March 28, 2025
3 minutes
feasibility mismatches
inflated incidence rates
market research feasibility
overpromised feasibility
survey sampling issues
Feasibility mismatches are one of the most disruptive and costly issues in market research. Whether it’s inflated incidence rates, unrealistic fielding expectations, or unfulfilled quotas, these challenges can derail your project, erode client trust, and inflate costs.
At DataDiggers, we work with research agencies and brands across the globe, so we’ve seen firsthand how common—and avoidable—these problems are. This article breaks down where feasibility mismatches come from, how to anticipate them, and what you can do to avoid getting caught in the gap between promise and performance.
1. Inflated Incidence Rate Assumptions
Too often, incidence rates (IRs) are based on past projects that differ in scope, geography, or targeting criteria. Even a modest overstatement can have cascading effects: underdelivery, budget overruns, and last-minute fieldwork extensions.
2. Optimism in Delivery Timelines
In fast-turn project environments, timelines can get compressed to win the bid. But if the underlying audience doesn’t match the projected availability, you’re left scrambling—or compromising on sample quality to make up time.
3. Mismatch Between Target Audience and Panel Reality
Not all panels are built the same. Some providers say they can reach certain segments, but lack the profiling depth, diversity, or scale to back it up.
4. Lack of Pretesting and IR Validation
Skipping a feasibility test or IR check might seem like a shortcut—but it’s often a false economy. When studies fail mid-field, it’s usually because assumptions weren’t validated up front.
5. Gaps in Stakeholder Communication
Misalignment between agency, client, and sample supplier can lead to scope creep, misinterpreted targeting specs, or late-stage changes that upend feasibility.
Use Data-Driven IR Estimates
Avoid guesses. Demand IR projections based on real profiling and actual historical performance. At DataDiggers, we rely on decades of panel health tracking across 100+ markets and 1.5M+ deeply profiled respondents.
Pre-Test for Confidence
A small-scale feasibility test before launch can flag issues with targeting, survey logic, or completion rates—allowing time to adjust before you scale.
Audit Panel Strength and Transparency
Ask your provider for more than just numbers. What is the recruitment method? How are respondents verified? What are the profiling layers? Transparency is a sign of maturity—and essential for trust.
Set Expectations Realistically
Include a buffer in timelines. Over-relying on “best case” delivery windows only increases pressure—and compromises quality if things don’t go as planned.
Stay in Sync
Document your incidence assumptions, quotas, and eligibility criteria clearly—and reconfirm them throughout the project lifecycle. Feasibility isn’t static. Stay agile.
At DataDiggers, we’ve built our operations around solving feasibility challenges before they reach your project. Our approach includes:
When targeting is complex or audiences are hard to reach, we don’t stop at traditional feasibility methods. Our broader ecosystem includes advanced solutions that expand what's possible—without risking quality or integrity.
Alongside our AI-driven synthetic personas tool Syntheo and our scenario modeling engine Modeliq, we also offer Correlix.
For bias correction, data augmentation, and simulation at scale, Correlix uses advanced statistical and machine learning models to generate high-integrity synthetic data that reflects real-world patterns—without compromising privacy or quality.
These tools work in synergy to help you model what could happen, where feasibility is limited, or where early-stage inputs need support—giving you new confidence even in uncertain or constrained scenarios.
Feasibility mismatches are preventable—but only with the right practices and the right partners. When sample is misaligned with project requirements, everything downstream suffers: cost, time, quality, and ultimately, decision-making.
At DataDiggers, we don't just estimate feasibility. We validate it. We simulate it. And when traditional paths don’t suffice, we innovate our way around the obstacle.
Let’s start a conversation about making your next study not just possible—but predictable.
