THE A-Z OF MR TERMS

Why a Glossary Matters

The world of market research (MR) is full of acronyms, frameworks, and technical terms. For brands, marketers, and budding researchers in India, understanding these terms is key to making informed decisions.

Whether you’re reviewing reports, briefing a research agency, or analyzing insights, a common language ensures clarity, efficiency, and better strategic outcomes.

This glossary will give you a quick reference to the most commonly used MR terms, helping you navigate reports, dashboards, and discussions confidently.

A–Z Market Research Terms Guide

A

• A&U (Attitude & Usage): Measures how consumers feel about a product and how they use it.
  
  

• Ad Tracking: Monitors advertising impact, recall, and engagement over time.
  
  

• Analytics Dashboard: Interactive platform showing metrics and trends in real-time.
  
  

• Attribute Testing: Evaluating consumer response to product features (taste, color, packaging).
  
  

Example (India): FMCG brands like Britannia track ad recall across urban/rural segments after campaigns.

B

• Brand Health: Evaluates awareness, favorability, loyalty, and purchase intent.
  
  

• Benchmarking: Comparing a brand against competitors or historical performance.
  
  

• Behavioral Segmentation: Dividing consumers based on purchase patterns, frequency, or loyalty.
  
  

• Blind Test: Consumer testing without brand identifiers to avoid bias.
  
  

C

• CSAT (Customer Satisfaction Score): Measures satisfaction with products or services.
  
  

• Conjoint Analysis: Determines which combination of product features drives preference.
  
  

• Cross-Tabulation: Analyzing relationships between two or more survey variables.
  
  

• Customer Journey Mapping: Visual representation of all touchpoints a consumer experiences.
  
  

Example (India): E-commerce brands like Nykaa map purchase journeys across app, social, and offline experiences.

D

• Demographics: Age, gender, income, education, occupation.
  
  

• Data Cleaning: Correcting or removing inconsistent or invalid responses.
  
  

• Decision Trees: Modeling consumer choices to predict behavior.
  
  

• Discriminant Analysis: Statistical method to distinguish between consumer groups.
  
  

E

• Ethnography: Observing consumers in their natural environment for rich insights.
  
  

• Exit Interviews: Understanding why a customer leaves a service or subscription.
  
  

• Engagement Metrics: Measures interactions with digital or offline campaigns.
  
  

F

• Focus Group: Small group discussions to explore attitudes, motivations, and perceptions.
  
  

• Forecasting: Predicting future trends, sales, or demand.
  
  

• Factor Analysis: Reducing large sets of variables to key underlying factors.
  
  

G

• Gap Analysis: Identifies gaps between current and desired market performance.
  
  

• Geo-Segmentation: Segmenting based on region, city tier, or climate.
  
  

• Guesstimates: Initial market sizing based on assumptions before primary research.
  
  

H

• Hypothesis Testing: Statistical method to test assumptions.
  
  

• Heat Maps: Visual representation of concentration or intensity of data points.
  
  

• Hybrid Research: Combining qualitative and quantitative methods for deeper insights.
  
  

I

• Insight: Actionable interpretation explaining why behavior occurs.
  
  

• Incidence Rate: Proportion of respondents who qualify for the survey.
  
  

• In-Depth Interview (IDI): One-on-one interviews to explore motivations and perceptions.
  
  

J

• Journey Mapping: Charting the full consumer experience across touchpoints.
  
  

• Job-to-be-Done (JTBD): Understanding the consumer problem your product/service solves.
  
  

• Judgment Sampling: Non-random selection based on researcher expertise.
  
  

K

• KPI (Key Performance Indicator): Metrics used to track success against goals.
  
  

• Knowledge Panel: A summary of insights collected from multiple sources.
  
  

• Key Driver Analysis (KDA): Identifies which attributes drive satisfaction or loyalty.
  
  

L

• Laddering: Technique exploring underlying motivations behind choices.
  
  

• Longitudinal Study: Research over time to track behavioral or attitudinal changes.
  
  

• Likert Scale: Rating scale from strongly disagree to strongly agree, used in surveys.
  
  

M

• Market Segmentation: Dividing consumers into homogenous groups.
  
  

• MaxDiff Scaling: Prioritizing attributes or features by relative importance.
  
  

• Mystery Shopping: Evaluating service delivery secretly to ensure quality.
  
  

N

• NPS (Net Promoter Score): Measures loyalty based on likelihood to recommend.
  
  

• Neural Network Analysis: AI technique to find patterns in large datasets.
  
  

• Net Reach: Percentage of target audience reached by a campaign.
  
  

O

• Open-Ended Questions: Respondents provide free-form answers for qualitative insights.
  
  

• Omnichannel Research: Studying consumer behavior across all channels and devices.
  
  

• Observational Study: Collecting data by watching actual behavior without interference.
  
  

P

• Persona: Fictional, representative customer profile including motivations, behaviors, and pain points.
  
  

• Psychographics: Consumer lifestyles, values, beliefs, and personality traits.
  
  

• Predictive Analytics: Using historical data to forecast future consumer behavior.
  
  

• Pulse Survey: Short, frequent surveys to monitor trends or sentiment in real time.
  
  

Q

• Qualitative Research: Exploratory research to uncover the “why” behind behavior.
  
  

• Quota Sampling: Ensuring demographic or behavioral representation in surveys.
  
  

• Questionnaire Design: Crafting surveys to collect unbiased and actionable data.
  
  

R

• Research Design: Overall strategy for answering research questions.
  
  

• Rating Scales: Scales to quantify attitudes, satisfaction, or agreement.
  
  

• Return on Insights (ROI): Evaluating business impact of insights generated.
  
  

S

• Sample Size: Number of respondents included in a study.
  
  

• Segmentation: Dividing a market into smaller groups with shared traits.
  
  

• SOM / SAM / TAM: Market sizing frameworks: Serviceable Obtainable, Serviceable Available, Total Addressable Market.
  
  

• Sentiment Analysis: Using AI or manual coding to classify opinions as positive, neutral, or negative.
  
  

T

• Tracking Study: Continuous monitoring of metrics over time.
  
  

• Top Box / Bottom Box: Measures of extreme positive or negative survey responses.
  
  

• Test Market: Small-scale launch to evaluate consumer response before a full rollout.
  
  

U

• Usability Testing: Evaluating product or digital interfaces for ease of use.
  
  

• U&A Study (Usage & Attitude): Explores usage patterns and consumer attitudes.
  
  

• Unstructured Data: Data that is not organized, like open-ended survey responses or social media comments.
  
  

V

• Validation: Ensuring data accuracy, reliability, and relevance.
  
  

• Voice of Customer (VoC): Captures customer expectations, preferences, and feedback.
  
  

• Variance Analysis: Examining the difference between expected and actual outcomes.
  
  

W

• Weighting: Adjusting survey data for over- or under-represented groups.
  
  

• Win/Loss Analysis: Evaluates why customers chose or rejected a brand/product.
  
  

• Wave Analysis: Comparing survey responses across multiple waves of research.
  
  

X

• Experience Mapping: Visualizing customer experience across touchpoints and journeys.
  
  

• Exploratory Research: Research to discover patterns, insights, or hypotheses before formal testing.
  
  

Y

• Yield Analysis: Evaluating efficiency and profitability of campaigns, channels, or product launches.
  
  

• Youth Panel Research: Studies focusing on Gen Z or millennial consumers in India.
  
  

• Year-on-Year (YoY) Analysis: Comparing metrics against the previous year to identify trends.
  
  

Z

• Zero-Based Research: Starting research without assumptions, purely data-driven.
  
  

• Zoom Interviews: Online one-on-one qualitative interviews, increasingly common in India’s digital MR space.
  
  

• Z-Score Analysis: Standardizing data points to identify outliers in statistical research.

How to Use This Guide

1. Reference Tool: Keep handy when reviewing research reports or dashboards.

2. Team Alignment: Ensures all stakeholders speak a common MR language.

3. Learning Resource: Ideal for students, interns, and young professionals entering MR.

4. Decision Support: Turns complex terminology into actionable understanding.

Pro Tip: Keep a digital version of this glossary updated. Indian market practices, new methodologies, and AI tools continuously introduce new acronyms and concepts.