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As research teams adopt AI-powered platforms, automated analysis, and mixed-method workflows, the language of insight generation is evolving fast.
This glossary brings together the essential terms every modern researcher needs: from established concepts like brand tracking to AI-native methods like AI-moderated interviews, automated coding, and real-time quality controls.
Use it as your up-to-date reference for understanding how market research is changing and how AI is transforming the way insights are collected, analysed, and activated.
AI-Moderated Interviews (AIMI)
Interviews conducted by an AI moderator that interact with respondents in real timeask dynamic follow-upsand automatically flag data-quality issues (e.g. fraudinattentive respondents). AIMIs enable large-scale, voice-based (or otherwise automated) data collection with depth and speed.
AI-Powered Platform
A software platform that uses artificial intelligence to automate research workflows: from building the research project, to running interviews, analysing responses and generating reports. On Glaut, this means the platform can conduct AI-moderated interviews (voice or text), automatically code open-ended responses, apply quality controls (e.g. fraud detection), and produce ready-to-share insight reports, enabling fast, scalable, and rigorous market research.
Access Panels
Pre-recruited groups of individuals who agree to participate in research studies are often used for surveys or quick-turn polling. Panels are designed to represent a broader population across key demographics or behaviors.
Accompanied Shopping (Shop-along Interviews)
A qualitative technique where a researcher accompanies participants during actual shopping, observing behavior, decision drivers, emotional triggers, and context in real-time.
Ad-Hoc Research
One-off custom research conducted to answer a specific business or marketing question rather than a recurring or continuous tracking study.
Aided Awareness
A measure of brand or product recognition when respondents are prompted, e.g. shown a logonameor visual cue and asked whether they recognise it.
Alternative Hypothesis
In statistical testing the hypothesis that assumes there is an effect or difference, used as a counterpart to the null hypothesis when testing data significance.
Ambiguous Questions
Survey or research questions that are unclear, vague, or poorly worded , leading to potential misinterpretation or unreliable responses.
Animatics
A sequence of rough images (hand-drawn or digital) used to simulate how a final video or animation will look; often used in creative testing or pre-testing phases.
Area Sampling (Geographical Sampling)
Sampling method that selects respondents based on predefined geographic areas, e.g., neighborhoods or regions, then draws from those areas to build a sample.
Artificial Intelligence and Market Research
The use of AI technologies (e.g., for automation, analysis, natural language interviewing, segmentation) to support or replace traditional research tasks, from data collection to insight generation. Examples of AI tools are Glaut, Conveo.ai, Outset.ai, Listen Labs, and Yasna.
Attitudinal Research
Research focused on what customers think, believe, or feel about a product, brand, service, or experience , as opposed to what they do.
Audience Profiling
The process of building detailed profiles of target customers’ demographics, psychographics, behaviors, and needs to better tailor marketing communication and product strategies.
Audits
Systematic independent examinations of data processes or performances to ensure validity, compliance, or quality, often used in research to check methodology or data integrity.
Behavioural Segmentation
Segmenting customers based on actual behaviors, usage patterns, purchase history, loyalty, rather than only demographics or attitudes.
Research focused on determining how many people recognize a brand and what they associate with it.
Brand Equity
The value of a brand in consumers’ minds is based on perception, loyalty, and reputation, rather than just the company’s financial value.
Brand Health Tracking
Ongoing measurement of brand metrics (awareness, consideration, loyalty, sentiment) over time to monitor brand performance and identify shifts.
Brand Positioning Research
Research to understand how customers perceive the brand relative to competitors, what space the brand occupies in customers’ minds, and its unique value proposition.
Repeated (e.g. quarterly, semi-annual) studies using consistent measures to monitor brand performance over time.
Category Entry Point Research
Studies identifying the triggers, circumstances, or channels through which customers first enter a product category, before they start thinking about specific brands.
Cognitive Biases
Systematic mental shortcuts or distortions that influence how people perceive, remember, or respond, which may lead to biased or non-rational decision-making or survey responses.
Comparative Analysis
Evaluation of your performance (brand-product metrics) relative to competitors or industry benchmarks to understand relative strengths, weaknesses, and opportunities.
Competitor Benchmarking
Systematic comparison of key metrics (price, quality, features, satisfaction, perception) between your brand/product and competitors.
Concept Testing
Pre-launch testing of a product idea or concept to assess appeal, positioning, messaging, or viability before full development.
Confusion Matrix
A tool (often used in predictive modeling) that displays correct versus incorrect predictions, employed when assessing classification models or predictive tools.
Conjoint Analysis
A method to understand how customers jointly value different attributes, useful for identifying feature trade-offs and optimal product configurations.
Cross-Tabs (Crosstabs)
A statistical method that analyzes survey data across two or more variables simultaneously, used to discover relationships (e.g., age × purchase intent, gender × brand perception). Automatically generated with AI-powered software like Glaut.
Analysis of the full journey a customer has with a brand , all touchpoints from awareness to purchase to support , to understand satisfactionfrictionand loyalty over time.
Customer Journey Mapping
Visual mapping of customers’ interactions, feelings, pain points, decisions, and behaviors across all stages of their relationship with a brand.
Customer Satisfaction (CSAT)
A metric measuring how satisfied a customer is with a product, service, or interaction, often using numeric scales after purchase or support.
Consumer Behaviour
The study of how individuals or groups make purchasing decisions, including motivations, influences, usage influences, and decision-making processes.
Creative Testing
Pre-testing marketing creatives (ads, messaging, visuals) with real or test audiences to optimize content, avoid missteps, and validate effectiveness before rollout.
Data Triangulation
Using multiple data sources, methods, or perspectives to validate findings, increasing robustness and credibility, and reducing method-specific bias.
Data Quality Control
Built-in or process-based safeguards to ensure that collected data is valid, non-fraudulent, and reliable. This is especially important when using large-scale automated or AI-based tools, as it helps maintain the integrity of insights. (Note: these are not traditional MR terms but are highly relevant for AI-native platforms like Glaut.)
Desk Research (Secondary Research)
Gathering and analyzing existing data from external sources, reports, databases, public statistics, academic studies, before or alongside primary research. Researchers use AI tools like Perplexity and the "Deep research" mode on ChatGPT and Gemini.
Discrete Choice Modelling
A statistical modeling method that predicts which option customers will select when given multiple choices, used to infer preferences, elasticity, or forecast decisions.
Ethnography in Market Research
Qualitative method involving observation or immersion in real-world environments (home, store, workplace) to understand behavior in context.
Eye Tracking
Biometric method measuring where, how long, and in what sequence a person looks, often used to test ad packaging, web pages, or product design.
Focus Group Moderator
A trained facilitator who guides a small group discussion (e.g., 6-10 participants) ensures balanced participation, probes deeply, and elicits rich qualitative data.
Fraud Prevention
Built-in or process-based safeguards to ensure that collected data is valid, non-fraudulent, and reliable. Especially relevant when using large-scale automated or AI-based tools, helping to maintain the integrity of insights. (Note: these are not classical MR terms but are strongly relevant for AI-native platforms like Glaut.)
Gamification in Market Research
Applying game-like mechanics (points, challenges, badges, progress bars) to research tasks to increase engagement, reduce fatigue, and improve data quality.
Halo Effect
A cognitive bias where a positive impression in one area (e.g., design) affects opinions about unrelated areas (e.g., functionality), skewing responses or perceptions.
Heuristic Evaluation
Usability inspection method where experts evaluate a digital product (app/website) against known heuristics to find design flaws or friction points, often used instead of full user testing when quick results are needed.
Implicit Association Testing (IAT)
A method for measuring implicit or unconscious attitudes by evaluating the speed and strength of associations between words, images, or concepts in respondents’ minds.
In-Depth Interviews (IDIs)
One-on-one open-ended interviews (often 30–90 minutes) enable deep exploration of motivations, emotions, decision-making processes, and context that quantitative data can’t reveal.
Incidence Rate
The proportion of a sampled population that qualifies to participate in a study, used to plan screening efforts and estimate the number of invitations needed.
Survey Response Bias
Systematic distortions in respondents’ answers caused by question wording, order, social desirability, and misunderstanding can skew data validity.
Key Driver Analysis
Statistical technique to identify which factors (drivers) most strongly influence an outcome, such as satisfaction, loyalty, or purchase intent, helping prioritize focus areas.
Likert Scales
Common survey scale format for measuring opinions or attitudes involves respondents indicating agreement/disagreement or frequency/intensity on a fixed scale (e.g., 1–5, 1–7).
Longitudinal Studies
Studies that follow the same sample (or panel) over time are useful for tracking change; trends, customer journeys, or long-term effects.
Market Basket Analysis
Technique analyzing which products are commonly purchased together, useful for bundling, cross-sell, promotion strategies, and understanding purchasing patterns.
Market Segmentation
Dividing a broad market into subgroups of customers with shared characteristics (behavioral, demographic, psychographic) to enable targeted strategies.
Market Penetration
Measure of how much of the total addressable market a brand or product currently serves , and an indicator of growth potential.
MaxDiff Analysis
Also called maximum difference scaling, it is a method where respondents repeatedly choose the most and least important items from a list, revealing relative preferences without bias.
MedianMean Mode
Basic statistical measures of central tendency are used to summarize quantitative data, helping interpret survey or numeric results.
Mixed-Methods Research
A mixed-methodology is an approach that combines qualitative depth with quantitative scale and speed in the same study. Researchers can gather the same quantitative numerical insights with a qualitative layer of insights to provide a fuller picture than standalone methods.
Mobile Ethnography
A form of ethnographic research where participants record real behavior, experiences, or environments over time (e.g., via mobile diaries, photos, videos), allowing for in-context insights.
A popular loyalty metric, respondents rate how likely they are to recommend a product/brand (0-10 scale). Based on their score they are grouped into Promoters (9-10), Passives (7-8), or Detractors (0-6).
Omnibus Research (Omnibus Surveys)
A cost-efficient survey model where different clients share a single survey wave and insert their own questions benefiting from shared sample and lower costs.
Online Survey Tools
Software or platforms enabling digital questionnaire creation, distribution, collection, and analysis, which are fundamental for quantitative research and many mixed-method designs.
The process of collecting unscripted, free-text or spoken feedback (open-ended responses), then using AI-powered analysis engine to automatically code and categorise verbatim, detect recurring themes or sub-themes, and surface emotional tone, motivations, and insights. This turns raw, unstructured feedback into structured output (themes, codes, sentiment, named entities) ready for dashboards, export or reporting.
Primary Research
Original data collection directly from sources (respondents) via methods such as surveys, interviews, observations, or experiments, to address specific research questions.
Research focused on understanding motivations, feelings, attitudes, and context, via methods such as interviews, focus groups, and observation, generating non-numeric data.
Research focused on the numerical measurement of behaviors, attitudes, or outcomes through structured surveys, experiments, or metrics, generating data suitable for statistical analysis.
Research Fieldwork
The execution phase of primary research, whether in person or remote, includes interviews, observations, focus groups, survey administration, etc.
Sample Size / Sample Size Calculator
Determining how many respondents you need to gather reliable, representative data is often calculated based on population size, incidence rate, desired confidence, and margin of error.
Statistical Significance
In inferential statistics, a result is statistically significant when the probability that it occurred by chance is below a preset threshold (e.g., p ≤ 0.05), providing confidence that observed effects are real.
This glossary aims to provide researchers with a clear, shared vocabulary for the methods, tools, and analytical approaches that shape modern research practice. As data collection and analysis develop, having precise language helps teams work more efficiently, compare methodologies more effectively, and select the best mix of techniques for each project.
If there are new terms you believe should be included, feel free to email us at marketing@glaut.com. We’ll review your suggestion and add it to keep this glossary as accurate and useful as possible.
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