SQL for Marketing: Essential Queries Every Marketing Professional Needs (2026)

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• Why Marketing Professionals Need SQL
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Example: Filter Paid Search Campaigns Over $1,000 Spend

SELECT campaign_name, channel, spend, impressions, clicks
FROM campaigns
WHERE launch_date >= '2026-01-01'
  AND channel = 'paid_search'
  AND spend > 1000
ORDER BY spend DESC;

This query returns only paid search campaigns launched in 2026 that spent over $1,000, sorted by highest spend first. You can swap "paid_search" for "social", "email", or any channel in your data.

Example: Total Spend and Average CPC by Channel

SELECT channel,
       COUNT(*) AS total_campaigns,
       SUM(spend) AS total_spend,
       SUM(clicks) AS total_clicks,
       ROUND(SUM(spend) / NULLIF(SUM(clicks), 0), 2) AS avg_cpc
FROM campaigns
WHERE launch_date >= '2026-01-01'
GROUP BY channel
ORDER BY total_spend DESC;

This query groups all campaigns by channel and calculates total spend, total clicks, and average cost-per-click. The NULLIF prevents division-by-zero errors if a channel has zero clicks.

Example: Campaign Spend with Conversion Data

SELECT c.campaign_name,
       c.channel,
       c.spend,
       COUNT(cv.conversion_id) AS conversions,
       ROUND(c.spend / NULLIF(COUNT(cv.conversion_id), 0), 2) AS cost_per_conversion
FROM campaigns c
LEFT JOIN conversions cv ON c.campaign_id = cv.campaign_id
WHERE c.launch_date >= '2026-01-01'
GROUP BY c.campaign_name, c.channel, c.spend
ORDER BY cost_per_conversion ASC;

LEFT JOIN ensures you see all campaigns, even those with zero conversions. This query calculates cost per conversion for each campaign, a critical metric for evaluating campaign efficiency.

Example: Monthly Spend and ROI Trends

SELECT DATE_TRUNC('month', launch_date) AS month,
       SUM(spend) AS monthly_spend,
       SUM(revenue) AS monthly_revenue,
       ROUND((SUM(revenue) - SUM(spend)) / NULLIF(SUM(spend), 0) * 100, 1) AS roi_pct
FROM campaigns
WHERE launch_date >= '2025-01-01'
GROUP BY DATE_TRUNC('month', launch_date)
ORDER BY month;

DATE_TRUNC rounds each date down to the first day of the month, so all January campaigns group together. This shows spending trends and ROI over time, helping you identify which months performed best.

Example: Top Campaigns Per Channel

SELECT channel,
       campaign_name,
       revenue,
       ROW_NUMBER() OVER (PARTITION BY channel ORDER BY revenue DESC) AS rank
FROM campaigns
WHERE launch_date >= '2026-01-01';

This assigns a rank to each campaign within its channel. The top revenue campaign in paid search gets rank 1, the second gets rank 2, and so on. Add WHERE rank <= 3 to get only the top 3 per channel.

Example: January 2026 Channel Performance

SELECT channel,
       SUM(spend) AS total_spend,
       SUM(revenue) AS total_revenue,
       ROUND((SUM(revenue) - SUM(spend)) / NULLIF(SUM(spend), 0) * 100, 1) AS roi_pct,
       ROUND(SUM(spend) / NULLIF(SUM(conversions), 0), 2) AS cpa
FROM campaigns
WHERE launch_date BETWEEN '2026-01-01' AND '2026-01-31'
GROUP BY channel
ORDER BY roi_pct DESC;

This shows which channels are most profitable. An ROI of 150% means for every dollar spent, you earned $1.50 back. Lower CPA is better, it means you're acquiring customers more efficiently.

Example: Email Engagement Metrics

SELECT e.campaign_name,
       e.sent_count,
       e.open_count,
       ROUND(e.open_count * 100.0 / NULLIF(e.sent_count, 0), 1) AS open_rate,
       e.click_count,
       ROUND(e.click_count * 100.0 / NULLIF(e.open_count, 0), 1) AS click_to_open_rate,
       COUNT(cv.conversion_id) AS conversions
FROM email_campaigns e
LEFT JOIN conversions cv ON e.campaign_id = cv.campaign_id
GROUP BY e.campaign_name, e.sent_count, e.open_count, e.click_count
ORDER BY open_rate DESC;

Open rate shows what percentage of recipients opened the email. Click-to-open rate measures how engaging your email content is once opened. Joining conversions shows which emails drive actual business results.

Example: Monthly CAC Trends by Channel

SELECT DATE_TRUNC('month', c.launch_date) AS month,
       c.channel,
       SUM(c.spend) AS monthly_spend,
       COUNT(DISTINCT n.customer_id) AS new_customers,
       ROUND(SUM(c.spend) / NULLIF(COUNT(DISTINCT n.customer_id), 0), 2) AS cac
FROM campaigns c
LEFT JOIN new_customers n ON c.campaign_id = n.campaign_id
  AND n.signup_date >= c.launch_date
GROUP BY DATE_TRUNC('month', c.launch_date), c.channel
ORDER BY month, cac;

This query shows if your CAC is increasing or decreasing over time for each channel. Rising CAC might mean increased competition or ad fatigue. Falling CAC suggests improving efficiency.

Example: Full Funnel Conversion Rates

SELECT
  COUNT(DISTINCT CASE WHEN stage = 'visit' THEN user_id END) AS visitors,
  COUNT(DISTINCT CASE WHEN stage = 'signup' THEN user_id END) AS signups,
  COUNT(DISTINCT CASE WHEN stage = 'trial' THEN user_id END) AS trials,
  COUNT(DISTINCT CASE WHEN stage = 'purchase' THEN user_id END) AS purchases,
  ROUND(COUNT(DISTINCT CASE WHEN stage = 'signup' THEN user_id END) * 100.0 /
        NULLIF(COUNT(DISTINCT CASE WHEN stage = 'visit' THEN user_id END), 0), 1) AS visit_to_signup_pct,
  ROUND(COUNT(DISTINCT CASE WHEN stage = 'purchase' THEN user_id END) * 100.0 /
        NULLIF(COUNT(DISTINCT CASE WHEN stage = 'visit' THEN user_id END), 0), 1) AS visit_to_purchase_pct
FROM funnel_events
WHERE event_date >= '2026-01-01';

CASE statements count unique users at each funnel stage. The conversion percentages show what portion of visitors make it through each step. If only 2% of visitors purchase, you have opportunities to optimize earlier stages.

Example: Monthly Purchase Cohorts

SELECT
  DATE_TRUNC('month', first_purchase) AS cohort_month,
  DATE_TRUNC('month', order_date) AS activity_month,
  COUNT(DISTINCT customer_id) AS active_customers
FROM (
  SELECT customer_id,
         order_date,
         MIN(order_date) OVER (PARTITION BY customer_id) AS first_purchase
  FROM orders
) sub
GROUP BY DATE_TRUNC('month', first_purchase), DATE_TRUNC('month', order_date)
ORDER BY cohort_month, activity_month;

The subquery finds each customer's first purchase date using a window function. The outer query counts how many customers from each cohort made a purchase in each subsequent month. This reveals retention patterns and helps forecast revenue.

"Find all campaigns with spend over $5,000 that had zero conversions."

SELECT campaign_name, channel, spend
FROM campaigns
WHERE spend > 5000
  AND conversions = 0
ORDER BY spend DESC;

This tests basic filtering. You're looking for high-spend campaigns that didn't deliver results, which is crucial for identifying underperforming investments.

"Calculate the total spend and average conversion rate by marketing channel for Q1 2026."

SELECT channel,
       SUM(spend) AS total_spend,
       AVG(conversion_rate) AS avg_conversion_rate
FROM campaigns
WHERE launch_date BETWEEN '2026-01-01' AND '2026-03-31'
GROUP BY channel
ORDER BY total_spend DESC;

This tests date filtering, aggregation, and grouping. Q1 means January through March. The query shows which channels received the most budget and how they converted on average.

"Which email campaigns had an open rate above 25% but a conversion rate below 2%?"

SELECT e.campaign_name,
       ROUND(e.open_count * 100.0 / NULLIF(e.sent_count, 0), 1) AS open_rate,
       ROUND(COUNT(cv.conversion_id) * 100.0 / NULLIF(e.sent_count, 0), 2) AS conversion_rate
FROM email_campaigns e
LEFT JOIN conversions cv ON e.campaign_id = cv.campaign_id
GROUP BY e.campaign_name, e.sent_count, e.open_count
HAVING ROUND(e.open_count * 100.0 / NULLIF(e.sent_count, 0), 1) > 25
   AND ROUND(COUNT(cv.conversion_id) * 100.0 / NULLIF(e.sent_count, 0), 2) < 2
ORDER BY open_rate DESC;

This tests HAVING clauses and calculated fields. Campaigns with high opens but low conversions have engaging subject lines but weak content or poor landing pages. This insight drives optimization.

"Find the top 3 campaigns by ROI for each channel."

WITH ranked_campaigns AS (
  SELECT channel,
         campaign_name,
         ROUND((revenue - spend) / NULLIF(spend, 0) * 100, 1) AS roi_pct,
         ROW_NUMBER() OVER (PARTITION BY channel ORDER BY 
           (revenue - spend) / NULLIF(spend, 0) DESC) AS rank
  FROM campaigns
  WHERE launch_date >= '2026-01-01'
)
SELECT channel, campaign_name, roi_pct, rank
FROM ranked_campaigns
WHERE rank <= 3
ORDER BY channel, rank;

This tests CTEs (Common Table Expressions) and window functions. The CTE calculates ROI and ranks campaigns within each channel. The outer query filters for top 3. This pattern is common in advanced SQL for marketing analytics.

"Calculate month-over-month growth in new customer acquisitions by channel."

WITH monthly_acquisitions AS (
  SELECT channel,
         DATE_TRUNC('month', signup_date) AS month,
         COUNT(DISTINCT customer_id) AS new_customers
  FROM new_customers
  GROUP BY channel, DATE_TRUNC('month', signup_date)
)
SELECT channel,
       month,
       new_customers,
       LAG(new_customers) OVER (PARTITION BY channel ORDER BY month) AS prev_month,
       ROUND((new_customers - LAG(new_customers) OVER 
         (PARTITION BY channel ORDER BY month)) * 100.0 /
         NULLIF(LAG(new_customers) OVER 
         (PARTITION BY channel ORDER BY month), 0), 1) AS growth_pct
FROM monthly_acquisitions
ORDER BY channel, month;

This tests LAG window functions, which access previous rows. LAG gets last month's new customer count for comparison. The growth percentage shows if acquisition is accelerating or slowing by channel. This is advanced SQL for marketing analysts.

What to Practice:

• Pull all campaigns from a specific date range
• Filter campaigns by channel or spend threshold
• Sort results by different metrics (spend, clicks, impressions)
• Calculate total spend across all campaigns
• Count how many campaigns ran per month

Marketing Application:

These skills let you pull specific campaign data and calculate high-level metrics like total monthly spend or average campaign cost. You can already start answering basic questions your manager asks.

What to Practice:

• Join campaigns with conversions to calculate cost per conversion
• Combine customer data with purchase history
• Link email campaigns with click and conversion events
• Join ad platform data with website analytics
• Use GROUP BY with JOINs to aggregate across tables

Marketing Application:

JOINs unlock the real power of SQL for marketing. You can now answer questions like "What's our CAC by acquisition channel?" or "Which email campaigns drove the most purchases?" These require combining data from multiple systems.

What to Practice:

• Rank campaigns within each channel by performance
• Calculate month-over-month or week-over-week growth
• Build cohort retention analyses
• Use CTEs to break complex queries into readable steps
• Create rolling averages for trend smoothing

Marketing Application:

These advanced techniques let you build sophisticated analyses: cohort retention, growth tracking, top-N reports per segment, and complex funnel analyses. This is senior-level SQL marketing analytics work.