Estimate daily active demand for a Zomato-style restaurant-discovery product in one major Indian city under scale, incentive, and regulatory constraints

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 estimating the daily active demand for a Zomato-style restaurant-discovery product in one major Indian city. The focus is not just total food-delivery demand, but active user demand for discovering restaurants, evaluating options, checking trustworthy reviews/menus, and potentially converting into a table booking, takeaway, or online order.

Assume the city is large, urban, and digitally mature, with a mix of office workers, students, families, tourists, and high-frequency food app users. A meaningful segment includes diners with dietary constraints such as vegetarian, Jain, vegan, halal, gluten-free, allergy-related, calorie-conscious, or health-specific preferences, who may rely more heavily on search filters, reviews, photos, menus, and trust signals before deciding where to eat.

Your estimate should account for market scale, user behavior, restaurant supply, incentive effects such as discounts or sponsored visibility, and regulatory or trust constraints around reviews, food labeling, privacy, and restaurant information accuracy. The output should be a clear estimate of daily active demand, with the reasoning broken down into measurable components.

The experience should consider:

- The geographic scope: one major Indian city, with clear assumptions about population, internet access, smartphone usage, and addressable urban diners.

- The unit of demand: daily active restaurant-discovery usage, and whether it includes browsing, search, menu views, reviews, bookings, delivery intent, or order conversion.

- User segments and cohorts: students, office-goers, families, tourists, premium diners, and users with dietary or health constraints.

- Frequency assumptions: how often different cohorts search for restaurants, eat out, order in, compare options, or revisit the app in a day.

- Adoption and penetration: awareness of Zomato-like products, installed base, monthly active users, daily active users, and category-specific usage.

- Conversion layers: discovery-to-restaurant-page views, page views-to-bookings, page views-to-orders, and cases where users discover online but transact offline.

- Sensitivity factors: weekends vs weekdays, festivals, salary cycles, weather, office density, discounts, ad ranking, restaurant availability, and delivery versus dine-in behavior.

- Sanity checks: comparison against restaurant capacity, delivery order volume, app engagement benchmarks, and realistic limits on daily food-related decisions per person.

The goal is to produce a structured, defensible guesstimate that shows how you define the market, narrow the scope, make assumptions, calculate demand, and test whether the final number is plausible for a Zomato-style restaurant-discovery product operating at city scale.