Context

Teams often ask analysts not just for numbers, but for a recommendation. In SQL interviews, this tests whether you can move from raw data to a defensible path forward.

Core question

Explain how you would use data in SQL to recommend a path forward for a product or business decision. Your answer should cover:

1. How you define the business question and success metric
2. How you use SQL to summarize and segment the data
3. How you compare options or identify trends
4. How you turn query results into a practical recommendation
5. What caveats or limitations you would mention

Scope guidance

The interviewer is not looking for a complex query here. They want a structured explanation of how SQL supports decision-making: selecting the right metric, aggregating data correctly, checking segments, and communicating a recommendation with appropriate caution.

Define the decision and metric first

A recommendation is only useful if it is tied to a specific decision. Before writing SQL, clarify what choice needs to be made and which metric best reflects success, such as conversion rate, revenue, retention, or support volume.

SELECT channel, SUM(revenue) AS total_revenue
FROM campaign_results
GROUP BY channel;

Use aggregation to summarize performance

SQL helps reduce raw event-level data into interpretable summaries. GROUP BY with COUNT, SUM, AVG, MIN, and MAX is often enough to compare categories, time periods, or user segments and identify where performance differs.

SELECT plan_type, COUNT(*) AS customers, AVG(monthly_spend) AS avg_spend
FROM subscriptions
GROUP BY plan_type;

Segment the data before making a recommendation

Overall averages can hide important differences. Breaking results down by region, device, plan, customer type, or time period helps determine whether a recommendation is broadly valid or only true for certain groups.

SELECT device_type, AVG(conversion_rate) AS avg_conversion
FROM daily_channel_metrics
GROUP BY device_type;

Compare options with clear decision criteria

A recommendation should be based on explicit criteria, not just the largest number in a table. You should explain what threshold or trade-off matters, such as higher revenue with acceptable churn, or lower cost with similar engagement.

SELECT
  campaign_name,
  SUM(signups) AS total_signups,
  SUM(spend) AS total_spend,
  CASE
    WHEN SUM(spend) = 0 THEN NULL
    ELSE SUM(signups)::DECIMAL / SUM(spend)
  END AS signups_per_dollar
FROM campaign_results
GROUP BY campaign_name;

State limitations and next steps

Good analysts do not overstate certainty. SQL can show patterns and comparisons, but you should mention data quality issues, small sample sizes, seasonality, missing context, or the need for follow-up analysis or experimentation.