Comprehensive Guide to Designing and Conducting Power Analysis for “Go Dark” Testing

In the realm of Digital Marketing and advertising, “go dark” tests have emerged as a strategic method to evaluate the true impact of specific channels or campaigns. By temporarily shutting down a channel in selected regions or markets while maintaining business as usual elsewhere, organizations can isolate the effect of that channel and make data-driven decisions. However, designing these experiments effectively and conducting proper power analysis can be challenging, especially given the limited resources and limited publicly available guidance on the subject.

This article provides a detailed overview of how to design and perform power analysis for “go dark” tests, enabling marketers and data analysts to conduct rigorous experiments and derive meaningful insights.

What Is a “Go Dark” Test?

A “go dark” test involves selectively suspending or disabling marketing channels in certain geographic locations or segments—while continuing normal operations elsewhere. For example, an organization might cease advertising in specific states to assess how much revenue or conversions are attributable solely to that channel.

This approach allows for:
– Isolating the incremental value of a particular channel.
– Reducing confounding factors.
– Understanding regional differences in customer behavior.

Designing a “Go Dark” Experiment

1. Define Clear Objectives

Before designing the experiment, clarify what insights you seek. Are you measuring revenue impact, conversion rate changes, or customer engagement? Clear objectives will inform your experimentation parameters.

2. Select Test and Control Regions

Choose regions or segments where the channel will be turned off (test group) and regions where campaigns continue as usual (control group). Ensure these regions are comparable in terms of customer base, market size, and other relevant factors to minimize confounding variables.

3. Determine the Duration of the Test

Decide how long the “go dark” period should last. Sufficient duration ensures data stability and accounts for customer purchase cycles. Typically, this ranges from a few weeks to a month, depending on the business model.

4. Randomization and Segmentation

If possible, randomize regions within the same market to control for external variables. Alternatively, select regions based on prior similarity to ensure comparability.

5. Metrics and Data Collection

Establish key performance indicators (KPIs), such as revenue, conversions, or engagement metrics. Implement accurate tracking methods to capture data reliably.

Performing Power Analysis for “Go Dark” Tests

Power analysis is essential to determine the likelihood that your experiment