In contrast, surveys that shape major business and policy decisions are often launched without proper validation. This article examines why surveys lack standardized quality checks, the risks of relying on untested data.

In this series of articles, we explore real-world use cases for synthetic data across industries — from finance to healthcare to consumer insights.

We kick things off with the Insights Industry, where surveys remain the backbone of decision-making. But with rising dropouts, disengaged respondents and growing pressure on data quality, researchers face an urgent question:

Can synthetic data help us stress-test surveys before launch?

Picture this!

You’ve spent weeks designing a survey. The logic is airtight. The questionnaire looks sharp. The client signs off. You launch. At first, completes start flowing. Then suddenly… drop-offs spike. By Q15, half your respondents have bailed. By Q20, it’s a ghost town!

Those standard questions come back to haunt your project manager - “Why is my LOI double what was promised? Why do I have so many incomplete records? Why are people skipping entire sections?”

You scramble for answers. But the truth stings: you only discovered the flaws after fieldwork began.

The Researcher’s Nightmare

This isn’t rare. In today’s world, this is the norm. Respondents are highly disengaged, and the slightest distraction - a notification, a boring matrix, a vague open-end is enough for them to move on.

That means:

High dropouts that drain budgets
Skipped questions that leave you with gaps
Inflated LOI that damage trust with both respondents and clients

The fact is, surveys are expensive to fix once they’re live.But what if you could stress test them before launch without wasting a single respondent?

Synthetic Data: Your Crash-Test Dummies

Buying a new car? Cruise Control, ADAS, Auto Climate Control, Airbags, ABS, EBD and oh yes… the Global NCAP rating - very important.

Cars are deliberately smashed into barriers. Test dummies take the hit. Sensors record every fracture, every airbag deployment, every weak spot. The point isn’t to guarantee safety in every real crash. The point is to simulate risks before real lives are on the line.

Now replace “car” with “survey.” And “test dummies” with synthetic respondents.

Dropout Simulation Train on past surveys (10k+ records with LOI + dropout markers). Models can then simulate fatigue curves: 20-min survey? 25% dropout. 30-min survey? 40% dropout.
Skipped Question Prediction Just as NCAP dummies flag where airbags fail, synthetic data can forecast where respondents will skip or abandon questions — and even fill in likely patterns.
LOI Stress Testing Add 5 minutes? Dropout climbs. Remove a matrix? Completion stabilises.

It’s not perfect foresight. But neither are crash tests. They don’t predict every accident, they flag high-probability risks before it’s too late.

The Catch

As with everything AI…. Every use case comes with a word of caution. Synthetic data can’t read minds. It won’t tell you “Q12’s wording is confusing” or “this routing is broken.”

Why? Because those issues rarely live in your historical data. Models learn patterns like “long surveys = more dropouts” or “mobile users quit faster.” They don’t yet learn design nuance.

So, use it as an early-warning system, not a silver bullet.

The Bottom Line

Survey pre-testing with synthetic data is like crash-testing a car. The dummies don’t drive in the real world. But they tell you whether the airbags deploy when things go wrong. And in a world where disengaged respondents abandon surveys at the first sign of friction, why wouldn’t you crash-test your design first?

Over to you:

How do you tackle survey drop-offs today?
What’s your best trick to keep respondents engaged all the way through?
Would you want synthetic respondents to stress test your surveys before launch?