Food and Beverage Consumer Research in India: How to Reach and Screen the Right Buyers

SuperJ members are general Indian consumers, so food and beverage buyers are among them. No category or brand field is documented, so ask which behavioural segments exist and always screen.

Targeting. Set cities, gender, age and NCCS as described in how audience targeting works and the guide to surveying any audience. For food, choose cities by where the product is sold. For South India, name the southern cities you want, as set out in surveying South India.

Cells. A screener inside the survey confirms category use. Use in-survey over-quota routing to close a cell, such as 100 quick-commerce users, when it fills. Ask the team which screener answers can carry a quota.

Ask the category question before any brand question, so the brand list does not tell people what you want. Rotate brand options. Use this bank and cut it to fit.

Questions 1 to 3 define who is in the study. Question 4 gives you channel cells, which matter because quick-commerce and kirana shoppers can differ on pack size and price.

Food and beverage studies live on usage and choice, not on awareness alone. These are the measures to name in your brief.

A flavour range is a reach problem too: TURF analysis finds the set of flavours that appeals to the most people. Our product testing guide and packaging testing guide cover the test designs. For usage and attitude work, see the usage and attitude guide.

For worked category examples with real sample sizes, read the chai-time snacking study (350 snack buyers who drink tea in the afternoon or evening) and the bhujia brand study (300 buyers who had tried at least two of four brands). Both screen on category use first.

Food is a bigger share of rural spending. In the 2023-24 Household Consumption Expenditure Survey, rural households spent 47.04% of consumption on food and urban households 39.68% (PIB, 7 January 2025). The survey covered 2,61,953 households. Average monthly per capita spend was ₹4,122 rural and ₹6,996 urban. Pack sizes and price points that work in one may not transfer to the other.

The market is large and still described by estimates. IBEF says the Indian food processing market reached US$ 382.24 billion in FY25. Treat that as context, not a sampling frame. IBEF's page carries projections that do not line up with each other, so we quote only the FY25 figure.

Labels are regulated, so test real-looking packs. Under the FSSAI Labelling and Display Regulations, 2020 (Version VIII, 9 September 2025), a food package carries an FSSAI logo and licence number. It also carries a green circle-in-square mark if vegetarian or a brown triangle-in-square mark if non-vegetarian. A pack test image without these marks is a test of an unrealistic pack.

Where to look for more. Channel mix and brand share data come from retail audits and industry bodies, which we do not republish. If a figure is not linked here, we have not verified it. For city differences, see the Tier 2 and Tier 3 consumer research guide.

Illustrative study: a snack flavour and pack test among weekly packaged-snack buyers in four Tier 1 cities. You want 400 completed interviews, 100 per city.

Precision. At 95% confidence on a 50% result, 400 completes give a margin of 1.96 × √(0.25 ÷ 400) = 4.9 points, and a 100-person city cell gives 9.8 points. If you want to compare cities, read a gap under about 10 points as noise.

Incidence. Incidence is defined in the audience hub guide. The rates below are assumptions for the sums, not our panel's data.

Screened-out respondents do not count towards a response bundle, so low incidence does not change the price of a complete. It changes whether the audience can be filled and how long fieldwork takes. That is why you ask for a feasibility count before you commit.

• Feasibility for your category. "How many weekly buyers of [category] in [cities], and in what time?" Ask for a written count for your exact audience.

• Recency. "Is 30-day category use confirmed by a screener answer, or inferred from an old profile?"

• Channel cells. "Which screener answers, such as kirana versus quick-commerce, can carry a quota?"

• Brand lists. "Do you rotate brand options and add a decoy brand, so brand-user cells are not inflated?"

• Pack stimuli. "Can the survey show pack images with the FSSAI marks, and route on diet for veg or non-veg packs?"

• Kirana-only households. "How do you handle buyers who rarely use smartphone apps?"

Generic checks on verification and data quality are in verified survey respondents and survey data quality.

Kirana-only households and part of the rural food spend described above are under-represented, because the panel is smartphone users. Quick-commerce and delivery-app buyers are not under-represented in the same way. For a study that must cover kirana-only buyers, you need face-to-face fieldwork, which we do not run. Brand and category shares in your results are survey shares, not market shares: those come from retail audits. A very narrow audience, such as one brand in one city, may fill slowly or not at all. Stated switching and purchase intent are not sales.

For a flavour and pack test among smartphone-using snack buyers, the example above costs this at list price.

Pro is sold in bundles of 400 to 1,000 responses and Enterprise pricing is custom: see pricing. SuperJ has a 25M+ consumer footprint of which the survey-eligible panel is a subset, and verification and quality checks are on the panel methodology page.