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Consumer Intelligence

Usage & Attitude Study

Usage & Attitude Study

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Definition:

A usage and attitude study is a foundational consumer intelligence method designed to document how target audiences interact with a product, service, or category, alongside the beliefs, perceptions, and motivations that shape that behavior. These studies typically explore purchase frequency, consumption habits, brand perceptions, unmet needs, and competitive context, giving insights teams a structured baseline for segmentation, positioning, and innovation decisions. Within consumer intelligence programs, usage and attitude studies are often run periodically to track how behavior and sentiment shift over time, making them essential for brand health monitoring and category strategy. When conducted qualitatively, they surface the reasoning and emotion behind behavior that survey data alone cannot explain.

How Conveo Does It

Conveo runs usage and attitude studies through AI-moderated video interviews with real participants, not synthetic respondents or AI avatars. Teams can launch a study in under 30 minutes using an AI-drafted interview guide built from their research brief, then run hundreds of conversations in parallel across markets and languages. Multimodal analysis captures speech, tone, and facial cues alongside verbal responses, surfacing the emotional context and behavioral nuance that makes usage and attitude findings credible and decision-ready for enterprise stakeholders, typically within days.

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Frequently asked questions.

A usage and attitude study is a research method that documents how consumers currently use a product or category and what attitudes, beliefs, and motivations shape that behavior. It captures both the what and the why, covering purchase habits, frequency of use, brand perceptions, and unmet needs. The output gives brand and insights teams a structured, evidence-based picture of the consumer landscape they are operating in.
Usage and attitude studies establish the behavioral and attitudinal baseline that makes all other research more meaningful. Without knowing how consumers actually use a category and what drives their choices, teams risk building strategy on assumptions rather than evidence. These studies are particularly valuable for segmentation, positioning work, and innovation pipelines, where understanding the gap between current behavior and unmet needs is the starting point for any credible strategic recommendation.
A usage and attitude study is typically a deep, periodic diagnostic that maps consumer behavior and motivation across a category at a specific point in time. Brand tracking, by contrast, monitors a defined set of brand health metrics continuously over time to detect shifts in awareness, consideration, and preference. Usage and attitude studies provide the foundational context; brand tracking measures movement against it. Many enterprise research programs use both in combination to connect behavioral insight with longitudinal performance data.
AI is making usage and attitude studies faster to design, easier to scale, and richer in the depth of insight they produce. AI-moderated interviewing removes the scheduling constraints that traditionally limited sample sizes in qualitative usage and attitude work, allowing hundreds of conversations to run in parallel across markets. Automated analysis of speech, tone, and facial expression surfaces emotional and behavioral signals that manual coding often misses, giving researchers a more complete picture of how and why consumers behave the way they do.
Enterprise teams typically use usage and attitude studies at the start of a brand or category strategy cycle to establish a consumer baseline before making positioning, innovation, or communication decisions. They are also used to re-examine a category after a significant market shift, such as a new competitor entry or a change in consumer behavior. Insights teams often run them across multiple markets simultaneously to identify regional differences in usage patterns and attitudes that affect how global strategies need to be adapted locally.

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