The E-commerce Data Landscape
E-commerce and retail generate some of the richest datasets in business. Every click, every cart addition, every purchase creates data that companies use to optimize everything from marketing spend to warehouse operations. The volume is enormous, the relationships are complex, and the business questions are directly tied to revenue.
When you interview at an e-commerce company, from a startup to an enterprise retailer, the SQL questions will reflect real problems their data teams solve daily.
Customer Lifetime Value Analysis
Calculating customer lifetime value (CLV) is fundamental to e-commerce analytics. This metric influences marketing budgets, customer acquisition strategies, and retention programs. Interview questions in this area test your ability to aggregate customer behavior over time while accounting for various complexities.
A basic CLV question might ask you to calculate total revenue per customer. More sophisticated versions add layers: calculate CLV by acquisition cohort, compare CLV across marketing channels, or project future value based on historical patterns.
These queries require:
- Aggregation across multiple orders over time
- Joining customer, order, and potentially marketing attribution data
- Date-based cohort assignment
- Window functions for cumulative calculations
- Handling of returns, refunds, and cancellations
The interviewer wants to see that you understand CLV is not just a sum of purchases but a nuanced metric that varies by how you define the customer relationship and measurement period.
Cohort Analysis and Retention
Retention analysis examines how customer behavior changes over time from their first purchase. E-commerce companies obsess over these metrics because acquiring new customers is expensive, making repeat purchases extremely valuable.
Interview questions might ask you to calculate the percentage of customers who make a second purchase within 30, 60, or 90 days. Or to build a full cohort retention table showing monthly retention rates for each acquisition month.
These problems test your ability to:
- Define cohorts based on first purchase date
- Calculate time intervals between events
- Pivot data to create retention matrices
- Handle customers who appear in multiple time periods
- Use window functions to identify first and subsequent purchases
Basket Analysis and Product Affinity
Understanding which products customers buy together drives merchandising, recommendations, and promotions. Basket analysis questions test your ability to find patterns in transaction data.
A common interview question asks you to find product pairs that frequently appear together in orders. More advanced versions might ask you to calculate lift or confidence metrics from association rules, or to identify products that serve as gateway items leading to higher-value purchases.
These queries often require self-joins on order data to compare items within the same transaction. The challenge lies in handling the combinatorial complexity efficiently and understanding what the resulting metrics actually mean for the business.
Inventory and Supply Chain
Retail companies must balance having enough inventory to meet demand against the cost of carrying excess stock. SQL interview questions in this area test your understanding of inventory metrics and forecasting.
You might be asked to calculate days of inventory remaining based on recent sales velocity, identify products at risk of stockout, or find items with declining sell-through rates that might need markdown.
These problems combine:
- Aggregation of sales data over rolling time windows
- Joining inventory levels with sales history
- Date arithmetic for time-based calculations
- Understanding of retail metrics like sell-through rate and inventory turns
Promotional Analysis
E-commerce runs constant promotions, and measuring their effectiveness requires careful SQL analysis. Interview questions might ask you to compare sales during a promotional period versus a baseline, calculate the incremental revenue attributed to a discount, or identify which customer segments respond best to different promotion types.
The challenge lies in defining appropriate comparison periods, handling the complexity of overlapping promotions, and accounting for factors like seasonality that might confound the analysis.
Practice with Realistic Retail Data
E-commerce SQL questions require practice with realistic data structures that capture the complexity of retail operations. Generic practice problems rarely include the nuances of multi-item orders, returns, inventory levels, and promotional calendars.
SQLSandboxes offers case studies that simulate e-commerce analytics scenarios, including customer analytics and product performance analysis. The SQL Editor provides practice databases with realistic retail schemas.
For more on preparing for domain-specific interviews, see our guide on Why Domain-Specific SQL Practice Matters and our articles on SQL in Finance and Fintech and SQL in Healthcare.
Key Takeaways
When preparing for e-commerce and retail SQL interviews, focus on these areas:
- Customer lifetime value calculations with cohort segmentation
- Retention analysis using window functions and date arithmetic
- Basket analysis with self-joins for product affinity
- Inventory metrics combining sales velocity with stock levels
- Promotional analysis with appropriate baseline comparisons
E-commerce companies value candidates who can translate business questions about customers and products into efficient, accurate queries. Showing that you understand retail metrics and their implications will differentiate you from candidates who only demonstrate technical SQL skills.