Meesho Business Analyst Interview Guide

Round 1: Technical SQL & Problem SolvingThe SetupThis round began with a brief resume walk-through before the interviewer pasted a raw problem statement into a shared coding editor.The Question: "You have a table of flight/travel routes. Find the cheapest cost to travel from a starting point to an ending point, but there's a catch: the traveler cannot take a direct flight. They must make at least one halt. How would you write this?"The Interviewer Interaction & Solution EvolutionMy Initial Clarification: Before jumping into the code, I asked the interviewer to clarify the table schema. I asked if we could assume a standard routes table with origin, destination, and cost. The interviewer confirmed this and added, "Assume all costs are positive, and the routes are directed."My Stated Approach: I explained that since a direct path was barred and exactly one intermediate stop was the minimum requirement, we needed to connect two separate legs of a journey. I told them my strategy was to use a Self-Join on the routes table to map Leg A (Start to Halt) to Leg B (Halt to End).Live Coding: I wrote out the query, joining the table to itself where the destination of the first table matched the origin of the second (r1.destination = r2.origin).I wrote a SQL query: SELECT r1.origin AS start_location, r2.destination AS end_location, MIN(r1.cost + r2.cost) AS minimum_travel_cost FROM routes r1 JOIN routes r2 ON r1.destination = r2.origin WHERE r1.origin <> r2.destination GROUP BY r1.origin, r2.destination; Interviewer Follow-up & My Defense: The interviewer stopped me and asked, "What happens if your query accidentally creates a loop where someone flies from Delhi to Mumbai, and then Mumbai back to Delhi? Does your query handle that?"My Response: I pointed to the WHERE r1.origin <> r2.destination clause I had included. I explained that this explicit filter ensures that the final destination cannot be the same as the starting point, effectively killing any circular, two-leg loops. The interviewer nodded and said, "Good catch. What if there could be multiple halts?" I wrapped up the round by explaining that for $N$ halts, we'd pivot this strategy into a Recursive Common Table Expression (CTE) to dynamically track path depth. Round 2: Product Sense & Case StudyThe SetupThis round was conversational but highly unstructured. The interviewer skipped introductions and went straight into a macro product issue faced by social commerce platforms.The Question: "Let’s look at Meesho. Suppose you look at the executive dashboard and notice that the number of completed orders has been steadily dropping month-on-month. You are the analytics lead. Walk me through exactly what you do."The Interviewer Interaction & Troubleshooting WalkthroughMy Initial Clarification: I started by defining the scope. I asked, "Is this a sudden, sharp drop from last month, or a gradual decline over 3–4 months? And is it affecting both the app and the website?" The interviewer responded, "It’s a gradual, steady decline over the last quarter, across the entire platform."How I Structured the Analysis: I told the interviewer I would break the investigation down into two buckets: Internal Metrics (things we broke or changed) and External Factors (things changing in the market).The Live Back-and-Forth Discussion:Phase 1: Digging into Internal MetricsI told the interviewer I wanted to check the user conversion funnel first: Acquisition $\rightarrow$ Product Search $\rightarrow$ Cart Add $\rightarrow$ Checkout. I hypothesized that if App Downloads were steady but Orders were down, the leak was inside the app.Interviewer Pushback: "Let's say search traffic and cart adds are completely normal. Users are putting things in their carts at the exact same rate as last year. What do you check next?"My Real-Time Pivot: "If cart-adds are steady but orders are dropping, that points directly to a Checkout Funnel failure. I told them I would look at two specific things: First, technical metrics (payment gateway failure rates or UPI drop-offs). Second, operational changes—specifically, if Meesho recently altered its shipping fees, tax displays, or estimated delivery timelines at the final checkout screen, causing high cart abandonment."Phase 2: Transitioning to External FactorsThe interviewer then asked, "What if checkout data looks perfectly clean? No bugs, no fee changes. What's your next move?"My Response: I explained that if our internal machine is running smoothly, the macro environment is shifting. I stated I would investigate Seasonality (comparing this quarter against the same quarter last year to rule out a post-festive slump) and Competitive Poaching.Specific Example I Used: I brought up Shopsy and Flipkart. I explained that if competitors launched aggressive discount campaigns or zero-delivery-fee incentives during these specific months, our price-sensitive user base would naturally shift their buying behavior over to them.