Ola Product Analyst Interview: Rounds, Questions and Preparation

What the job involves

Product Analysts at Indian unicorns and high-growth startups like Ola work at the intersection of data, product, and business. They write SQL queries, build dashboards, run experiments, and analyse user behaviour to help product managers make decisions that improve metrics like retention, conversion, and supply-demand balance. The role is fast-paced, requires strong problem-solving, and often involves presenting insights to cross-functional teams.

Role and Technical Knowledge

1. 1

   What are the key metrics you would track for a ride-hailing app like Ola to measure rider retention?

   A strong answer covers DAU/MAU, weekly retention cohorts, repeat ride rate, and time between rides. Mention segmenting by city, user type, and ride frequency.

2. 2

   Write a SQL query to find the top 5 cities by number of completed rides in the last 30 days.

   Use JOINs between rides and cities tables, filter by status = 'completed' and date range, GROUP BY city, ORDER BY COUNT DESC, LIMIT 5. Explain each clause clearly.

3. 3

   How would you measure the success of a new feature that shows estimated wait time before booking a ride?

   Define primary metric (e.g., booking conversion rate) and secondary metrics (cancellation rate, time to book). Suggest an A/B test with control and treatment groups, and mention guardrail metrics like rider satisfaction.

4. 4

   What is the difference between a funnel analysis and a cohort analysis? Give an example for a food delivery app.

   Funnel shows step-by-step drop-off (e.g., app open to order placed). Cohort groups users by signup week and tracks retention over time. Use both to identify where users drop off and how retention changes.

Practical Scenarios and Case Studies

1. 5

   Ola notices a 10% drop in daily rides in a particular city. How would you investigate the root cause?

   Break down by supply (driver availability), demand (user sessions), and external factors (weather, strikes). Check data for driver login hours, rider app opens, and completion rates. Suggest quick checks like comparing with previous weeks and other cities.

2. 6

   You are asked to design an A/B test to increase the number of users who apply a promo code. What steps would you take?

   Define hypothesis, choose primary metric (promo code application rate), randomise users, decide sample size and duration, and set guardrails (e.g., revenue per user). Mention pitfalls like novelty effect and network effects in ride-hailing.

3. 7

   A product manager wants to launch a new subscription plan for frequent riders. How would you estimate the potential impact on revenue?

   Segment users by ride frequency, estimate adoption rate using survey or past data, calculate incremental revenue from subscription fees minus discounts. Use a guesstimate structure: total users, target segment, conversion rate, average fee.

4. 8

   You have data showing that users who take their first ride within 24 hours of signup are 3x more likely to return. How would you use this insight?

   Suggest interventions like onboarding incentives, push notifications, or first-ride discounts to drive early first ride. Propose measuring the impact via a controlled experiment and tracking long-term retention.

Behavioural and HR Questions

1. 9

   Tell me about a time you used data to solve a problem in a college project or internship.

   Use STAR format: describe the situation, the data you analysed, the method, and the outcome. Highlight how your analysis led to a decision or improvement.

2. 10

   Why do you want to work as a Product Analyst at a fast-growing startup like Ola?

   Show passion for data-driven decision-making, mention the scale and impact of ride-hailing, and connect your skills to the company's mission. Avoid generic answers about 'learning and growth'.

3. 11

   Describe a time you had to explain a complex analysis to a non-technical person.

   Focus on simplifying without losing accuracy, using visuals or analogies, and checking for understanding. Emphasise the outcome: the person made a better decision.

4. 12

   How do you handle tight deadlines when you have multiple data requests?

   Talk about prioritisation based on business impact, communicating trade-offs, and using tools to automate repetitive tasks. Give a concrete example from college or work.

Guesstimates and Business Understanding

1. 13

   Estimate the number of daily rides in a city like Bangalore for a ride-hailing app.

   Break down by population, smartphone penetration, app adoption, average rides per user per week, and active days. State assumptions clearly and sanity-check the final number.

2. 14

   If Ola wants to increase its market share in tier-2 cities, what data would you look at first?

   Consider supply (driver sign-ups, active drivers), demand (app downloads, ride requests), competition, and pricing. Suggest a dashboard with key metrics and a plan to test incentives.

3. 15

   What is one metric you would not want to optimise alone for a ride-hailing app, and why?

   Example: ride completion rate could be gamed by forcing rides, harming user experience. Discuss trade-offs with rider satisfaction, driver earnings, and long-term retention.

4. 16

   How would you decide whether to invest more in driver acquisition or rider acquisition?

   Analyse current supply-demand gaps, marginal cost of acquiring each side, and network effects. Use data on wait times, cancellation rates, and rider retention to prioritise.