| 1 |
Find customers who made at least one purchase in the last 30 days but none in the 31–90 day window. |
Amazon, Swiggy, Flipkart |
Medium |
Practical |
Most frequent |
| 2 |
List orders with missing/invalid addresses (NULL or empty) in the last 7 days. |
Flipkart, Uber Eats, Zomato |
Easy |
Practical |
Most frequent |
| 3 |
Identify users who signed up >1 year ago but have zero activity in the last 6 months. |
Microsoft, Ola, Paytm |
Medium |
Practical |
Most frequent |
| 4 |
Return products where price changed more than twice in the last quarter, showing timestamps and old/new prices. |
Amazon, Walmart, Myntra |
Medium |
Practical |
Normal |
| 5 |
For each day last month, show total orders vs canceled orders and cancellation rate. |
Swiggy, Zomato, Uber Eats |
Easy |
Practical |
Most frequent |
| 6 |
Find users who placed orders only via mobile app and never via website. |
Google, Flipkart, Amazon |
Medium |
Practical |
Normal |
| 7 |
Top 10 customers by revenue in each city for the last 60 days. |
Amazon, BigBasket, Urban Company |
Medium |
Practical |
Most frequent |
| 8 |
Retrieve employees whose salary is above their department’s average. |
Accenture, TCS, Infosys |
Medium |
Practical |
Most frequent |
| 9 |
List users who purchased the same product more than once in the same week. |
Walmart, Amazon, Grofers |
Medium |
Practical |
Normal |
| 10 |
Find products not sold in the last 6 months but still marked active. |
Flipkart, Myntra, Amazon |
Medium |
Practical |
Most frequent |
| 11 |
Retrieve orders where delivery time exceeded 7 days from order date. |
Amazon, FedEx, Delhivery |
Easy |
Practical |
Most frequent |
| 12 |
Find customers who placed first order in last 30 days and ordered again within 7 days (early repeat rate). |
Swiggy, Uber Eats, Zomato |
Medium |
Practical |
Normal |
| 13 |
List sellers who listed more than 100 unique SKUs in the last quarter. |
Amazon, Flipkart, eBay |
Medium |
Practical |
Normal |
| 14 |
Identify users with orders but no valid email or phone (NULL or malformed). |
Ola, Paytm, PhonePe |
Medium |
Practical |
Normal |
| 15 |
Top 3 categories by sales volume in each region last month. |
Amazon, Walmart, BigBasket |
Medium |
Practical |
Most frequent |
| 16 |
Compare weekend orders vs weekday orders for the last 3 months. |
Swiggy, Zomato, Uber |
Easy |
Practical |
Normal |
| 17 |
Customers with at least 3 consecutive months of purchases (retention pattern). |
Netflix, Amazon Prime, Hotstar |
Hard |
Practical |
Normal |
| 18 |
Find products purchased only once across entire history (one-off items). |
Amazon, Flipkart, Myntra |
Medium |
Practical |
Most frequent |
| 19 |
Identify duplicate customer records (same email/phone with different customer_id). |
TCS, Cognizant, Infosys |
Medium |
Practical |
Most frequent |
| 20 |
Customers who spent > ₹50,000 in last 90 days but nothing in last 30 days. |
Amazon, Flipkart, Myntra |
Hard |
Practical |
Normal |
| 21 |
Weekly cohort analysis: users who joined in Week N and made purchase in Weeks N+1, N+2. |
Amazon, Swiggy, Paytm |
Hard |
Practical |
Most frequent |
| 22 |
Calculate average order value (AOV) per marketing channel last month. |
Amazon, Ola, Zomato |
Medium |
Practical |
Most frequent |
| 23 |
Identify spike days where orders were > 2x moving average of past 7 days. |
Flipkart, BigBasket, Amazon |
Medium |
Practical |
Normal |
| 24 |
List customers with negative lifetime value (refunds > purchases). |
Amazon, Myntra, Zomato |
Medium |
Practical |
Normal |
| 25 |
Find items frequently returned: items with return rate > 10% in last 90 days. |
Amazon, Myntra, Walmart |
Medium |
Practical |
Most frequent |
| 26 |
For each product, calculate the day-of-week distribution of sales. |
Amazon, Flipkart, BigBasket |
Medium |
Practical |
Normal |
| 27 |
Identify users who churned after a price increase (orders before vs after price change). |
Netflix, Amazon Prime, Disney+ |
Hard |
Practical |
Medium |
| 28 |
Compute customer lifetime value estimate (sum of spend over last 12 months). |
Amazon, Flipkart, Walmart |
Medium |
Practical |
Most frequent |
| 29 |
Find users who used coupons in >50% of their orders. |
Swiggy, Zomato, Flipkart |
Medium |
Practical |
Normal |
| 30 |
Show top referrers by number of new signups attributable to them. |
Paytm, PhonePe, Ola |
Medium |
Practical |
Normal |
| 31 |
Products with sudden drop in conversion rate (views→purchases) month-over-month. |
Amazon, Myntra, Flipkart |
Hard |
Practical |
Normal |
| 32 |
Identify customers who upgraded subscription tier in last 6 months. |
Netflix, Spotify, Amazon Prime |
Easy |
Practical |
Normal |
| 33 |
Compare retention between users who received onboarding email vs those who didn’t. |
Google, Microsoft, Amazon |
Hard |
Practical |
Medium |
| 34 |
For each marketing campaign, compute cost per acquisition (CPA). |
Amazon, Ola, Flipkart |
Medium |
Practical |
Most frequent |
| 35 |
Flag orders where billing_amount ≠ sum(line_item_amounts). |
Amazon, Walmart, Myntra |
Medium |
Practical |
Most frequent |
| 36 |
List products that have never had a review but have >100 sales. |
Flipkart, Amazon, Myntra |
Medium |
Practical |
Normal |
| 37 |
For each city, compute average delivery time and 90th percentile delivery time. |
Swiggy, Zomato, Amazon |
Hard |
Practical |
Most frequent |
| 38 |
Identify customers with multiple failed payments in 30 days. |
Paytm, PhonePe, Razorpay |
Medium |
Practical |
Most frequent |
| 39 |
Find hourly traffic peaks for last two weeks (count events by hour). |
Google, Microsoft, Amazon |
Easy |
Practical |
Most frequent |
| 40 |
Identify orders shipped but not delivered and older than expected SLA. |
Amazon, FedEx, Delhivery |
Medium |
Practical |
Most frequent |
| 41 |
Compute month-over-month growth in active users by country. |
Netflix, Amazon, Google |
Medium |
Practical |
Most frequent |
| 42 |
Detect suspicious accounts with identical IP and shipping address but different emails. |
Amazon, Paytm, Ola |
Hard |
Practical |
Normal |
| 43 |
List products where stock_count dropped to zero and stayed zero for >14 days. |
Flipkart, BigBasket, Walmart |
Medium |
Practical |
Normal |
| 44 |
Calculate average time between first app open and first purchase per cohort. |
Swiggy, Zomato, Uber Eats |
Hard |
Practical |
Medium |
| 45 |
Identify top 5 SKUs responsible for 80% of returns (Pareto). |
Amazon, Myntra, Walmart |
Medium |
Practical |
Normal |
| 46 |
For users with subscription cancellations, show average days since last login. |
Netflix, Spotify, Hotstar |
Medium |
Practical |
Normal |
| 47 |
Compute funnel drop-off rates: view → add-to-cart → checkout → purchase. |
Amazon, Flipkart, Myntra |
Hard |
Practical |
Most frequent |
| 48 |
Find products with > 50% of orders containing discounts above 30%. |
Flipkart, Myntra, Amazon |
Medium |
Practical |
Normal |
| 49 |
Identify top 10 most viewed but least purchased products. |
Amazon, Flipkart, Myntra |
Medium |
Practical |
Normal |
| 50 |
Compute repeat purchase rate for customers acquired in last 6 months. |
Amazon, Swiggy, Zomato |
Medium |
Practical |
Most frequent |
| 51 |
For each seller, compute average time to ship after order placed. |
Amazon, Flipkart, eBay |
Medium |
Practical |
Most frequent |
| 52 |
Find customers who used both App and Website in last 90 days. |
Google, Amazon, Flipkart |
Medium |
Practical |
Normal |
| 53 |
Identify SKUs with inventory mismatch between warehouse table and vendor reported count. |
Amazon, Walmart, BigBasket |
Hard |
Practical |
Medium |
| 54 |
For subscription users, compute monthly churn rate and retention cohort. |
Netflix, Amazon Prime, Spotify |
Hard |
Practical |
Most frequent |
| 55 |
List users who cancelled within 24 hours of sign-up (trial churn). |
Netflix, Hotstar, Prime |
Medium |
Practical |
Normal |
| 56 |
Find products where average rating < 3 and sales > 1000 in last 6 months. |
Amazon, Myntra, Flipkart |
Medium |
Practical |
Normal |
| 57 |
Identify customers who moved to a competitor (inferred by no orders and competitor purchases via partner data). |
Amazon, Flipkart, Swiggy |
Hard |
Practical |
Rare |
| 58 |
Compute gross margin per product (price − cost) aggregated by category. |
Flipkart, Amazon, Walmart |
Medium |
Practical |
Most frequent |
| 59 |
For each promo code, compute lift in conversion compared to control group. |
Swiggy, Zomato, Flipkart |
Hard |
Practical |
Medium |
| 60 |
Find peak payment failure hours and their failure rates. |
Paytm, Razorpay, PhonePe |
Medium |
Practical |
Most frequent |
| 61 |
Identify users who frequently buy discounted items only (≥80% orders with discounts). |
Amazon, Flipkart, Myntra |
Medium |
Practical |
Normal |
| 62 |
Compute time-to-first-purchase median for new users by acquisition channel. |
Amazon, Swiggy, Zomato |
Medium |
Practical |
Most frequent |
| 63 |
List users with addresses in multiple cities (potential fraud). |
Ola, Amazon, Flipkart |
Hard |
Practical |
Normal |
| 64 |
For returns, compute proportion due to size/fit vs quality vs shipping. |
Myntra, Amazon, Flipkart |
Hard |
Practical |
Medium |
| 65 |
Calculate average session duration per device type (mobile/desktop). |
Google, Flipkart, Amazon |
Medium |
Practical |
Normal |
| 66 |
Identify products where cost_price > selling_price (data errors). |
Amazon, Walmart, Flipkart |
Easy |
Practical |
Most frequent |
| 67 |
For each SKU, compute days-since-last-sale and flag stale SKUs (>90 days). |
Amazon, Flipkart, BigBasket |
Easy |
Practical |
Most frequent |
| 68 |
Find customers who reactivated after a pause of >180 days and their average spend. |
Netflix, Amazon, Spotify |
Medium |
Practical |
Normal |
| 69 |
Compute average order processing time (order_created → order_shipped). |
Amazon, Flipkart, Walmart |
Medium |
Practical |
Most frequent |
| 70 |
Identify products frequently bundled together (co-purchase patterns). |
Amazon, Flipkart, Myntra |
Hard |
Practical |
Normal |
| 71 |
For each store/restaurant, compute 7-day rolling average of orders. |
Swiggy, Zomato, Uber Eats |
Medium |
Practical |
Most frequent |
| 72 |
Find users who used two different payment methods in same week. |
Paytm, PhonePe, Razorpay |
Medium |
Practical |
Normal |
| 73 |
Identify fastest-growing cities by percentage growth in orders quarter-over-quarter. |
Amazon, Flipkart, BigBasket |
Medium |
Practical |
Most frequent |
| 74 |
Compute percentage of revenue from top 1% customers (contribution concentration). |
Amazon, Walmart, Flipkart |
Hard |
Practical |
Normal |
| 75 |
For cancelled subscriptions, analyze top reasons (categorize and count). |
Netflix, Spotify, Prime |
Medium |
Practical |
Most frequent |
| 76 |
Identify orders with mismatched currency conversion or exchange rate issues. |
Amazon, PayPal, Expedia |
Hard |
Practical |
Medium |
| 77 |
Find products with high cart abandonment: high add-to-cart but low purchase. |
Amazon, Flipkart, Myntra |
Medium |
Practical |
Most frequent |
| 78 |
Compute improvement in key metric after a UI change (A/B) — use pre/post windows. |
Google, Facebook, Amazon |
Hard |
Practical |
Medium |
| 79 |
List customers who used referral code and converted to paying customers within 30 days. |
Paytm, PhonePe, Ola |
Medium |
Practical |
Normal |
| 80 |
Identify anomalies in daily revenue using simple IQR rule (outlier detection). |
Amazon, Flipkart, Swiggy |
Medium |
Practical |
Normal |
| 81 |
For each product category, compute seasonality index month-over-month. |
Amazon, Walmart, BigBasket |
Hard |
Practical |
Medium |
| 82 |
Identify users with inconsistent gender/age info across different data sources. |
Google, Facebook, Amazon |
Medium |
Practical |
Normal |
| 83 |
Find average number of items per order and trend over 6 months. |
Flipkart, Amazon, Myntra |
Easy |
Practical |
Most frequent |
| 84 |
Compute time from first visit to first purchase grouped by acquisition campaign. |
Amazon, Swiggy, Flipkart |
Hard |
Practical |
Medium |
| 85 |
List top N reasons for customer support tickets in last 30 days (categorized). |
Amazon, Flipkart, Zomato |
Medium |
Practical |
Most frequent |
| 86 |
For each SKU, compute sell-through rate: sold / available_stock over time. |
Walmart, BigBasket, Amazon |
Medium |
Practical |
Normal |
| 87 |
Find customers who increased their basket size by >50% after receiving a targeted promo. |
Flipkart, Amazon, Myntra |
Hard |
Practical |
Medium |
| 88 |
Identify orders missing tax or with incorrect tax calculation. |
Amazon, Walmart, Flipkart |
Medium |
Practical |
Normal |
| 89 |
Compute lifetime transactions distribution and identify Pareto (top 20% customers by txn count). |
Amazon, Walmart, Flipkart |
Medium |
Practical |
Most frequent |
| 90 |
For each delivery partner, compute on-time delivery percentage and average delay. |
Swiggy, Zomato, Amazon Logistics |
Medium |
Practical |
Most frequent |
| 91 |
Identify SKU cannibalization: new SKU launch causing sales drop in similar SKUs. |
Amazon, Flipkart, Myntra |
Hard |
Practical |
Medium |
| 92 |
Find customers who interact with support more than X times and their churn probability. |
Amazon, Netflix, Flipkart |
Hard |
Practical |
Medium |
| 93 |
Aggregate daily active users (DAU), weekly active users (WAU), monthly active users (MAU). |
Google, Microsoft, Amazon |
Medium |
Practical |
Most frequent |
| 94 |
Detect and list suspiciously high discounts applied by specific sellers (possible fraud). |
Amazon, Flipkart, eBay |
Hard |
Practical |
Normal |
| 95 |
Compute percent repeat buyers within 30, 60, 90 day windows for each cohort. |
Amazon, Swiggy, Zomato |
Hard |
Practical |
Most frequent |
| 96 |
For subscription trials, compute conversion rate to paid within 14 days. |
Netflix, Spotify, Amazon Prime |
Medium |
Practical |
Most frequent |
| 97 |
Identify records with inconsistent timestamps (created_at > updated_at). |
Any, Data Engineering |
Easy |
Practical |
Most frequent |
| 98 |
For multi-currency orders, compute revenue normalized to base currency and sum by country. |
Amazon, Expedia, Booking.com |
Hard |
Practical |
Normal |
| 99 |
Find top search queries that returned zero results and count affected users. |
Google, Amazon, Flipkart |
Medium |
Practical |
Normal |