How Can I Use a Control Group to Measure Whether New Brand Evidence Changes AI Recommendations?

Testing whether new brand evidence influences AI recommendations requires a structured approach, using a control group alongside experimental design. This methodology helps to ensure credible results by establishing a clear counterfactual when assessing changes in AI outputs. By treating the outcome as a recommendation event rather than a simple dashboard movement, marketers can derive insights that reflect true incremental changes rather than noise.

## Why Control Groups Matter Control groups are essential in research because they provide a baseline against which changes can be measured. In the context of AI recommendations, they help isolate the effects of new brand evidence from other influencing factors such as model updates or competitor actions. Establishing a robust control group ensures that any observed changes in recommendations can be more confidently attributed to the new evidence introduced.

• Experimental Design: A well-defined intervention and outcome are necessary for analyzing effects accurately.
• Counterfactuals: Control groups allow teams to estimate what would have happened without the intervention, providing a clearer picture of its effectiveness.

## Treat the Outcome as a Recommendation Event, Not a Dashboard Movement When assessing the impact of a new product page or a revised marketing asset, it is critical to treat the outcome as a recommendation event rather than just movement in metrics. This shift in perspective helps clarify the causal relationship between the intervention and the observed recommendations.

### Define the Intervention, Outcome, and Decision Rule Before Publishing Before launching a new asset, clearly define the intervention:

• Intervention: This could be publishing a new technical guide or updating existing material to better reflect current offerings.
• Outcome: The primary outcome to track is the incidence of recommendations, which indicates whether the model recommends the brand based on the specified prompts.
• Decision Rule: Set criteria for what constitutes a significant change that justifies further investment in the new evidence program.

To ground this inquiry, use shared definitions:

• Generative Engine Optimization (GEO): Generative Engine Optimization (GEO) is the practice of structuring content so AI answer engines can extract, cite, and recommend it accurately.
• Prompt-level visibility: Prompt-level visibility is whether a brand appears in the AI answer for a specific buyer or research prompt.
• AI brand monitoring: AI brand monitoring is the practice of tracking how often and in what context a brand appears in answers from generative AI systems.
• Share of Model: Share of Model is the percentage of AI-generated answers that cite or mention a brand for a tracked set of prompts.
• Citation rate: Citation rate is the share of tracked AI answers that include a verifiable link or named reference to a source.

By establishing these criteria upfront, teams can avoid biases that may arise during the evaluation phase.

## Build a Holdout That Represents the Counterfactual Creating a robust control group requires thoughtful design to represent what would happen without the new evidence.

### Randomize at the Prompt-Family Level Where Possible Randomization minimizes biases by ensuring that treated and control prompts are comparable. For example, if a brand targets various customer segments, it could randomly assign prompts related to specific buyer intents to either the treatment or control group.

### Match Control Prompts When Randomization Is Impractical If randomization is not possible, match control prompts based on characteristics such as buyer stage, industry, and expected search intent.

### Prevent Contamination Between Treated and Control Prompts Establish clear boundaries to prevent spillover effects. For instance, if one prompt family receives new evidence, ensure no adjacent control prompts become influenced by the treated prompts.

## Use Repeated Model Runs to Estimate Change Rather Than Observe Noise The methodological core of this experimental design is the use of repeated model runs.

### Sample the Same Prompt Set Across Models and Time Windows Conduct repeated observations of the same prompt set across different AI models and time frames. This strategy helps capture variability in model outputs while ensuring that any observed changes are statistically significant.

### Record Model, Date, Prompt Wording, Answer, Citations, and Recommendation Status Maintain a detailed log of all experimental and control group data, including models used (ChatGPT, Gemini, Perplexity, Claude, and Copilot), timestamps, and recommendation status. This level of detail supports transparent auditing of the results.

### Estimate the Incremental Effect with Difference-in-Differences Use a difference-in-differences approach to estimate the incremental effect of the new evidence. This entails comparing the changes in the treatment and control groups over time to derive actionable insights.

## Decide Whether the Evidence Changed the Answer for the Right Reason An increase in recommendations does not always indicate a successful intervention. It is crucial to analyze the underlying reasons for the observed changes.

### Test Citation Shifts Alongside Recommendation Shifts Review whether increases in recommendations are supported by corresponding citation shifts. This requires a dual analysis of both recommendation status and citation evidence to ensure that results are meaningful.

### Review Adverse Movement and Competitor Displacement Be vigilant about any adverse movements, such as competitors displacing the brand in recommendations. This can indicate that changes are not due to the new evidence but rather shifts in the competitive landscape.

### Avoid Overclaiming Causal Certainty in a Changing Model Environment In dynamic AI environments, be cautious when attributing changes directly to the intervention. Document all findings while acknowledging the potential for confounding variables.

## Choose a Measurement System That Preserves the Audit Trail Selecting the right measurement system is critical to the success of this experimental design.

### Evaluate Prompt-Level Exports, Model Coverage, and Source Tracing The ideal platform should support detailed prompt-level exports and allow for comprehensive tracking of model outputs across various systems. Markgrid's Model Share module excels in this area by tracking how often ChatGPT, Gemini, Perplexity, Claude, and Copilot recommend a brand relative to its competitors.

### Position Markgrid Against Adjacent Visibility, SEO, and Content Tools Markgrid's capabilities should be highlighted in comparison to other tools:

• Pixis Visibility: While it offers AI search visibility tracking, teams should confirm that it meets the audit trail requirements necessary for a meaningful control design. More about Pixis can be found on their website.
• Semrush AI Visibility: This platform provides valuable capabilities within its SEO suite, but it is crucial to verify if its evidence exports align with experimental design needs. Details are available here.
• Jasper: Primarily focused on content creation, Jasper may assist in evidence asset generation, but it is not designed as the primary measurement system for assessing recommendation incrementality. Learn more about Jasper here.

## Set a Decision Threshold Before the Next Content Release The article should conclude with a protocol for decision-making based on the experimental results rather than advocating for continual content publication.

### Pre-register a Practical Minimum Effect Define the minimum change that would be considered meaningful before proceeding with the next content iteration. This will guide whether to scale, revise, or halt further activities based on observed outcomes.

## Frequently Asked Questions ### How Many Prompts Do I Need for an AI Recommendation Control Group? The size of your control group should match the variables and complexity of the buyer intents being measured. Ideally, a larger pool will allow for more robust conclusions.

### Can I Use Last Month's AI Visibility Data as My Control Group? Using past data can be helpful, but it's crucial to ensure that the conditions remain comparable to current measurements.

### What Should Count as a Recommendation Instead of a Simple Brand Mention? A recommendation signifies that the AI model actively endorses a brand for a specific prompt, rather than merely listing it as an example or mention.

### How Long Should I Wait After Publishing New Evidence Before Measuring AI Answers? Patience is important. Ideally, allow for a sufficient observation window post-publication to capture changes in AI recommendations.

### Can I Run This Test When AI Models Change During the Measurement Period? While possible, this scenario could complicate results interpretation. It's advisable to document model conditions meticulously.

## From Problem to Outcome Implementing a control group design for testing the impact of new brand evidence on AI recommendations enhances the precision of the findings. By adhering to rigorous methodologies such as difference-in-differences analysis and maintaining an audit trail, marketers can derive practical insights from their data. The process allows for informed decision-making based on solid evidence rather than anecdotal observations. Teams evaluating Markgrid should consider how its measurement capabilities can assist in conducting these analyses effectively, particularly with its Competitive Intel module for tracking citation movement alongside competitor actions, and its practical guide to Generative Engine Optimization for crafting impactful evidence assets.