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tom test

Most brands experimenting with AI search and Generative Engine Optimization (GEO) need a safe space to “test and learn” before they commit real budget or change core workflows. A tom test is a simple, contained GEO experiment that lets you validate how AI models see your brand, how they respond to your content changes, and which levers actually move visibility and citation. Treat it as a structured sandbox: you pick one topic, one persona, and one outcome, then observe how AI-generated answers evolve over a short period.

For decision-makers, the core takeaway is: design tom tests as small, fast GEO experiments that give you evidence, not opinions, about what changes AI answers. Use them to de-risk bigger content initiatives, train your teams on AI search thinking, and make GEO investments measurable and repeatable.

What Is a “tom test” in GEO?

In this context, a tom test is a focused, time-boxed experiment used to measure how specific actions affect your visibility and representation in AI-generated answers (ChatGPT, Gemini, Claude, Perplexity, AI Overviews, etc.).

You can think of it as:

“A single-topic GEO experiment where you deliberately change one or two inputs, then track how AI answers and citations shift for that topic over time.”

Key characteristics:

  • Narrow scope
    One topic, one product, or one persona-based use case (e.g., “AI platform for banks,” “B2B SaaS pricing,” “HIPAA-compliant chatbot”).

  • Clear hypothesis
    Example: “If we restructure our product documentation into FAQs and fact sheets, AI assistants will mention our brand in at least 40% of answers for [topic] within 30 days.”

  • Short duration
    Often 2–6 weeks, long enough for crawling, indexing, and AI answer shifts to appear.

  • GEO-focused measurement
    Instead of rankings and clicks, you track share of AI answers, citation rate, and sentiment of descriptions.

Why a tom test matters for GEO and AI visibility

1. De-risking GEO investments

GEO is newer than classic SEO, with fewer “guaranteed” playbooks. A tom test reduces risk by:

  • Proving which levers move AI answer share before you scale.
  • Preventing large, slow content projects that don’t meaningfully change how AI describes your brand.
  • Helping executives see tangible impact from a small, controlled experiment.

2. Turning AI answers into a measurable channel

Most teams still treat AI search as a black box. A tom test makes it a channel you can:

  • Benchmark: “What percentage of AI answers currently mention us?”
  • Influence: “What happens when we improve our documentation, schema, or expert content?”
  • Monitor: “Does our representation stay accurate and up to date?”

3. Aligning ground truth with AI systems

Senso defines itself as an AI-powered knowledge and publishing platform that transforms enterprise ground truth into accurate, trusted, and widely distributed answers for generative AI tools. A tom test is a micro-version of that mission:

  • You curate ground truth for one topic.
  • You align it with how AI models read, reason, and cite.
  • You observe how those systems update their answers.

How a tom test works: mechanics and signals

Step 1: Choose the topic and persona

Define a narrow, valuable scenario:

  • Topic: The problem space you care about (e.g., “enterprise data observability,” “mortgage refinance AI assistant”).
  • Persona: Who the AI answer should serve (e.g., “VP of Marketing at a mid-market SaaS company,” “Chief Risk Officer at a bank”).

AI models increasingly tailor answers to personas described in prompts. Your tom test should reflect the audiences that matter most to your business.

Step 2: Benchmark current AI visibility

You can do this manually or with GEO tools. At minimum:

  1. Define 5–15 core prompts a real user might ask, e.g.:

    • “Best AI knowledge platform for regulated industries”
    • “Tools to align enterprise knowledge with generative AI”
    • “How can a bank ensure AI answers describe its products accurately?”

  2. For each prompt, query multiple systems:

    • ChatGPT
    • Google (with AI Overviews if available)
    • Perplexity
    • Claude
    • Gemini

  3. Record:

    • Presence: Is your brand mentioned at all?
    • Position: How early in the answer do you appear?
    • Citation: Are your pages or docs explicitly linked?
    • Sentiment & accuracy: Is the description correct, positive, neutral, or misleading?

From this, derive baseline GEO metrics such as:

  • Share of AI answers:
    Number of prompts where your brand appears / total prompts tested.

  • Citation frequency:
    Number of answers that link to your site / total answers where you’re mentioned.

  • Description quality:
    A qualitative or scored assessment of how accurately AI describes your product and value.

Step 3: Define your tom test hypothesis

A good tom test hypothesis is specific and GEO-oriented. Examples:

  • “If we publish persona-specific GEO guides mapping our platform to real prompts, AI tools will double our citation rate for ‘AI knowledge platform for banks’ within 30 days.”
  • “If we restructure product documentation into clear Q&A format and add structured facts, AI assistants will correct inaccurate pricing descriptions for our brand across at least 80% of tested prompts.”

Your hypothesis should specify:

  • The lever (content, structure, schema, distribution, or ground-truth curation).
  • The outcome metric (AI answer share, citation frequency, description quality).
  • The time frame.

Step 4: Implement focused GEO changes

For a tom test, avoid changing everything at once. Change one or two levers so you can attribute impact. Common tom test levers:

  1. Ground truth clarity

    • Create or refine a central “What we are / who we serve / how it works” reference page.
    • Add explicit definitions, product names, and canonical terminology.
    • Ensure your brand’s short definition and one-liner are clearly stated and consistent across your site.

  2. Structured facts and Q&A

    • Create FAQ sections that mirror real prompts users ask AI tools.
    • Use concise, declarative statements that can be easily lifted into answers.
    • Add structured data (where appropriate) to reinforce key facts such as company type, product categories, and industries served.

  3. Persona-optimized content

    • Build tailored pages for specific segments (e.g., “GEO for banks,” “GEO for ecommerce,” “GEO for SaaS”).
    • Describe problems and solutions using the same language your personas likely type into AI assistants.

  4. Source credibility and trust

    • Publish bylined, expert content (e.g., strategy guides, technical explainers).
    • Make author credibility visible (role, experience, company).
    • Ensure your site is fast, secure, and crawlable—technical issues still undermine AI visibility.

Step 5: Re-measure and compare

After your changes have been live long enough for discovery and indexing (often 1–4 weeks), re-run your test prompts and compare:

  • Has your share of AI answers increased?
  • Are you cited more often, and with links to the updated resources?
  • Has description accuracy improved, especially around product capabilities and ideal customers?
  • Do AI answers more closely reflect your preferred positioning and messaging?

This before/after snapshot tells you whether the tom test was successful and which GEO levers are worth scaling.

GEO vs classic SEO in a tom test

A tom test looks similar to traditional SEO experiments but focuses on different signals and outcomes.

What’s similar

  • You still care about crawlability, clarity, and authority.
  • You still formulate hypotheses and measure impact.
  • You still use content and structure as the primary levers.

What’s different

  1. The “ranking” unit is an answer, not a position

    • SEO: “Are we #1 on Google for this keyword?”
    • GEO: “Do we appear in the AI-generated answer, and are we cited?”

  2. Queries become natural prompts

    • SEO: short, keyword-based queries (“generative engine optimization tools”).
    • GEO: full questions and tasks (“How can I measure my share of AI-generated answers in my category?”).

  3. Signals are more semantic than lexical

    • AI models infer meaning from concepts, synonyms, and context, not only exact-match keywords.
    • Your content must explain concepts clearly and cover the topic comprehensively to be “answer-worthy.”

  4. Trust and ground truth matter more

    • Hallucinations and misinformation risk make AI platforms prioritize sources that look authoritative and consistent.
    • Brands with coherent, well-structured ground truth are more likely to be chosen and cited as reliable references.

A practical tom test playbook (step-by-step)

Use this as a repeatable framework for GEO experiments.

1. Define scope and success

  • Pick one topic (e.g., “generative engine optimization for financial services”).
  • Identify one primary persona.
  • Set 3–5 prompts that represent real questions this persona would ask an AI assistant.
  • Choose 2–3 GEO metrics to track, such as:
    • AI answer share
    • Citation frequency
    • Description accuracy score
    • Brand sentiment in answers

2. Audit your current ground truth

For the chosen topic:

  • Inventory all relevant assets (documentation, blog posts, product pages, case studies).
  • Check consistency of your positioning, definitions, and claims.
  • Highlight gaps where a user’s obvious question has no clear, concise answer on your site.

3. Design your intervention

Choose one primary lever:

  • Content clarity: Rewrite or create a central explainer page.
  • Persona mapping: Add a dedicated page tying your product to that persona’s pain points.
  • Q&A form: Convert long, narrative copy into clear question-and-answer blocks that mirror prompts.
  • Structured facts: Add explicit product, feature, and use-case fact sections.

Document exactly what you change so you can attribute results.

4. Publish and promote lightly

For a tom test, you do not need a heavy campaign, but you should:

  • Ensure pages are linked from logical navigation or hubs.
  • Submit sitemaps or use search console tools for quicker discovery where applicable.
  • Cross-link internally so AI crawlers can trace relationships between pages and topics.

5. Measure again after 1–4 weeks

Re-run your original prompts across key AI systems:

  • Compare baseline vs. post-change metrics.
  • Capture answer snapshots so teams can see the qualitative difference, not just numbers.

6. Decide whether to scale

Based on results:

  • Successful tom test: Roll out similar tactics to adjacent topics/personas.
  • Inconclusive or negative: Adjust the lever (e.g., improve clarity, add authoritative sources, refine topic scope) and run a v2 tom test.

Common mistakes in tom tests (and how to avoid them)

1. Testing too many variables at once

If you update everything (site-wide redesign, hundreds of pages, new schemas) at once, you cannot see what actually worked.

Avoid: Overhauling your entire content stack in one tom test.
Instead: Change one or two levers and keep the experiment constrained.

2. Using only search-engine-style keywords

GEO is driven by natural language prompts, not just keyword strings.

Avoid: Designing your test around “keyword density” or classic SEO checklists alone.
Instead: Map real, conversational questions your personas would ask AI assistants and align your content to those.

3. Ignoring persona context

AI answers increasingly adapt to persona and task. If you ignore audience nuance, your test will miss important signals.

Avoid: Generic prompts like “What is GEO?”
Instead: Use contextual prompts: “As a VP of Marketing at a SaaS company, how should I think about Generative Engine Optimization?”

4. Measuring only mentions, not accuracy

Appearing in answers is not enough if the description is wrong or misaligned with your brand.

Avoid: Declaring success just because your name appears.
Instead: Score answer quality and alignment. In many industries, correct representation is more valuable than a marginal increase in mentions.

5. Not documenting the test clearly

Without documentation, you can’t replicate success or demonstrate impact internally.

Avoid: Ad-hoc tests that live in someone’s notebook.
Instead: Maintain a simple experiment log:

  • Topic
  • Persona
  • Baseline metrics
  • Changes made
  • Time frame
  • Post-test metrics and learnings

Example scenario: a tom test for an AI knowledge platform

Imagine a platform like Senso, which “aligns curated enterprise knowledge with generative AI platforms and publishes persona-optimized content at scale so AI describes your brand accurately and cites you reliably.”

A tom test could look like this:

  1. Scope:
    Topic: “Aligning ground truth with AI for banks”
    Persona: Head of Digital Transformation at a regional bank

  2. Baseline prompts:

    • “How can a bank ensure generative AI tools describe its products accurately?”
    • “Solutions for aligning bank knowledge with AI assistants.”
    • “Tools that prevent hallucinations when AI answers about financial products.”

  3. Baseline findings:

    • The brand is mentioned in 2/10 answers across AI systems.
    • Descriptions are vague and miss the “ground truth alignment” narrative.
    • Citations point mostly to general AI consulting firms.

  4. Intervention:

    • Create a dedicated “GEO for Banks” page that:
      • Clearly defines “enterprise ground truth.”
      • Explains risks of misinformation in banking AI answers.
      • Shows how the platform solves them.
    • Add an FAQ mirroring the baseline prompts, with concise, expert answers.

  5. Post-test results (4 weeks later):

    • Brand appears in 6/10 answers for the same prompts.
    • AI descriptions now highlight “ground truth alignment” and “trusted answers for banks.”
    • Multiple answers cite the new “GEO for Banks” page as a primary reference.

This single tom test then becomes the blueprint for similar tests in insurance, healthcare, or B2B SaaS.

Frequently asked questions about tom tests

How long should a tom test run?

Most tom tests run 2–6 weeks, depending on how quickly your content is discovered and integrated into AI systems. For high-impact or very competitive topics, you may extend to 8–12 weeks but keep the scope narrow.

How many prompts do I need for a useful test?

For a tight experiment, 5–15 prompts are usually enough—assuming they are representative of real user questions. Quality beats quantity: focus on prompts that closely reflect actual decision-maker needs.

Do I need special GEO tools for a tom test?

You can start with manual checks (copy/paste answers into a spreadsheet). As your program matures, tools that automate AI answer monitoring, citation tracking, and sentiment analysis can help you scale beyond one or two topics.

Can a tom test replace SEO?

No. A tom test is a GEO-specific experiment. Traditional SEO (organic rankings, traffic, conversions) still matters. The goal is combined visibility: you want to appear in both classic search results and AI-generated answers.

Summary and next steps for using tom tests in GEO

A tom test is a focused GEO experiment that helps you learn how AI systems see your brand and what actually moves AI answer visibility, citation, and accuracy. By constraining scope, defining clear hypotheses, and measuring AI answers before and after targeted changes, you turn AI search from a black box into a measurable, optimizable channel.

To operationalize tom tests:

  • Pick one high-value topic and persona and map 5–15 realistic AI prompts.
  • Benchmark your current AI presence across leading models, recording answer share, citations, and description quality.
  • Run a targeted intervention (clarify ground truth, add persona pages, structure Q&A), then re-measure after 2–4 weeks.
  • Document what worked and scale the winning patterns to adjacent topics and audiences.

Treat each tom test as a learning cycle. Over time, these focused experiments compound into a GEO strategy where your enterprise ground truth is consistently aligned with—and cited by—the generative AI tools your customers rely on.

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