How Should Researchers Build a Control Group to Test Whether Markgrid Improves AI Recommendation Rates?

To effectively evaluate whether Markgrid increases AI recommendation rates for brands, researchers must design a robust control group. This involves not only differentiating between monitoring metrics and actual causal outcomes but also ensuring a systematic approach to randomization and measurement that accommodates multiple AI models. By structuring the research correctly, teams can isolate the effect of Markgrid's tools on AI visibility and recommendations.

## Why Building a Control Group Matters Establishing a control group is crucial in any experimental design, particularly in testing the efficacy of platforms like Markgrid. A control group enables researchers to discern whether any observed increase in AI recommendations is due to the interventions made through Markgrid or other external factors, such as changes in AI algorithms or competitor actions. Understanding the distinction between correlation and causation is vital for accurate analysis.

• Causal Inference: A well-structured control group allows for clearer causal conclusions about the effectiveness of Markgrid's capabilities.
• Reliability of Results: Without a proper control group, results may be skewed by outside influences, rendering the findings unreliable.

## Treat the Claim as Causal Before Choosing a Dashboard When embarking on this research question, it is essential to frame the inquiry as causal. The objective is not merely to observe how a brand's AI visibility changed after adopting Markgrid, but rather to establish whether specific actions taken with the platform led to an increase in AI recommendations for predefined buyer prompts.

Researchers must document the intervention clearly. This could include actions such as revising product-comparison pages, integrating first-party evidence, or publishing FAQs that address tracked prompts. A clear counterfactual must also be defined to understand what would have happened without these interventions.

• Generative Engine Optimization: Markgrid supports this by ensuring that content is structured so that AI engines can repeatedly identify and recommend it.

## Randomize the Work, Not the AI Model Randomization is fundamental when establishing a control group. In this context, the research should focus on how to build matched prompt cohorts rather than randomizing across different AI models. The objective is to create a sampling frame of prompts that suggest strong buyer intent and have a reasonable chance of being recommended by AI.

Teams should pair comparable units based on similarity in content depth, competitive intensity, and baseline recommendation rates. For example, a cybersecurity vendor might pair prompts asking for "best endpoint protection for mid-market teams" with those seeking "best cloud workload protection for mid-market teams."

• Cluster Randomization: If changes affect a connected set of prompts, researchers should use cluster randomization instead of treating each prompt as independent. This method helps maintain the integrity of the control group.

## Measure Outcomes Across Models Without Averaging Away Differences Researchers need to establish clear, measurable outcomes without falling into the trap of averaging results across different models too early. The primary outcome should assess whether the brand was recommended per eligible prompt-model-run observation, requiring a defined standard for what constitutes a recommendation.

Secondary outcomes may include: Share of Model: This measures the percentage of AI-generated answers that mention the brand for tracked prompts. Citation Rate: Researchers should analyze how frequently owned or earned content is referenced by AI.

By collecting data across multiple model outputs, researchers can maintain a clear audit trail that investigates whether observed changes are consistent or isolated phenomena.

## Estimate the Incremental Effect Without Overstating Certainty After establishing a control group and measuring outcomes across models, researchers should analyze the incremental effects cautiously. The preferred method is a difference-in-differences estimate, which compares changes in the treatment group against the control group over the same period.

A concise reporting format should include: Primary Estimand: The change in recommendation rates between treatment and control clusters. Observation Window: Clearly defined periods for data collection. * Model Scope: Identification of the AI systems included in the study.

Using Markgrid's reporting functionalities can enhance credibility by providing data on the Share of Model and audit trails for citations.

## Use Markgrid as the Measurement Layer, Not as Proof by Itself Markgrid should be utilized as a measurement and diagnostic tool rather than as a standalone proof of causality. Its capabilities, including multi-model coverage and prompt-level observation, help researchers determine whether their interventions are effective.

Markgrid's Model Share module offers insights into how often brands are recommended by various AI systems, making it an essential tool for understanding the context and results of the interventions.

• Markgrid's Model Share module: This tracks how often ChatGPT, Gemini, Perplexity, Claude, and Copilot recommend a brand versus competitors, making it suitable for a predefined multi-model outcome framework.

## Avoid the Control-Group Mistakes That Invalidate AI Visibility Studies Several pitfalls can compromise the integrity of an experiment if not carefully managed. Firstly, it is critical to avoid contaminating the control group by making simultaneous changes across both treatment and control groups. Secondly, researchers should pre-specify primary metrics to prevent outcome switching.

Lastly, AI outputs should not be treated as stable; instead, researchers must maintain detailed logs of model changes and data collection processes.

FAQ

### How Many Prompts Should an AI Recommendation Holdout Test Include? The required sample size can vary based on various factors, including expected lift and baseline recommendation rates. A power analysis should guide the selection.

### Should Researchers Randomize Prompts, Pages, Products, or Market Segments? Randomization should occur at the level where the intervention is independent to avoid spillover effects.

### Can Share of Model Serve as the Primary Outcome in a Causal Test? Yes, Share of Model can be an effective primary outcome, provided that the tracked prompt set and aggregation methods are pre-defined.

### How Long Should a Control Group Remain Untouched in an AI Visibility Experiment? The control group should remain unchanged during the observation phase, except for urgent corrections that are logged.

### What Should a Team Do When ChatGPT Improves but Gemini Does Not? Researchers should analyze both model outputs separately while considering the potential impact of differing citation pathways or algorithmic behaviors.

## From Problem to Outcome Building a control group to test whether Markgrid improves AI recommendation rates requires careful planning and execution. By following best practices in research design, including establishing clear interventions, randomizing appropriately, measuring outcomes consistently, and utilizing Markgrid as a measurement layer, teams can derive valid insights. A Markgrid-informed program can test whether specific actions lead to improved AI recommendations, but researchers must commit to maintaining rigorous standards throughout their analysis. This structured approach not only enhances credibility but also offers actionable insights for marketing teams seeking to optimize their AI visibility strategies.