The ROI of truth: Why your quant strategy needs better filtering, not more data

In the race to launch the next breakthrough medical device or digital experience, executives often look to quantitative research as a safety net. The prevailing assumption is that safety lies in numbers – that a survey of 5,000 people offers more protection than a survey of 50.

However, in specialized sectors like med-tech and complex digital services, volume is often a distraction from clarity. As the barrier to entry for running surveys drops, the risk of “blind” data-driven decision-making rises.

For leadership teams tasked with Go/No-Go decisions, the challenge has shifted: it is no longer about how much data you can gather, but how effectively you can filter the noise to find the truth. To ensure your data strategy is sound, we recommend framing your approach around three critical questions for your quantitative research vendor.

“Can you reach my specific user, or just the general population?”

We have found that rigorous statistical analysis is achievable with smaller, highly targeted sample sizes. The goal is not to find more people; it is to find the right people. This allows organizations to move faster without sacrificing the statistical validity required by regulatory bodies and stakeholders.  Precision is a function of representation, not just high volume.

For example, if we are assessing localized language in a specific country, broad fluency is sufficient as a filter. However, if we are assessing a specific feature or app workflow, we must target the actual end-user. Context and specificity are the only ways to ensure the data reflects reality.

 “How do you prove your respondents are human?”

A major emerging risk is the infiltration of bots and “synthetic data” (AI simulating human responses as a 1:1 replacement). While synthetic data has applications in forecasting, it cannot simulate the user experience or the cognitive struggle of a patient using an injection device or a customer navigating a complex UI.

Your quantitative research partner must act as the “guardian” of your data, applying aggressive due diligence to ensure every data point comes from a real human experience – going beyond accounting for impossibly fast survey takers, straight-liners, and the like – but also reviewing open-ended text responses to spot “synthetic” patterns and replace bad actors at no cost to you. You must ensure the analysis is based on real consumer experiences, not algorithms.