Demographic Survey Questions: Age, Income, Gender and Region

A standard demographic block copied from a template usually asks for eight fields and produces four usable cuts. Before you write any question, list the cuts you will report: city tier, age band, gender, income class, category usage. Then design backwards.

If you cannot name the table a field feeds, drop the field. This one habit shortens most demographic blocks by half.

A birth year is precise, personal and usually unnecessary. You will report bands anyway, and a band is faster to answer on a phone. Use the same bands across waves and studies so you can compare them.

Two rules: keep bands contiguous, and keep the lowest band honest about consent. If your panel starts at 18, do not offer an under 18 option. If you need a narrower cut later, ask a follow-up only of the group that qualifies.

Household income is hard to answer accurately anywhere. In India it is harder for three reasons: income arrives monthly, quarterly or seasonally; it is often shared across a household; and a large part of spending is not a simple function of income.

Exact rupee bands also age badly. A band that made sense at ₹25,000 a month reads differently after two years of inflation. This is why Indian research usually uses NCCS, which combines occupation and education of the chief earner with durable goods, instead of income alone. Our NCCS guide explains the grades and the questions behind them.

If you still need income, ask for a band and label it clearly: "monthly household income before tax". Offer a "prefer not to say" option and expect a meaningful share to use it. Never make income a required field on a consumer survey.

Gender. Offer male, female and an option not to say. Non-binary options are appropriate where the study population can use them, and the analysis should state how small cells are handled. If gender is a quota field, keep the screening question separate from the reporting question.

Education. Ask about the highest level completed, not the institution. Keep four or five levels so they map to NCCS inputs and stay usable.

Occupation. For consumers, ask the chief earner's occupation when you are computing NCCS; ask the respondent's own occupation when the study is about work. For professionals, ask for the role and the sector separately.

Region. Ask for the state, and let the city come from the panel profile or a city question. Region is not one field: if you need South India, name the states and cities you mean.

Put a demographic question first only when it screens someone out, such as an age gate or a category user filter. Everything else goes at the end, after the questions that carry the study's value.

There is a second reason for the end position: identity questions can prime later answers. Asking about income before a price question changes the frame. Asking at the end keeps the price answer closer to the product experience.

Keep the block on one screen if it fits, or two short screens. A progress indicator helps. If a field is optional, label it optional rather than leaving it blank.

Six fields, each feeding a named cut. That is usually enough for a consumer study. Add occupation or education only when NCCS or a professional audience is part of the analysis.

Religion, caste and sexuality are not needed for most commercial studies and should not be collected by default. If a study genuinely requires one of them, treat it as sensitive: state why in the introduction, keep it optional, and report only aggregates above your anonymity threshold.

For any demographic field, store the answer without identifiers and check the group sizes before publishing. A cut built on fewer than your stated minimum is not a finding. The survey bias guide covers the errors that appear when a demographic cut is over-read.

Audience targeting covers city, age band, gender, NCCS and behavioural segments, so the panel profile can fill some demographic fields without asking. Add the rest as questions at the end of the survey. The free plan includes 30 responses for a pilot, and pricing is per response: ₹150 on Plus from 100 responses, ₹120 on Pro in bundles of 400 to 1,000.

Every question returns its base, so a demographic cell that is too small to report is visible in the output. For how respondents are verified and what the panel figures mean, see panel methodology.