Zomato Business Analyst Interview Questions (2026)

1. 1Coding Screen45 minMedium

   Medium DSA problems focused on arrays, strings, and hashmaps with working code expected quickly.

2. 2Technical Round 250 minMedium

   A harder DSA problem plus practical engineering discussion drawn from your projects and real Zomato features.

3. 3Design / Product-Thinking Round55 minHard

   Design a Zomato or Blinkit feature end to end (e.g. live order tracking or dark-store picker flow), balancing tech design with product judgment.

4. 4Hiring Manager / Culture Round45 minMedium

   Blunt conversation on hunger, ownership, why Zomato, and how you handle chaos and hard feedback.

5. 5HR Round25 minEasy

   Compensation, ESOPs, notice period, and setting expectations on pace and in-office work.

1. Q1

   Design an A/B test for a new Food Delivery ranking or recommendation change. Define hypothesis, primary metric, guardrails, randomization unit, and launch decision rule

   MediumStatistics & Experimentation RoundA/B TestingZomato-specific

   Context: Context: Zomato wants to improve discovery and restaurant monetization without hurting customer experience.

   How to answer: A strong answer will define a clear hypothesis, such as 'The new ranking algorithm will increase user engagement and order conversion.' The primary metric should directly reflect this, like 'Orders per user' or 'Conversion rate from search/recommendation to order.' Guardrail metrics are crucial, including 'Average delivery time,' 'Cancellation rate,' and 'Customer support contacts' to ensure no negative impact on user experience or operations. The randomization unit should be the 'User ID' to maintain consistency across sessions, and the launch decision rule should involve statistical significance (e.g., p < 0.05) on the primary metric, sustained over a defined period, with no significant negative impact on guardrails.

2. Q2

   For Dining, should randomization happen at customer, session, device, restaurant, or city level? Explain the tradeoffs

   MediumStatistics & Experimentation RoundA/B TestingZomato-specific

   Context: Consider cross-device behavior, interference, marketplace effects, and operational feasibility.

   How to answer: Randomization for Zomato Dining A/B tests should primarily happen at the customer level to ensure independent observations and minimize bias, especially for features impacting individual user behavior. Session or device level randomization can be considered for very short-term, immediate interaction changes, but risks user contamination across sessions/devices. Restaurant level randomization is appropriate when the feature directly impacts the restaurant's operations or offerings, but requires careful consideration of network effects and potential for user exposure to both variants. City level randomization is generally too broad for Dining features unless the experiment involves a fundamental change in market strategy or regulatory compliance, and significantly reduces statistical power.

3. Q3

   Design a geo or city-level experiment for Zomato Gold. When is this better than user-level randomization, and what are the analytical downsides?

   MediumStatistics & Experimentation RoundA/B TestingZomato-specific

   Context: Use matched markets, pre-period balancing, spillover checks, and fewer experimental units.

   How to answer: A strong candidate would propose a geo-level experiment by selecting a set of comparable cities or micro-markets (e.g., neighborhoods within a large city) for treatment and control groups, ensuring pre-experiment metrics are similar. They would define key metrics like Zomato Gold subscriptions, order frequency, average order value, and retention. Geo-level randomization is superior when there are significant network effects, spillover effects between users, or when the feature itself requires market-wide adoption (e.g., merchant-side changes). Analytical downsides include lower statistical power due to fewer experimental units, increased susceptibility to confounding variables unique to specific geos, and potential for selection bias if geo-assignment isn't truly random or comparable.

4. Q4

   The Restaurant Ads experiment lifts menu-to-order conversion overall, but only for new users and only in one cuisine. How would you evaluate heterogeneous treatment effects?

   HardStatistics & Experimentation RoundA/B TestingZomato-specific

   Context: Balance pre-planned segments with exploratory slicing and multiple testing risk.

   How to answer: A strong candidate would first identify the need to segment the data by user type (new vs. existing) and cuisine type to isolate the observed effects. They would then propose statistical methods like interaction terms in regression models or subgroup analysis with appropriate multiple testing corrections to formally test for heterogeneous treatment effects. The evaluation would involve comparing the lift in menu-to-order conversion within the specific new user/cuisine segment against other segments and the overall average. Finally, they would discuss the business implications of these findings, such as targeted ad campaigns or further investigation into the 'why' behind the heterogeneity.

5. Q5

   Treatment improves menu-to-order conversion but worsens order cancellation rate for Hyperpure. Walk through a launch recommendation

   HardStatistics & Experimentation RoundA/B TestingZomato-specific

   Context: Make a decision under conflicting metrics and quantify tradeoffs for stakeholders.

   How to answer: A strong recommendation would involve quantifying the net impact of the treatment by calculating the monetary value of the improved conversion versus the cost of increased cancellations, specifically for Hyperpure orders. This requires understanding the average order value, gross margin, and the cost associated with a cancelled Hyperpure order (e.g., logistics, wasted food, customer service). If the net monetary gain is positive and significant, a phased rollout or targeted launch to specific user segments or geographies might be recommended, alongside further investigation into the root causes of the increased cancellations. If the net impact is negative or marginal, the treatment should be iterated upon or discarded, focusing on mitigating the cancellation increase.

6. Q6

   How would you design ramp-up, holdback, and post-launch monitoring for a successful Reviews A/B test?

   HardStatistics & Experimentation RoundA/B TestingZomato-specific

   Context: Include ramp stages, persistent holdback, alert thresholds, rollback criteria, and owner accountability.

   How to answer: For ramp-up, I would start with a small percentage (e.g., 1-5%) of traffic, primarily in a single, less critical market, closely monitoring key operational metrics like error rates and latency to ensure stability before gradually increasing exposure. Holdback would involve reserving a small, representative segment of the eligible user base (e.g., 2-5%) from the experiment for a longer duration to assess long-term novelty effects and potential cannibalization against the original experience. Post-launch monitoring would establish a dashboard tracking primary metrics (review submission rate, quality), guardrail metrics (app crashes, uninstalls), and business impact metrics (restaurant engagement, order volume) for at least 2-4 weeks, comparing the launched experience against the holdback group and historical trends.

7. Q7

   Dining is preparing for a high-traffic event or sale. What metrics and analyses would you use to prevent a business-critical failure?

   HardProduct Analytics & Business CaseBusiness CasesZomato-specific

   Context: Think capacity, inventory, payments, support load, latency, and real-time alerting.

   How to answer: To prevent business-critical failure during a high-traffic event, I would focus on three key areas: capacity planning, real-time monitoring, and post-event analysis. For capacity, I'd analyze historical data (peak traffic, conversion rates) and projected event uplift to forecast required server capacity, database load, and staffing for customer support and restaurant operations. Real-time monitoring would involve tracking key performance indicators (KPIs) like server response times, error rates, order placement success rates, payment gateway latency, and restaurant order acceptance rates, with predefined thresholds and alerts. Post-event, I'd conduct a root cause analysis for any issues encountered, comparing actual performance against forecasts to refine future planning and identify system bottlenecks.

8. Q8

   A new Restaurant Ads feature has 30% adoption but no movement in menu-to-order conversion. What analyses would you run before calling it unsuccessful?

   EasyProduct Analytics & Business CaseProduct AnalyticsZomato-specific

   Context: Consider exposure, eligibility, frequency, quality of adoption, and segment fit.

   How to answer: First, analyze the adoption quality by segmenting users (e.g., restaurant type, cuisine, location, ad spend) and examining their engagement with the feature. Second, investigate the 'no movement' in menu-to-order conversion by looking at leading indicators like click-through rates on ads, time spent on menu pages from ads, and cart abandonment rates post-ad click. Third, consider the time horizon and potential lag effects, as well as external factors or concurrent experiments that might be masking the feature's impact. Finally, assess the feature's primary goal – was it solely conversion, or also discovery, brand visibility, or new restaurant acquisition?

9. Q9

   Reviews has rising churn or inactivity among high-value customers. How would you quantify the problem and identify drivers?

   MediumProduct Analytics & Business CaseProduct AnalyticsZomato-specific

   Context: Include cohort trends, leading indicators, competitor/substitution signals, and service quality.

   How to answer: To quantify the problem, I would define 'high-value customer' (e.g., top X% by order value/frequency) and 'churn/inactivity' for reviews (e.g., no review activity for Y days after Z recent reviews). I'd then track the percentage of these high-value customers who churn from review activity over time, comparing it to previous periods or a control group. To identify drivers, I would segment these churned high-value customers by recent product changes, app version, device, cuisine type, order type, or even specific restaurant interactions. I would also analyze their last review activity, any support tickets, and conduct qualitative surveys or interviews with a sample to understand their motivations.

10. Q10

    Evaluate search or discovery quality for Dining. Which metrics would tell you whether users are finding what they want?

    MediumProduct Analytics & Business CaseProduct AnalyticsZomato-specific

    Context: Include query refinement, zero results, click depth, conversion, and long-term satisfaction.

    How to answer: A strong answer would define search/discovery quality for Zomato Dining as the user's ability to efficiently find relevant restaurants that meet their criteria and ultimately lead to a booking or visit. Key metrics include search-to-booking conversion rate, click-through rate (CTR) on search results, and the number of refinements or re-searches. Additionally, qualitative feedback and A/B testing on different ranking algorithms or filter options are crucial. The ideal candidate would also consider the long-term impact on user retention and engagement.

11. Q11

    Monetization increased for Restaurant Ads, but menu-to-order conversion and satisfaction declined. How would you frame the tradeoff?

    MediumProduct Analytics & Business CaseProduct AnalyticsZomato-specific

    Context: Quantify incremental revenue, user harm, partner impact, and reversibility.

    How to answer: A strong candidate would frame this as a classic short-term revenue gain versus long-term user experience and retention tradeoff. They would discuss how increased ad monetization, while positive for immediate revenue, likely led to a degraded user experience (e.g., more intrusive ads, less relevant restaurant recommendations), causing a drop in menu-to-order conversion and overall satisfaction. The key is to identify the need for a balanced approach, optimizing ad load and targeting to maximize revenue without significantly compromising core user metrics, potentially through A/B testing different ad placements or frequency caps. Ultimately, the tradeoff needs to be evaluated by quantifying the long-term impact of reduced conversion and satisfaction on user lifetime value against the short-term ad revenue gains.

12. Q12

    Create an operational scorecard for Hyperpure using order cancellation rate. Which leading and lagging indicators would you include?

    MediumProduct Analytics & Business CaseProduct AnalyticsZomato-specific

    Context: Make it useful for daily operations, not just monthly reporting.

    How to answer: A strong answer would define Hyperpure's operational scorecard for order cancellation rate, clearly stating its purpose (e.g., monitor efficiency, identify issues, improve supplier reliability). It would then propose a set of leading indicators, such as 'Order Acceptance Rate by Supplier,' 'Inventory Stock-out Rate,' and 'Delivery Partner Availability,' explaining how each predicts future cancellations. For lagging indicators, 'Overall Order Cancellation Rate,' 'Cancellation Rate by Reason (e.g., item unavailable, late delivery),' and 'Cancellation Rate by Supplier' would be included, detailing how they measure past performance. The candidate should also briefly mention data sources and potential actions based on scorecard insights.

13. Q13

    Estimate customer lifetime value for Reviews. What inputs would you use, and how would you handle early-tenure customers?

    MediumProduct Analytics & Business CaseProduct AnalyticsZomato-specific

    Context: Discuss gross margin, retention, frequency, discounting, cohort maturity, and uncertainty.

    How to answer: To estimate CLV for Reviews, I would define 'customer' as a user who has submitted at least one review. Key inputs would include average number of reviews per user, average revenue generated per review (e.g., through increased engagement, ad impressions, or direct monetization if applicable), and customer retention rate for reviewers. I would also consider the cost of acquiring and serving a reviewer. For early-tenure customers, I would use a cohort-based approach, observing their review submission patterns and retention over their initial months, and then extrapolate using a survival model or by applying a discount factor to account for their higher churn probability.

14. Q14

    Using search_events(user_id, event_name, event_ts, session_id) and orders(user_id, restaurant_impression_ts, menu_view_ts, order_placed_ts), write a SQL funnel from restaurant_impression to menu_view to order_placed for Dining. Return step counts and conversion rates by cuisine

    EasySQL Technical ScreenSQLZomato-specific

    Context: Hypothetical Zomato analytics case for Dining; schemas may be simplified for an interview. Assume all timestamps are UTC and the recruiter wants the query readable enough to review live.

    How to answer: The candidate should first identify that this funnel requires joining the `orders` table with itself or using conditional aggregation to track the sequence of events. They need to filter for 'Dining' related orders, which is implied by the `restaurant_impression_ts` and `menu_view_ts` columns. The core logic involves counting users at each step (impression, menu view, order placed) and then calculating conversion rates between consecutive steps. Grouping by cuisine will require an additional join to a `restaurants` table (or assuming cuisine is available in `orders` for simplicity if not explicitly provided).

15. Q15

    Given orders(order_id, user_id, created_at, status, gross_amount) and customers(user_id, signup_date), write SQL to build weekly first-delivered order cohorts and calculate retention in weeks 1, 2, 4, and 8 for Restaurant Ads

    EasySQL Technical ScreenSQLZomato-specific

    Context: Hypothetical Zomato analytics case for Restaurant Ads; schemas may be simplified for an interview. Use first successful transaction as the cohort date and exclude cancelled or test records.

    How to answer: The candidate should first identify 'first-delivered order' for each user by filtering `orders` for `status = 'delivered'` and ordering by `created_at` to find the minimum `created_at`. Then, group these first orders into weekly cohorts based on their `created_at` (e.g., using `DATE_TRUNC('week', created_at)`). For retention, they need to count how many users from each cohort placed subsequent delivered orders in weeks 1, 2, 4, and 8 relative to their cohort week. This requires calculating the week difference between the subsequent order and the first order's week, then aggregating counts for each specified week interval.

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Practise DSA and system design for scale; prepare product-thinking; expect a strong culture-fit round

Indicative Business Analyst pay in India: ~₹7–26 LPA (role-level range, not a Zomato-specific figure).

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