistance means and what to do about it.

Evidence Style: Representative — surfaces the most illustrative individual responses without flooding the report with raw data. Useful when you want to show stakeholders the reasoning behind a finding, not just the finding itself.

Dimensions of Analysis: Turn on Sentiment Analysis and Demographic Breakdown. Price sensitivity almost always varies across segments — these two dimensions are where the most actionable differences will surface.

Methodology Section: Keep this toggled on if you're sharing the report externally. It documents how the simulation was structured, which builds credibility with stakeholders who aren't familiar with synthetic research.

Format: PDF for sharing with stakeholders, Markdown if findings are going into an internal doc or Notion.

What to Look For in Your Results

A good pricing study surfaces three things:

The threshold — where price stops feeling reasonable and starts feeling like friction
The justification logic — how buyers rationalize the cost internally ("it pays for itself if...")
The model preference — whether your pricing structure matches how they think about value

When your report is ready, focus on:

Analysis — where does resistance cluster? Look for patterns across segments, not just overall sentiment
Responses — read individual answers to understand the reasoning behind reactions, not just the reactions themselves
Contradictions — an outlier who accepts a higher price often signals an upsell opportunity worth pursuing

Use these findings to refine your price point, adjust your packaging, or reframe how you communicate value on your landing page.