Research & Recuitment Operations

CLT

CLT

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Qualitative insights at the speed of your business

Conveo automates video interviews to speed up decision-making.

Definition:

A Central Location Test (CLT) is a quantitative and qualitative research methodology conducted in a controlled, neutral venue where participants interact with products, prototypes, or stimuli under consistent conditions. CLTs are a core method in research operations because the controlled environment reduces external variables, making comparisons across participants more reliable. Common applications include sensory evaluation, packaging assessment, concept screening, and ad testing. Because all participants experience the same physical setup, CLTs produce data that is easier to standardise and benchmark across waves. The format suits categories where physical interaction with a product or stimulus is essential to generating valid responses.

How Conveo Does It

Conveo extends CLT research by pairing in-venue product interaction with AI-moderated video interviews that capture the reasoning behind participant reactions in real time. Teams can launch a study in under 30 minutes and receive analysis within days rather than waiting weeks for agency-processed fieldwork. Every session involves real participants, not synthetic respondents, with adaptive probing that follows what each person actually says, producing findings that connect the sensory or physical response to the underlying motivation at enterprise scale.

Frequently asked questions.
A Central Location Test is a research method where participants are brought to a specific venue, typically a shopping mall, research studio, or hired facility, to evaluate products or concepts under controlled conditions. The standardised environment ensures every participant experiences the same setup, which makes it easier to compare responses and draw reliable conclusions across a sample. CLTs are common in FMCG, food and beverage, packaging, and advertising research.
CLTs are chosen when physical interaction with a product or stimulus is necessary for a valid response. Tasting a food product, handling packaging, or viewing a display in realistic retail conditions cannot be replicated accurately through a screen. The controlled venue also removes the variability of home environments, which matters when teams need consistent sensory conditions or want to prevent participants from being influenced by their own context before responding.
A CLT brings participants to a controlled venue for a single, time-limited evaluation under standardised conditions. An In-Home Usage Test (IHUT) sends products to participants who use them in their own environment over days or weeks. CLTs suit rapid comparative testing where consistency matters most. IHUTs suit categories where real-world usage context, habitual behaviour, or extended exposure is what drives the insight. Many research programmes use both methods at different stages of development.
AI is reducing the gap between what participants do in a CLT and why they do it. Traditionally, CLTs captured ratings and rankings efficiently but relied on separate qualitative follow-up to explain the numbers. AI-moderated interviews now run alongside or immediately after the in-venue task, probing reactions in real time and analysing responses the moment a session closes. This means teams get the structured data and the reasoning from the same participant in the same research event, without waiting weeks for synthesis.
Enterprise teams typically use CLTs at specific decision gates, particularly when a product or concept needs to be evaluated against a competitive set before moving to development or launch. CLT findings are most useful when connected to earlier qualitative discovery and later in-market tracking. Teams that build their CLT outputs into a shared insight library can compare results across waves, track how consumer response to a product evolves, and avoid repeating foundational research that has already been done.
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