Estimate daily active demand for a Zomato-style restaurant-discovery product in one major Indian city

Problem Statement Description

Product context: Zomato is an Indian food-tech and local commerce company; its products include restaurant discovery, reviews, food delivery, dining, ads, restaurant partner tools, and quick commerce through Blinkit.

You are asked to estimate the daily active demand for a Zomato-style restaurant-discovery product in one major Indian city, such as Bengaluru, Mumbai, Delhi NCR, Hyderabad, or Pune. The focus is on people who actively use the product to discover restaurants, compare options, check reviews, view menus, find dietary-fit places, make reservations, or move toward ordering.

This is a guesstimate exercise, so the interviewer is looking for how you structure the market, define the demand unit, make reasonable assumptions, and pressure-test the estimate. You should clarify whether you are estimating daily active users, daily discovery sessions, or daily high-intent demand events, and keep the scope consistent throughout.

The product context includes urban diners, food delivery users, office workers, students, families, tourists, and users with dietary constraints such as vegetarian, Jain, vegan, halal, gluten-free, or allergy-related needs. Demand may vary by weekday versus weekend, meal occasion, income level, neighborhood density, restaurant supply, trust in reviews, and availability of reliable restaurant information.

The experience should consider:

- The exact city boundary and population base you are estimating for

- The unit of demand: users, sessions, searches, restaurant page views, reservation attempts, or order-intent events

- Relevant population segments such as smartphone users, internet users, eating-out users, delivery users, and dietary-constrained diners

- Frequency assumptions across breakfast, lunch, snacks, dinner, weekends, office days, and special occasions

- Adoption assumptions for a Zomato-style app versus alternatives like Google Maps, Instagram, word of mouth, or direct restaurant apps

- Conversion intent differences between casual browsing, restaurant discovery, table booking, and food ordering

- Sensitivity checks for key assumptions such as urban smartphone penetration, dining frequency, and app usage frequency

- Sanity checks against restaurant density, meal occasions, traffic patterns, and plausible app engagement levels

Your goal is to produce a clear, defensible estimate of daily active demand, show the assumptions behind it, identify the biggest drivers of uncertainty, and explain how you would validate or refine the estimate with real product, market, or behavioral data.