Real People, Shallow Answers: The Low-Intent Problem

Market research has traditionally focused on identifying and excluding bots and automated fraud. However, an equally significant challenge has emerged in recent years: low-intent human respondents. These are real people who technically qualify for studies but engage with minimal attention, motivation, or effort.

As digital panels scale and incentive-based participation increases, low-intent behaviour has become a structural issue rather than an exception , one that directly affects data reliability and decision confidence.

Low-intent respondents are not fraudulent in the traditional sense. They complete surveys legitimately but display behaviours such as rushing through questions, providing shallow open-ended responses, or optimising for speed rather than accuracy. Because these respondents pass basic quality checks, their impact is often subtle and harder to detect.

This makes low-intent behaviour particularly challenging: the data appears clean, but insight depth and signal strength are weakened.

Several industry trends contribute to this shift. Survey frequency has increased, average incentives have tightened, and many respondents participate in multiple studies across platforms. Over time, this encourages task-oriented completion rather than thoughtful engagement.

Research literature on survey fatigue and satisficing shows that even well-intentioned participants may reduce cognitive effort when perceived burden outweighs perceived value. This is especially relevant in longer or highly repetitive studies.

Standard quality measures , speed checks, attention traps, and straight-lining flags , are effective at removing extreme cases but less effective at identifying disengagement that remains within acceptable thresholds. Low-intent respondents often adapt to known checks, making static rule-based approaches insufficient on their own.

As a result, data loss frequently occurs not through exclusion, but through diminished insight quality.

Recent advances in behavioural analytics and AI-assisted monitoring offer more nuanced ways to assess engagement. Metrics such as time-on-question variance, response consistency, open-ended richness, and interaction patterns provide a deeper view of respondent intent across the survey journey.

These approaches do not replace human judgement but help surface patterns that warrant closer review.

At TrustSample, low-intent participation is treated as a quality risk distinct from fraud. Our approach focuses on early detection and prevention , aligning study design, incentives, and respondent expectations , rather than relying solely on post-field exclusions.

By combining AI-supported behavioural indicators with human review, we aim to protect insight depth while maintaining representative samples and efficient fieldwork.