
Diagnosing stalled growth means finding the real reason an event, membership, or subscription program has stopped growing. We combine what audiences actually did in the past with what they tell us today, using behavior-calibrated conjoint analysis, to show what is holding growth back and what to change next.
Registrations, renewals, or sales stay flat year after year.
Audience behavior has changed, but no one can say how.
Reports show what happened, but not why.
New ideas are based on guesses instead of evidence.
Most organizations have plenty of data, but it only shows what happened. It doesn't explain why others became inactive, or what each audience group values.
Surveys can fill that gap, but they have a blind spot of their own: people don't always buy what they say they will. This is called hypothetical bias. Decisions built on either source alone can make the problem worse.

We look at past registrations, sales, and renewals to see what audiences actually did, and separate real trends from noise.
We survey both groups. Inactive customers are often ignored, but they explain where value is being lost.
The survey asks each group to choose between different mixes of pricing, programs, and packages. We then calibrate those choices against the historical data, so results reflect real buying behavior, not just stated intent.
We build a predictive model from the conjoint analysis. It predicts the right pricing, programs, and packages, and maps the strategy for the next event.
A research method that checks what people say against what they actually do, by bringing two sources of data together.
Historical data on what audiences registered for, bought, or renewed.
New conjoint survey data on the choices people make between pricing, programs, and packages.
Eliminating hypothetical bias: the gap between what people say they will buy in a survey and what they actually buy with real money.
Which audience groups matter most, and what each one needs.
The leaks to fix and the untapped areas to grow.
The pricing, package, or program changes worth making, backed by real buying behavior.
A revenue model built for the next strategic step. Audience behavior keeps changing, so for each new decision we repeat the approach: combine historical data with a new conjoint survey.
Leaders of conventions, exhibitions, memberships, and subscriptions.
Plenty of reports, but no clear reason growth is flat.
A pricing, package, or program change that needs evidence first.
Quick answers about how we diagnose stalled growth.
A method that combines historical data on what audiences actually bought with new conjoint survey data on the choices they say they would make. Calibrating one against the other shows what each group values, and how they are likely to act.
The gap between what people say they will buy in a survey and what they actually buy with real money. Behavior-calibrated conjoint analysis is designed to eliminate it.
Active customers explain why they stay. Inactive customers explain why they stopped, which past data can't show.
A regular survey only captures what people say. We check those answers against what audiences actually did, then build a model that predicts what to change for the next event.
Talk to us about what the data can and can't show.
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