Survey Audience Targeting in India: City, Age, NCCS and Quotas, Explained

Our platform fields surveys to our panel, which is in India only. These are the fields the audience system enforces before a respondent enters the survey.

Age band, gender, city and tier, and NCCS come from the member's panel profile. You can cut results by them without asking, which keeps the questionnaire short. Read more in our panel methodology.

Language is a design choice you make alongside targeting. Surveys run in 8+ Indian languages. See the multilingual survey guide.

NCCS is India's New Consumer Classification System, a way to group households by affluence. A 2015 trade-press explainer says it was co-developed by the Market Research Society of India (MRSI) and the Media Research Users Council. It says NCCS classifies households on education of the chief wage earner and the number of consumer durables owned from a set list (afaqs, 2015).

A later report describes NCCS as "based on ownership of consumer durables and vehicles" (Research Live, February 2024). That report adds that MRSI introduced a newer system, ISEC, to redefine the current classification. A second report says NCCS has become "less discriminatory and more volatile" (Daily Research News, February 2024).

What this means for you: NCCS is the class label our panel profile carries, with classes A1 to C1. Treat it as a rough affluence screen, not a precise income measure. ISEC is a newer system, so check with your client which one their own segments use.

Why not rupee income bands? Household income is hard to report honestly, and it is not a field our targeting supports. Ask what the income band was meant to find. Usually it was a proxy for affluence, and NCCS plus city tier covers that.

A quota fixes how many completes you want from a group. There are two ways to set them.

• Simple quotas are set on each field alone. For example: 50% female, 35% aged 24-35, 40% Tier 1. Each total is met, but the combinations are left to chance.
• Interlocking quotas set a target for each combination of fields, for example Tier 2 females aged 35-45. Each combination becomes its own cell with its own target (Cint, interlocked quotas concept).

Cint's documentation warns that without interlocking, a survey might fill large quotas with the easiest-to-find respondents. The same page lists the cost: limited feasibility, a higher cost per interview for scarce segments, and longer field time.

Our platform supports interlocking quotas across gender, age and city cells, and settles the cell counts so that splits come out exact. Practical rule: interlock only the combinations your analysis will report on. Use simple quotas for everything else.

Illustrative numbers only, to show the method. Suppose the brief is a packaged-snack concept test among adults in Tier 1, 2 and 3 cities, 400 completes, with a 50/35/15 tier split and an even gender split inside each tier.

Now add age. Say you want 20% aged 18-24, 35% aged 24-35, 25% aged 35-45, 15% aged 45-55 and 5% aged 56-90. On 400 completes that is 80, 140, 100, 60 and 20.

If you interlock all three fields, you get 6 tier-gender cells times 5 age bands, which is 30 cells. The smallest, a Tier 3 woman or man aged 56-90, would need 5% of 30, which is 1.5 completes. A cell that small is hard to fill and gives you nothing you can analyse.

Fix: interlock tier and gender (6 cells, smallest 30). Set age as a simple quota on the whole sample. If you must report age by tier, merge bands first, for example 18-35, 35-55 and 56-90. Always check the eligible count for the narrowest cell before you launch.

The panel includes general consumers and professionals such as doctors and carpenters, so a study can be pointed at a specific audience. We do not publish counts per profession. Ask for the eligible number before you commission the work. Studies on doctors or patients also need consent and safety-reporting handling. Our pharma research guide covers that.

Incidence is the share of people in your targeted group who actually qualify for the survey. A narrow behavioural screen on top of a narrow age band can leave very few eligible members.

Before you fix the sample size, do four things.

• Write the audience as one sentence, then list every field it needs. Delete any field that does not change a decision.
• Ask for the eligible count for the whole audience and for the smallest cell.
• Put the broadest screen in the targeting and the narrow one inside the survey, asked neutrally among decoy options.
• Plan the smallest group you will report. Merge cells that fall below the minimum you need for a stable read.

For more on how samples are built and where they bias, read sampling methods in India and Tier 2 and Tier 3 consumer research.

If your job is a consumer survey in India with a defined audience, this is what the 400-response grid above costs at list price.

Pro is sold in bundles of 400 to 1,000 responses and costs 20% less per response than Plus. Enterprise plans come with a dedicated research team and are priced on request.

The SuperJ network has a 25M+ consumer footprint. The survey-eligible panel is a subset, and availability depends on your targeting. Members complete Aadhaar-based KYC once, and each study receives a zero-knowledge proof of verification. Read the details on /panel-methodology and pricing.

The limit: SuperJ is app-based, so it reaches smartphone users only. Offline households need face-to-face fieldwork.