Day 128: Before You Optimise a Category, Check Whether Buyers Use It

Most Generative Engine Optimization (GEO) programs start with an ambitious list of category terms.

The marketing team identifies the category label they want to own, benchmarks their visibility across answer engines like ChatGPT Search and Google AI Overviews, and builds an optimization roadmap to secure top placement, frequent citations, and authoritative recommendations.

The default assumption is straightforward: if generative answer engines synthesize your company as a leading provider for a category phrase, market authority and buyer acquisition will follow.

But there is a silent, expensive trap in this approach: winning visibility for a category label that serious buyers never actually use when making a purchasing decision.

Generative answer engines excel at indexing and summarizing vendor consensus. If five software companies and three industry blogs publish whitepapers defining a newly minted category, answer engines can easily explain what the phrase means and list the suppliers associated with it. To an executive inspecting an answer-engine output grid, that result looks like category leadership.

In reality, it may be nothing more than an echo chamber of supplier marketing. Visibility inside a vendor-created vacuum does not generate buyer demand. It merely confirms that suppliers agree on their own terminology.

For CMOs, Marketing Directors, and founders, the first strategic question before scaling content, monitoring, or campaign investment is not "How do we win presence for this category term?" It is: "Does this category label belong to a real buyer conversation?"

The Category-Formation Trap in Generative Search

To understand why supplier-created terms mislead marketing teams, it is necessary to examine how answer-led surfaces process category vocabulary.

Generative answer engines digest public web content, documentation, press releases, and comparison articles. When presented with a query containing a specific category label, the engine retrieves relevant source material and structures a response based on the relationships established in those documents.

If a vendor invents a phrase—for example, "Continuous Revenue Velocity Orchestration"—and publishes content around it, answer engines will mirror that structure when asked about "Continuous Revenue Velocity Orchestration." They will define the concept using the vendor's language and cite the vendor as a pioneer.

That output creates a dangerous illusion of commercial traction. The marketing team sees clear citations, high answer relevance, and prominent brand positioning. They report a successful GEO campaign to leadership.

In a common version of this failure mode, when an enterprise buyer sits down to solve an operational problem, they may not use supplier-invented jargon at all. Rather than searching or prompting for "Continuous Revenue Velocity Orchestration," they are far more likely to ask how to reduce sales cycle friction, why Q3 pipeline conversion dropped, or how to automate CRM data reconciliation between sales and finance teams.

Because the vendor concentrated their optimization budget on the supplier-invented category phrase rather than the buyer's operational problem, the company risks remaining invisible during actual purchasing evaluations. In typical evaluation scenarios, answer engines provide thorough answers to the buyer's operational questions using competitors who anchored their public presence in established buyer language.

Winning a category phrase that buyers do not use is the digital equivalent of buying a billboard on a road no one drives on.

The Three-State Category-Formation Diagnostic

Before committing budget to category optimization, marketing leadership should evaluate target vocabulary through a three-state category-formation diagnostic. This diagnostic classifies terminology based on where it originates and how buyers interact with it.

1. Buyer-Native Vocabulary

Buyer-native vocabulary consists of terms, problem definitions, and category labels that buyers already use fluently in internal discussions, RFPs, budget line items, and search prompts.

• Characteristics: These terms reflect established industry norms or widely recognized operational problems. Buyers use them without needing a definition. Examples in enterprise software include phrases like "identity and access management," "SOC 2 compliance automation," or "contract lifecycle management."
• Generative Pattern: In observed search and answer contexts under recorded queries, engines often generate rich, competitive outputs containing specific evaluation criteria, implementation trade-offs, and established market alternatives.
• Commercial Reality: High buyer search and query volume typically exists, but competition is intense. The vocabulary maps directly to established buyer evaluations.

2. Buyer-Adjacent Vocabulary

Buyer-adjacent vocabulary refers to situations where buyers face an urgent, funded operational problem, but describe it using outcome or pain language rather than the supplier's preferred category label.

• Characteristics: The buyer's problem is real and urgent, but the vendor's category label is newer than the problem itself. For instance, a buyer might ask "how to prevent API rate-limit bottlenecks in microservices" rather than searching for "automated API traffic governance platforms."
• Generative Pattern: In observed answer contexts under recorded queries, engines frequently provide detailed technical or operational guidance for the problem, but only mention the vendor's category label if public source material explicitly connects the operational pain to the category term.
• Commercial Reality: Strong latent problem awareness exists, but the vendor must build a narrative bridge between the buyer's familiar problem language and the vendor's solution category.

3. Supplier-Invented Vocabulary

Supplier-invented vocabulary encompasses category labels, acronyms, and marketing frameworks created entirely by vendors, agencies, or PR campaigns that have not gained real traction among buyers.

• Characteristics: The terminology exists almost exclusively on vendor websites, sponsored whitepapers, and press releases. Buyers do not use the phrase in RFPs, team meetings, or unprompted search queries.
• Generative Pattern: In recorded tests, answer engines can define the term when explicitly queried, but their responses draw almost entirely from vendor self-descriptions. Under broader problem queries, the phrase rarely appears in organic, non-vendor source contexts or independent comparisons.
• Commercial Reality: Zero established buyer query intent attached to the phrase in recorded conditions. Winning visibility for the term yields high internal satisfaction but negligible commercial pipeline.

Mapping Diagnostic States to Commercial Choices

Classifying category vocabulary into these three states gives CMOs and founders a clear framework for resource allocation. Instead of applying a uniform "optimise for visibility" strategy to every term, leadership can select the appropriate commercial choice for each vocabulary state.

Choice A: Compete (For Buyer-Native Vocabulary)

When a category label is buyer-native, the commercial objective is direct competitive differentiation.

Because buyers already use the term to evaluate options, GEO efforts must focus on defining precise capability boundaries, clear adoption requirements, and distinct value propositions. The goal is not to explain what the category is, but why your specific approach is the safest, most effective choice for a defined buyer context.

• Resource Focus: Comparative positioning, detailed integration documentation, explicit trade-off explanations, and transparent technical specifications.

Choice B: Bridge (For Buyer-Adjacent Vocabulary)

When the vocabulary is buyer-adjacent, the commercial objective is problem-first entry.

Attempting to force buyers to search for your new category name before they understand its connection to their immediate pain is inefficient. Marketing programs should anchor public content and answer-engine presence in the buyer's operational language first. Once the answer engine addresses the buyer's specific problem, the content introduces the category framework as the structured, permanent solution.

• Resource Focus: Operational problem-solving guides, root-cause teardowns, and clear conceptual bridges connecting daily pain points to your category architecture.

Choice C: Educate Cautiously (For Strategic Supplier-Invented Vocabulary)

If your company is deliberately creating a new product category, supplier-invented vocabulary is sometimes unavoidable. However, leadership must treat this as a long-term category creation effort, not an immediate lead-generation mechanism.

Do not mistake answer-engine presence for market validation. If you choose to educate the market on a supplier-invented term, budget for sustained, multi-channel category education across industry publications, executive events, and analyst relations. Recognize that answer engines will only reflect category formation after buyers begin using the phrase externally.

• Resource Focus: Foundational category manifestos, executive thought leadership, and external co-marketing with early-adopter customers who validate the new terminology.

Choice D: Hold or Stop Funding (For Unvalidated Supplier-Invented Vocabulary)

If a target term was invented internally or suggested by an agency, but shows no signs of buyer adoption or adjacent problem resonance, the correct decision is to stop funding it.

Continuing to spend budget on content creation, answer-engine tracking, and campaign optimization for a ghost category drains resources that should be directed toward buyer-native or buyer-adjacent opportunities.

• Resource Focus: Reallocate budget and technical resources to terms that align with active buyer purchasing behavior.

What Answer Engines Expose—and What They Cannot Prove

Using answer-engine observations to inform category strategy requires strict epistemic discipline.

Answer-led and search-led surfaces provide valuable visibility into how public information is structured, how concepts are linked, and what language is used in public comparisons under recorded conditions. They serve as an effective diagnostic tool for identifying whether a category phrase has broader public distribution or remains confined to vendor sites.

However, answer-engine outputs cannot prove buyer demand, market size, purchasing intent, market share, or conversion causality.

A vendor who dominates the answer-engine responses for a niche, vendor-created phrase cannot claim they have captured market share. Conversely, a vendor who appears alongside established competitors in buyer-native queries has evidence that their positioning is legible within an established buyer vocabulary set.

Preserving Search System Realities

When evaluating and optimizing for search-led and answer-led surfaces, marketing leaders must also maintain realistic expectations regarding underlying search infrastructure.

Google's AI features, such as AI Overviews, draw directly from core Search ranking, indexing, and quality systems. Securing sustainable visibility across these surfaces depends on foundational web quality, authoritative source material, and clear user value.

There are no silver-bullet shortcuts. Google does not require specialized llms.txt files, proprietary AI metadata tags, arbitrary content chunking, or hyper-focused schema hacks for its AI features to index and present content. Modern search architectures evaluate content through comprehensive quality and relevance models built on core Search infrastructure.

A Strategic Action Plan for Marketing Leadership

To ensure your GEO strategy drives commercial value rather than vanity metrics, execute this four-step category audit:

1. Inventory Your Target Category Terms: List every category phrase, product descriptor, and capability label currently included in your content strategy, SEO tracking, and GEO monitoring tools.
2. Apply the Three-State Diagnostic: Audit public question patterns and buyer interaction data to classify each term as Buyer-Native, Buyer-Adjacent, or Supplier-Invented.
3. Reallocate Marketing Capital:
   • Compete aggressively on Buyer-Native terms by sharpening differentiation and capability boundaries.
   • Bridge Buyer-Adjacent terms by creating problem-first content that connects operational pain to your solution.
   • Re-evaluate Supplier-Invented terms. Either fund explicit, long-term category education or stop funding terms that lack buyer resonance.
4. Establish Quarterly Review Triggers: Category vocabulary is dynamic. Review your category classification quarterly to detect when buyer-adjacent terms shift into buyer-native language, or when supplier-invented terms gain real market traction.

Category visibility is only valuable when it connects to an active buying conversation. Before you spend another quarter optimizing for a category label, verify that you have evidence buyers use the label, or that the label bridges clearly from problem language they already use.