India Consumer Research Methodology

For its Report series in India, Mintel uses both face-to-face and online research methodologies (only for reports for which offline samples are not suitable) to interview consumers representative of four broad regions of India, covering metros as well as cities in tiers 1 to 3, ages ranging from 18 to 65 and socio-economic classes A to C.

  • Our face to face surveys are conducted in 7 local languages (Hindi, Gujarati, Marathi, Bengali, Oriya, Telugu and Tamil). Respondents are also given the option to take the survey in English if they wish to do so.
  • Mintel applies interlocking quotas on age by gender, region by socio-economic class and city tier by region. The sample is skewed towards the higher socioeconomic classes, A and B.
  • Our sample data can be considered as indicative of urban consumers rather than representative of India as a whole. While it does give a proxy of the nation’s attitudes it has been designed to provide comparable, robust results for a wide range of demographic segments.
  • The majority of the research is conducted using a face to face methodology. However, for more online focused subject matters, we tend to employ an online methodology research.

Our Research Partners – Ipsos Observer India

Ipsos Observer is part of Ipsos India and is specialized in providing research support services across India and globally. Ipsos India has one of the best infrastructure and operations support with more than 650 employees locally.  Ipsos India runs all projects in accordance with the standards laid out in ISO 20252:2012, ensuring a consistent quality of work to the highest standards in the industry. Ipsos' processes are annually audited by external accredited quality assessors.   

Sampling and Quality control:

A CAPI (Computer assisted personal interview) methodology is carried out using internet enabled Andriod Tablets. The scripting of the survey is done on Ipsos India's i-field platform.  In order to achieve the required quotas for age, gender and socio economic group, interviews were conducted door-to-door across 16 selected cities in 6 local languages.  


Ipsos India applies the following 4 layers of quality checks:



% Of Activity On The Total Sample

Interviews Accompanied with Interviewer

2% - 5%

Telephonic Check


Interview Audio file assessment


Automated GPS location of interviewing place


Sample size

Mintel runs consumer research in cities spanning 3 tiers and 4 broad regions of India.  Rather than mirror exactly India’s population distribution, sample sizes have been selected to provide comparable figures between regions, tiers and age groups.  Specific quotas for the 3,000 sample are listed below:

Region City Tier Sample (N)
North Delhi Metro 300
North Lucknow Tier 1 150
North Jalandhar Tier 2 150
North Ambala Tier 3 150
  Total   750
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Region Male (N) Female (N) Total (N)
18-24 75 75 150
25-34 75 75 150
35-44 75 75 150
45-54 75 75 150
55-64 75 75 150
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Further Analysis

Mintel employs numerous quantitative data analysis techniques to enhance the value of our consumer research. The techniques used vary from one report to another.  Below describes some of the more commonly used techniques.


Repertoire Analysis

This is used to create consumer groups based on reported behaviour or attitudes. Consumer responses of the same value (or list of values) across a list of survey items are tallied into a single variable. The repertoire variable summarizes the number of occurrences in which the value or values appear among a list of survey items.  For example, a repertoire of brand purchasing might produce groups of those that purchase 1-2 brands, 3-4 brands and 5 or more brands. Each subgroup should be large enough (ie N=75+) to analyse.


Cluster Analysis

This technique assigns a set of individual people in to groups called clusters on the basis of one or more question responses, so that respondents within the same cluster are in some sense closer or more similar to one another than to respondents that were grouped into a different cluster.


Correspondence Analysis

This is a statistical visualization method for picturing the associations between rows (image, attitudes) and columns (brands, products, segments, etc.) of a two-way contingency table. It allows us to display brand images (and/or consumer attitudes towards brands) related to each brand covered in this survey in a joint space that is easy to understand. The significance of the relationship between a brand and its associated image is measured using the Chi-square test. If two brands have similar response patterns regarding their perceived images, they are assigned similar scores on underlying dimensions and will then be displayed close to each other in the perceptual map.


CHAID analysis

CHAID (Chi-squared Automatic Interaction Detection), a type of decision tree analysis, is used to highlight key target groups in a sample by identifying which sub-groups are more likely to show a particular characteristic. This analysis subdivides the sample into a series of subgroups that share similar characteristics towards a specific response variable and allows us to identify which combinations have the highest response rates for the target variable. It is commonly used to understand and visualise the relationship between a variable of interest such as “interest in trying a new product” and other characteristics of the sample, such as demographic composition.


Key Driver Analysis

Key driver analysis can be a useful tool in helping to prioritise focus between different factors which may impact key performance indicators (eg satisfaction, likelihood to switch providers, likelihood to recommend a brand, etc). Using correlations analysis or regression analysis we can get an understanding of which factors or attributes of a market have the strongest association or “link” with a positive performance on key performance indicators (KPIs). Hence, we are able to identify which factors or attributes are relatively more critical in a market category compared to others and ensures that often limited resources can be allocated to focusing on the main market drivers.


TURF Analysis

TURF (Total Unduplicated Reach & Frequency) analysis identifies the mix of features, attributes, or messages that will attract the largest number of unique respondents.  It is typically used when the number of features or attributes must be or should be limited, but the goal is still to reach the widest possible audience. By identifying the Total Unduplicated Reach, it is possible to maximize the number of people who find one or more of their preferred features or attributes in the product line. The resulting output from TURF is additive, with each additional feature increasing total reach. The chart is read from left to right, with each arrow indicating the incremental change in total reach when adding a new feature. The final bar represents the maximum reach of the total population when all shown features are offered.