ElasticRun Product Analyst Interview: Rounds, Questions and Preparation

What the job involves

A Product Analyst at Indian unicorns and high-growth startups like ElasticRun works closely with product, engineering, and business teams to define metrics, analyse user behaviour, and run experiments that improve product adoption and revenue. Day to day, you will write SQL queries, build dashboards, interpret A/B test results, and translate data into actionable product recommendations for fast-paced logistics, fintech, or e-commerce platforms.

Role and product knowledge

1. 1

   What are the key metrics you would track for a last-mile delivery product like ElasticRun?

   A strong answer covers delivery time, cost per delivery, success rate, and repeat usage, linking each to business impact.

2. 2

   How does a product analyst differ from a data analyst or business analyst in a startup?

   Focus on product analyst owning product decisions and experiments, while data analysts focus on reporting and business analysts on process.

3. 3

   Explain the concept of a north star metric and give an example for a B2B logistics platform.

   Define north star as the single metric capturing core value, then suggest 'number of successful deliveries per active retailer' for B2B logistics.

4. 4

   What is a funnel analysis, and how would you use it to improve a kirana store onboarding flow?

   Describe steps from sign-up to first order, identify drop-off points, and suggest fixes like OTP simplification or field agent support.

Technical and analytical skills

1. 5

   Write a SQL query to find the top 5 cities by average delivery time last month.

   Use GROUP BY city, AVG(delivery_time), WHERE date filter, ORDER BY average DESC, LIMIT 5, and mention handling nulls.

2. 6

   How do you calculate statistical significance in an A/B test for a new checkout flow?

   Explain p-value, confidence level, sample size, and that you need enough users before trusting results.

3. 7

   What is cohort analysis, and how would you use it to measure retention of delivery partners?

   Group partners by joining month, track activity over weeks, and spot if later cohorts retain better or worse.

4. 8

   Given a dataset with missing delivery timestamps, how would you clean and use it?

   Discuss imputation, flagging missing rows, checking patterns, and deciding whether to exclude or estimate based on trip distance.

Practical scenarios and case studies

1. 9

   Daily active retailers on your app dropped 15 percent week over week. How would you investigate?

   Break down by region, device, acquisition channel, and recent app updates; check seasonality and competitor actions before concluding.

2. 10

   You have to prioritise between fixing a slow checkout page and adding a new product filter. How do you decide?

   Use impact vs effort, tie to north star metric, and suggest running a quick experiment or user survey to quantify impact.

3. 11

   Design a dashboard for the operations head to monitor delivery partner performance daily.

   Include metrics like on-time rate, average trips per day, idle time, and ratings, with filters by city and partner cohort.

4. 12

   A product manager asks you to prove that a new feature increased orders. What steps do you take?

   Check if it was an A/B test, compare treatment and control, control for seasonality, and report confidence intervals.

Behavioural and HR questions

1. 13

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

   Use STAR format, highlight the dataset, your analysis method, and the measurable outcome or decision it influenced.

2. 14

   Why do you want to work as a product analyst at a high-growth startup like ElasticRun?

   Show understanding of the startup's sector, mention learning speed and ownership, and connect your skills to their product challenges.

3. 15

   Describe a situation where you had to explain a technical finding to a non-technical person.

   Emphasise simplicity, using visuals or analogies, and checking understanding; mention the positive outcome.

4. 16

   How do you handle tight deadlines when multiple stakeholders ask for reports at the same time?

   Talk about prioritising by business impact, communicating trade-offs early, and automating repetitive reports where possible.