Managing a digital advertising budget requires constant balancing. Spend quickly, and you risk exhausting your budget before the month ends. Spend slowly, and you lose valuable chances to reach your audience. Accurate, timely pacing information plays a crucial role in managing this challenge.

Many organizations currently pull data into spreadsheets and handle manual calculations. Imagine building a dynamic, automated ad spend forecast directly within your data warehouse instead.

Our goal is to guide you through this process. Leveraging the power of Snowflake and the flexibility of SQL allows you to transform your raw ad spend data into a predictive engine. This strategy centralizes your logic, scales effortlessly, and empowers marketing teams to make better decisions quickly. In this guide, we’ll walk through the steps from data preparation to building the final SQL query, which will serve as your single source of truth for ad spend pacing.

Before diving into SQL, it is important to understand the benefits behind this approach. Moving your forecasting logic into Snowflake is not just a technical task; it’s a strategic move with significant business advantages.

• A Centralized Source of Truth: Your Snowflake instance likely already houses data from Google Ads, Facebook Ads, LinkedIn, and other platforms. Building the forecast here eliminates data silos and ensures everyone, from marketing to finance, works from the same numbers. Our Data Engineering services specialize in creating unified data environments like this.
• Scalability and Speed: Snowflake’s architecture scales seamlessly as your ad spend and data volume grow. Queries that would time out elsewhere complete in seconds, providing near real-time visibility into your pacing.
• Enhanced Collaboration and Automation: Defining forecast logic in SQL makes it version-controlled, repeatable, and easy to automate. Scheduling the query to run daily allows the latest pacing data to be automatically pushed to BI dashboards or Slack channels, cutting manual work and letting your team focus on strategic priorities.

Reliable forecasts start with clean, well-structured data. Begin by creating a unified view of your ad spend across all platforms. We assume your raw data from ad sources is already loaded into Snowflake using a tool like Fivetran.

Create a single table or view including these key columns:

• REPORT_DATE: The date the spend occurred.
• PLATFORM: The advertising platform (e.g., ‘Google Ads’, ‘Facebook Ads’).
• CAMPAIGN_NAME: The campaign name.
• SPEND: The amount spent on that date.

You can build this unified model using a Common Table Expression (CTE) or a materialized view in Snowflake. Below is a simplified example combining data from two different platform tables:

With this view ready, you have a clean foundation to build your forecast.

Now to the core: the SQL query calculating pacing and forecasting future spend. Using CTEs keeps the query clear and maintainable.

The pacing logic compares how much budget you’ve spent versus the time elapsed in the budget period.

Pacing Percentage = (Percentage of Budget Spent) / (Percentage of Time Elapsed)

A 100% result means you’re perfectly on track. Above 100% indicates over-pacing, and below 100% indicates under-pacing.

Here’s the complete SQL query:

This query provides a clear report with four key metrics: your budget, actual spend to date, forecasted total spend by period end, and current pacing percentage.

The SQL query is your forecasting engine, but its real value lies in how you use the results. Connect the output directly to BI tools like Looker, Tableau, or Power BI. This lets you create dashboards that visualize your ad spend pacing, enabling your marketing team to quickly understand the data.

Visualizing this data helps your team instantly answer important questions:

• Are we on track to meet our monthly budget?
• Will we overspend or underspend based on the current run rate?
• Should we adjust daily budgets to stay on pace?

Adopting this proactive approach to budget management marks a mature data strategy. It turns data from a reactive report into a proactive, decision-making tool—a central piece in building a Modern Data & Analytical Platform.

Manually exporting ad spend data to spreadsheets for forecasting is no longer necessary. Leveraging Snowflake and SQL lets you build a robust, automated pacing and forecasting system where your data already lives. This method is more efficient, scalable, and reliable.

We demonstrated how to unify your ad spend data and build a SQL query calculating pacing and forecasted spend. The key is putting actionable data into your marketing team’s hands, helping optimize campaigns and maximize budget impact. Start with one platform to validate the concept, then expand across all marketing channels.

Ready to transform your Snowflake data into a predictive powerhouse? Contact Stellans today to learn how our data experts can build custom forecasting and analytics solutions that drive real business growth.

How is this different from the pacing reports inside Google Ads or Facebook Ads?
Platform-specific reports are useful but siloed. This Snowflake-based approach offers a holistic view across all your channels, giving you one source of truth for your total marketing budget rather than just one platform.

Can I use this for different budget periods, like quarterly or annually?
Definitely. This SQL query is designed for monthly periods, but you can adjust the DATE_TRUNC and LAST_DAY functions for other periods (e.g., ‘quarter’). Just ensure your budget input matches the new period.

What if my daily ad spend fluctuates wildly, such as on weekends?
This model uses a simple average daily spend. For more accuracy, you can enhance it with a moving average (AVG(spend) OVER (ORDER BY report_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW)) to smooth fluctuations or develop different forecasts for weekend vs. weekday spend.

Does this require any special Snowflake features or a specific tier?
No. All functions used here (DATEDIFF, DATE_TRUNC, SUM, CTEs) are standard features available in all Snowflake versions. The solution is accessible and efficient for any standard Snowflake warehouse.