SFMC SQL Hacks: Optimize Queries and Extract Key Metrics in?Seconds

“Information is power, but only when it is organized and used effectively.”?—?William?Pollard

In Salesforce Marketing Cloud (SFMC), efficiently extracting email campaign data is crucial for performance analysis and improved segmentation. However, SQL queries in Query Studio or Automation Studio can become slow and inefficient, affecting decision-making.

In this article, I’ll share how to optimize SQL queries in SFMC to extract sends, opens, and clicks efficiently, along with a real-life case from a retail client and how DataViews helped us resolve a data issue.

?? Understanding Query Studio and Automation Studio

?? Query Studio allows you to run SQL queries on SFMC Data Views without needing to set up automation. It is useful for ad hoc analysis and quick data validation.

?? Automation Studio, on the other hand, is ideal when you need to run SQL queries on a scheduled basis and store the results in a Data Extension.

?? Which one should you use?

? Query Studio → For quick, one-time queries and data exploration.

? Automation Studio → For automated reports and ongoing data processing.

?? Real Case: Retail Client in Sportswear

A while ago, I worked with a sportswear brand using Salesforce Marketing Cloud to send promotional emails. One of their biggest challenges was measuring the actual impact of their email campaigns:

• How many emails were sent in the last week?
• How many customers opened the email and what was the first link they clicked?
• Which promoted products generated the most engagement?

Their marketing team relied on SFMC’s standard reports, but these did not allow them to analyze individual-level data or cross-reference information between campaigns.

By using Data Views and optimized SQL queries, we were able to extract key insights about open rates and clicks on featured products. This helped them segment emails more effectively and increased their online store conversion rate by 25%.

Below, I’ll share the optimized queries we used for this case.

?? 1?? Extracting Sends from the Last 7?Days

?? To analyze recent email sends, the standard query was too slow due to the large number of records in _Sent and _Job. To optimize it:

? Filter _Job.SchedTime before the JOIN to reduce the number of processed records.

? Use TOP 1000 to limit the results and improve speed.

? Use INNER JOIN instead of LEFT JOIN if missing _Job records are not needed.

?? Optimized query to retrieve recent sends:

SELECT TOP 1000 
    s.AccountID,
    s.JobID,
    s.ListID,
    s.BatchID,
    s.SubscriberKey,
    s.EventDate AS SentEventDate,
    j.AccountUserID,
    j.EmailID,
    j.FromName,
    j.FromEmail,
    j.SchedTime
FROM (
    SELECT JobID, AccountUserID, EmailID, FromName, FromEmail, SchedTime
    FROM _Job
    WHERE SchedTime >= DATEADD(day, -7, GETDATE())  -- Filters only the last 7 days
) j
INNER JOIN _Sent s ON s.JobID = j.JobID        

?? Expected Results Example:

?? 2?? Extracting the First Open for Each?User

?? If we want to analyze which users opened an email, many SFMC queries return multiple records per user, making the query slow.

?? Optimized query to get only the first open per user:

SELECT TOP 1000
    s.SubscriberKey,
    s.EventDate AS SentEventDate,
    j.EmailSubject,
    OpenEvents.FirstOpenEventDate
FROM (
    SELECT JobID, EmailSubject
    FROM _Job
    WHERE SchedTime >= DATEADD(day, -7, GETDATE())  
) j
INNER JOIN _Sent s ON s.JobID = j.JobID
OUTER APPLY (
    SELECT TOP 1 _Open.EventDate AS FirstOpenEventDate
    FROM _Open
    WHERE _Open.SubscriberKey = s.SubscriberKey
      AND _Open.JobID = s.JobID
    ORDER BY _Open.EventDate ASC
) AS OpenEvents        

?? Expected Results Example:

?? Using DataViews for Query Optimization

In Salesforce Marketing Cloud, Data Views are predefined views that store email send, open, click, and bounce data. They are essential for extracting key metrics without overloading the system.

?? Key Data Views in SFMC:

• _Sent → Email sends
• _Open → Email opens
• _Click → Link clicks
• _Job → Campaign details

A great resource for understanding how these tables connect is DataViews.io, which provides interactive diagrams of SFMC’s database structure. Using these insights helped us build optimized queries and significantly improve report execution times.

?? Conclusion

Optimizing queries in SFMC is crucial for extracting meaningful data without overloading Query Studio or Automation Studio.

? Always filter _Job.SchedTime before joining data to reduce processing time.

? Use OUTER APPLY instead of LEFT JOIN extracting only the first open or click per user. ? Limit records TOP 1000 to avoid long execution times.

? Leverage DataViews.io to understand SFMC’s database structure and optimize queries.

?? Have you encountered issues with slow queries in SFMC? Share your experience in the comments! ??

• Datacamp Platform
• Follow me on Linkedin https://www.dhirubhai.net/in/kevin-meneses-897a28127/
• Medium https://medium.com/@kevinmenesesgonzalez/subscribe
• Subscribe to the Data Pulse Newsletter https://www.dhirubhai.net/newsletters/datapulse-python-finance-7208914833608478720
• Join my Patreon Community https://patreon.com/user?u=29567141&utm_medium=unknown&utm_source=join_link&utm_campaign=creatorshare_creator&utm_content=copyLink