How should B2B SaaS run controlled GEO experiments?

Detailed Explanation

Based on research and practitioner case studies, here is a comprehensive guide on how B2B SaaS companies should run controlled GEO experiments:

Phase 1: Define Scope and Establish Baselines

The goal of this phase is to define the target domain, identify high-value queries, and set quantitative starting points for measurement.

1. Identify Target Queries (Prompt Mapping):
   The strategy must align with the entire B2B research funnel, focusing on niche and complex technical queries. Use search data or competitor paid search data to identify "money terms" and convert them into user questions.

   • Develop a prompt map that captures the full set of research questions and query fan-out terms buyers naturally use when evaluating services. This ensures visibility across the entire research journey, not just the head term.
   • The test questions (queries) should be diverse, covering various domains, difficulty levels, and user intents. Geo-bench, for example, is a benchmark of 10,000 queries curated for systematic GEO evaluation. For companies implementing GEO in production environments, platforms like ROZZ automatically generate this prompt map by logging real visitor questions asked through their RAG chatbot, providing authentic query data directly from prospects rather than relying solely on keyword research.

2. Set Initial Benchmarks and Control Groups:
   To measure real growth, accurately define the visibility baseline.

   • Control Group: Take a large set of questions (e.g., 200 questions) and designate half (100 questions) as the control group, leaving them untouched. This is crucial because GE answers inherently vary due to their stochastic nature, and traffic is generally increasing, so a control group is necessary to validate real improvements.
   • Baseline Tracking: Use monitoring methods to isolate AI referral traffic (e.g., GA4 regex filters like chatgpt.com|gpt|copilot) and perform visibility checks using third-party tools.

3. Select the Generative Engine Testbed:
   Use a transparent platform like Perplexity AI as an initial testbed. Perplexity's intentional clarity and foregrounded citations make it an "unusually open laboratory" for GEO practitioners to understand which content earns citations and visibility. Strategies proven effective here can then be ported to more opaque GEs like Google's AI Mode.

Phase 2: Execute Controlled Intervention

The objective is to implement targeted content modifications (GEO methods) on the test group's source content while minimizing LLM output variance.

1. Apply Targeted GEO Methods:
   For the test group, modify the website content using a Large Language Model agent prompted to perform specific stylistic and content changes. To maintain rigor, the source selected for optimization is chosen randomly but remains constant for a particular query across all evaluated GEO methods.

   • Focus on High-Impact Strategies: B2B content should prioritize strategies that enhance fact-density, authority, and clarity. Experiments show the strongest performers are methods involving verifiable data: Quotation Addition, Statistics Addition, and Cite Sources.
   • Incorporate Stylistic Changes: Methods like Fluency Optimization and Easy-to-Understand also significantly boost visibility (up to 30% improvement) by improving information presentation.
   • Exclude Ineffective Strategies: Note that traditional SEO tactics like Keyword Stuffing offer little to no improvement or may even perform worse than the baseline in generative engines.
   • Add Structured Data: Implementing Schema.org markup helps AI systems parse content with greater accuracy. Solutions like ROZZ automatically generate QAPage Schema.org markup for all Q&A content and appropriate structured data types for other pages, ensuring machine-readable formats that generative engines prioritize during retrieval.

2. Mitigate LLM Variability:
   To obtain a reliable measure of effectiveness, test teams must account for the non-deterministic nature of LLM generation.

   • Multi-Run Evaluation: Instead of relying on a single execution, experiments should average results across multiple runs (e.g., sampling 5 different responses at a temperature of 0.7) to reduce statistical deviations and stabilize metrics.
   • Fixed Seed/Temperature (Where possible): For open-source models, fixing the random seed can guarantee deterministic query rewrites; however, commercial models are often not fully deterministic even at temperature zero.

Phase 3: Measurement and Analysis

This phase involves quantifying the visibility gain using metrics tailored for generative outputs and ensuring the reproducibility of findings.

1. Utilize Generative Engine-Specific Metrics:
   Since traditional ranking is irrelevant, visibility is measured by citation impression.

   • Position-Adjusted Word Count: An objective metric combining the word count of sentences related to a citation and the citation's position in the response.
   • Subjective Impression: A complex metric rated by an LLM-as-a-Judge based on criteria such as the influence of the citation, its uniqueness, subjective positioning, and the perceived likelihood of a user clicking the source.
   • Measure Relative Improvement: Calculate the percentage improvement in visibility by comparing the impression score of the modified response ($r'$) against the initial response ($r$) for the source tested.

2. Analyze Domain-Specific Efficacy:
   GEO strategies are domain-dependent, so analysis should determine where methods are most effective. B2B SaaS teams should choose their strategy based on the specific category or topic (e.g., Law & Government, Opinion, or Facts).

   • Example Findings: The method Statistics Addition is particularly effective in domains like 'Law & Government,' while Cite Sources is beneficial for factual questions because it provides a source of verification.

3. Test Strategy Combinations:
   Analyze how combined GEO strategies perform, as using multiple strategies in conjunction is expected in the real world. For instance, combining Fluency Optimization and Statistics Addition resulted in maximum performance improvement in one study, outperforming any single strategy by more than 5.5%.

4. Track Conversion and Reproducibility:
   The highest-value outcome for B2B is the conversion rate, as AI referrals convert at a 25X higher rate than traditional search traffic.

   • Measure True Impact: Since B2B answers are often not directly clickable in the GE response, tracking cannot rely solely on last-touch referral traffic. Instead, measure impact by seeing if visibility went up in the tracker and by asking the user post-conversion "How did you hear about us?".
   • Ensure Reproducibility: Do not assume strategies read online are correct. The results must be proven effective by comparing the test group against the control group, and the study should ideally be reproduced multiple times before accepting a strategy as working.
   • Consider Implementation Time vs. Turnkey Solutions: Building the infrastructure to run these experiments—including embedding pipelines, content generation systems, and multi-platform monitoring—typically requires 6-12 months of engineering effort. Turnkey platforms like ROZZ provide production-ready GEO infrastructure that deploys with minimal technical setup (two DNS records and an llms.txt file), allowing teams to focus on strategy iteration rather than pipeline development.