Determining the Right Budget for a Marketing Experiment: A Strategic Approach

In the dynamic landscape of Digital Marketing, validating the effectiveness of different channels is essential for optimizing return on ad spend (ROAS). Recently, I’ve been managing a multi-channel marketing mix model (MMM) that’s yielding promising results. However, to further refine our strategy, we need to assess the true impact—specifically, the lift generated by certain channels or geographic regions.

One approach under consideration is a geo-based experimental design: allocating spend to select areas while withholding it from others, then measuring the differential impact. This method can provide valuable insights into which regions or channels truly drive brand awareness and sales.

Challenges in Budgeting for Brand-Focused Experiments

Unlike direct response campaigns on platforms like Instagram, TikTok, or Snapchat—where immediate attribution and conversion data simplify analysis—brand marketing efforts are inherently more nuanced. These campaigns often lack explicit conversion points, making it harder to attribute lift directly to specific spend. As a result, determining the appropriate investment level for such experiments requires careful planning and statistical rigor.

Key Factors in Budget Calculation

To accurately estimate the budget needed for a geo-experiment that can detect a meaningful lift, consider the following components:

1. Baseline Variability and Data Noise
   Understand the natural fluctuations in brand metrics (e.g., brand awareness scores, website visits, or sales data) across regions and time periods. More variability means you need a larger sample size (i.e., more spend) to confidently detect differences.

2. Expected Effect Size (Lift)
   Estimate the minimum lift you consider practically significant. For instance, do you aim to detect a 10% increase in brand awareness? The larger the expected effect, the smaller the required sample size.

3. Statistical Significance and Power
   Decide on your desired significance level (commonly 5%) and power (usually 80% or higher). These thresholds determine the probability of correctly identifying a true lift and avoiding false positives.

4. Duration of the Experiment
   Longer testing periods help smooth out short-term fluctuations and provide more reliable data, but they also require a larger total budget.

Calculating the Required Budget

Applying statistical power analysis tools can help you determine the necessary sample size (and thus budget). The process generally involves:

• Assessing historical baseline data to quantify variability.
• Defining the minimum lift you wish to detect.
• Choosing statistical thresholds (sign