How to Run a Causal Impact Analysis Without Using R

What is Causal Impact Analysis?

Causal Impact Analysis is a powerful statistical technique used to estimate the effect of an intervention on an outcome. It helps marketing leaders distinguish between correlation and causation, offering clear insights into whether a campaign, product launch, or pricing change truly impacted key business metrics.

Why is Causal Impact Analysis Important?

This analysis enables marketers to make data-driven decisions by estimating what would have happened in the absence of an intervention. By isolating the true effect of a marketing campaign, brands can validate strategies and confidently present their findings to stakeholders.

The Traditional Method: Using R for Causal Impact Analysis

Historically, marketers relied on R, a programming language designed for statistical computing, to conduct Causal Impact Analysis. The process included:

• Installing the CausalImpact Package: Required familiarity with R and package management.
• Data Preparation: Creating a properly formatted time series dataset covering the intervention period and control data.
• Running the Analysis: Using R scripts to specify test parameters, debug errors, and interpret complex outputs.
• Interpreting Results: Understanding statistical outputs, graphs, and summary statistics.

While effective, this method was often inaccessible to marketers without coding experience.

How to Run a Causal Impact Analysis in Stella

Stella simplifies Causal Impact Analysis, making it easy to run within our Incrementality Tool without needing R. Users can create a free Stella account and run one Causal Impact study before needing to upgrade. Here’s how:

Step 1: Prepare Your Data

Format your dataset with the following columns:

• Date: Include at least 120 days of historical data.
• Total Revenue: The primary business outcome you want to analyze.
• Test Channel Spend: The amount spent on the specific campaign or channel being tested.
• Test Period: Include 15-30 days after the intervention period to measure impact.

Step 2: Run the Analysis in Stella

1. Upload your dataset into the Stella UI.
2. In the "Study Form" tab, leave the Regions field blank.
3. Navigate to the "Post Test Analysis" tab.
4. Under "Advanced Options", unselect all toggles. This ensures Stella defaults to the causal impact model, running an analysis similar to R’s CausalImpact package.
5. Click "Run Analysis" and review the results to determine statistical significance and impact.

Examples of Causal Impact Analysis in Action

1. Evaluating a New Marketing Channel
A brand launches TikTok ads and wants to measure their effect on organic search traffic. Stella compares pre- and post-launch performance to determine if TikTok ads drove incremental traffic.

2. Testing the Impact of Pausing Prospecting Campaigns
A brand temporarily turns off prospecting campaigns to analyze their impact on total sales. Stella’s analysis reveals whether these campaigns were driving new customer acquisition or had minimal effect.

3. Assessing New Creative Performance
A company introduces fresh ad creatives and wants to determine if engagement and conversion rates improve. Stella helps analyze whether the new creative approach was truly effective.

4. Measuring the Effectiveness of Updated Email Copy
An email marketing team revamps messaging and uses Stella to compare pre- and post-update performance. The analysis shows whether engagement and conversions increased.

Conclusion

Causal Impact Analysis is essential for marketing leaders looking to validate their strategies with data-driven insights. While R was once the primary tool for this analysis, Stella now offers a no-code alternative that allows users to run a free study before upgrading. By leveraging Stella’s Incrementality Tool, marketers can quantify the impact of their initiatives and confidently refine their strategies to drive measurable business growth.

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