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Two marketers walk into the same GEO workshop. They learn the same framework. They leave with the same checklist: improve brand definition consistency, earn third-party mentions, add structured data, target niche prompts. Marketer A works in CRM. Marketer B works in Email Marketing. Six months later, Marketer A has made no measurable progress. Marketer B has moved from position 18 to position 9 in AI recommendations. They followed the same playbook. The difference is the arena they’re playing in — and the arena was decided before either of them started.

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The same checklist, two different realities

To understand why this happens, we need to look at the structural data behind each category. In our previous analysis, Two Filters, One Invisible Wall, we measured how AI recommendation weight is distributed across 10 software categories. Two metrics capture the competitive structure:
  • AI Visibility Rate — the percentage of G2-listed products that AI recommends at all (Filter 1)
  • Gini coefficient — how concentrated recommendation weight is among those brands that are recommended (Filter 2)
Here’s how CRM and Email Marketing compare on every structural dimension:
MetricCRMEmail MarketingGap
G2-listed products1,0164972.0x
AI recommends36421.2x
AI Visibility Rate3.5%8.4%2.4x
Gini coefficient0.7570.5681.3x
Brands for 80% weight7152.1x
% of recommended for 80%19.4%35.7%1.8x
Top 3 DECA share54.0%25.8%2.1x
Market typeMonopoly-likeDistributed—
We use “Monopoly” here to describe AI recommendation concentration — the degree to which a few brands capture most of the recommendation weight — not the actual number of vendors in the commercial market. Every metric points in the same direction: CRM is structurally locked, Email Marketing is structurally open.

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Why each checklist item works differently

The generic GEO checklist isn’t wrong. Each item on it genuinely improves AI visibility signals. The problem is that the same improvement produces different results depending on the competitive structure of the category.

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Brand definition consistency

What it means: Ensuring your brand is described consistently across sources — your website, third-party reviews, analyst reports, directory listings — so that AI can form a clear, stable understanding of what your product does. In CRM (Monopoly, Gini 0.757): HubSpot, Salesforce, and Microsoft Dynamics have been consistently defined across thousands of sources for decades. A challenger improving its brand definition consistency is narrowing a gap that has been accumulating for 20+ years. The signal improvement is real but tiny relative to the incumbent advantage. In Email Marketing (Distributed, Gini 0.568): The top 3 brands hold 25.8% of DECA — not 54%. Brand definitions are less entrenched. Improving brand consistency moves the needle more because there’s less established signal to compete against. Estimated impact difference: In a Monopoly category, brand definition improvements might shift DECA Score by 1–3 points over 6 months. In a Distributed category, the same effort can produce 5–10 points of movement.

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Third-party mentions

What it means: Being mentioned in independent sources — review sites, comparison articles, expert analyses, blog posts — that AI uses as training or retrieval context. In CRM: HubSpot alone has more third-party mentions than many entire categories combined. Adding a few dozen new mentions for a challenger brand barely registers. Each new mention represents a smaller proportional increase in signal share. In Email Marketing: The mention landscape is less concentrated. The same 50 new mentions that are invisible in CRM might be meaningful in Email Marketing.

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Niche prompt targeting

What it means: Optimizing for specific, narrower prompts rather than generic category queries — for example, “best CRM for real estate agents” rather than “best CRM.” In CRM: Niche targeting is the most viable strategy, but competition for niches is also fierce. Even in niches, the incumbent signal advantage persists. In Email Marketing: Niche prompts are abundant and less contested. Many specific use cases have no dominant brand in AI recommendations. A brand can build position without confronting the top brands directly.

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The math behind the feeling

When practitioners say “GEO doesn’t work for us,” they’re often describing a structural phenomenon they can feel but can’t measure. In a category with Gini 0.757 (CRM), the top 19.4% of recommended brands hold 80% of all recommendation weight. Moving from position 25 to position 15 means gaining a fraction of the remaining 20% — a change so small it may not be visible in actual AI responses. In a category with Gini 0.568 (Email Marketing), the top 35.7% hold 80% of weight, but the distribution within the top group is flatter. Moving from position 15 to position 9 means gaining a meaningful share of a more evenly distributed pool.
ScenarioDECA gainCRM contextEmail Marketing context
+5 points5Rank 20 to ~18; still invisibleRank 12 to ~9; begins appearing in primary recommendations
+10 points10Rank 15 to ~12; occasionally appearsRank 9 to ~5; regularly appears in top recommendations
+15 points15Rank 12 to ~8; starts appearing sometimesTop 3; becomes a default recommendation
Practical decision rule: If your category has low AI Visibility Rate and high Gini, prioritize niche prompts and adjacent categories. If low visibility but moderate Gini, prioritize recommendation-pool entry. If higher visibility and low Gini, prioritize broad category ownership before the market concentrates.

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What Marketer A should actually do

This analysis doesn’t mean GEO is pointless in Monopoly categories. It means the strategy must be different. In Monopoly categories (Gini 0.73 or above):
  1. Identify the thinnest niches. Not “best CRM for small business” (heavily contested) but “best CRM for immigration law firms” (sparse signal).
  2. Build signal in adjacent categories. Cross-category signal bleeds into primary category recommendations over time.
  3. Target non-dominant AI platforms. Concentration patterns vary by platform.
  4. Invest in long-form differentiation. Create new comparison dimensions that AI can learn, where incumbents have less established signal.
In Distributed categories (Gini below 0.65):
  1. Execute the standard GEO checklist aggressively. Every signal improvement compounds in a distributed market.
  2. Move fast — the window closes. Distributed categories become more concentrated over time. Early movers gain structural advantages that compound as Gini rises.
  3. Target generic prompts, not just niches. Competing for “best email marketing software” is viable in ways that “best CRM” is not.

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The uncomfortable conclusion

The GEO industry has a framing problem. Most content presents GEO as a universal practice — one set of best practices that works everywhere. The data says otherwise. Across 10 categories, the Gini coefficient ranges from 0.568 to 0.757. The number of brands capturing 80% of recommendation weight ranges from 7 to 59. These are structurally different competitive environments that require structurally different strategies. The first question for any GEO practitioner shouldn’t be “What’s my GEO checklist?” It should be “What’s the concentration structure of my category?”
Key takeaway: Identical GEO strategies produce 2x different outcomes depending on category structure. In Monopoly categories (Gini 0.73 or above), standard GEO checklists have low ROI because 80% of recommendation weight is locked into fewer than 20% of brands. In Distributed categories (Gini below 0.65), the same checklists produce measurable results because the weight distribution has room for movement. Know your category’s Gini before choosing your strategy.

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Methodology

Data source: DecaGEO AI recommendation tracking. Hundreds of recommendation-seeking prompts sent to ChatGPT (GPT-5.4) weekly, US region. Tracking week of May 17, 2026. DECA Score calculation: Normalized score (0–100) based on a brand’s recommendation frequency and prominence relative to others in the same category. Gini coefficient calculation: Standard Gini formula applied to DECA Scores within each category. G2 listing counts: Retrieved from G2.com category pages on May 19, 2026. Impact estimates: DECA Score movement ranges are directional estimates based on current score distributions, not predictions. Limitations: Data reflects one AI platform (ChatGPT) in one region (US). Strategy recommendations are based on structural analysis and may not apply to all competitive situations. The relationship between Gini coefficient and GEO ROI is observational, not causal.

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FAQ

Because AI recommendation weight is not distributed equally across categories. In a Monopoly category (Gini 0.73 or above), 80% of recommendation weight is concentrated in fewer than 20% of recommended brands. Any improvement by a non-dominant brand is competing against a massive existing signal advantage. In a Distributed category (Gini below 0.65), the weight distribution is flatter, so the same signal improvement represents a larger proportional change.
Compare three things: AI Visibility Rate, Gini coefficient, and your current DECA Score. If a category has low visibility and high Gini, it is harder to enter and harder to move once inside. If a category has higher visibility and lower Gini, the same GEO effort is more likely to produce measurable movement. A CRM vendor with strong email or project management features may get faster GEO traction by building signal in those adjacent categories first.
No. The Gini coefficient measures concentration (Filter 2), but the AI Visibility Rate measures entry difficulty (Filter 1). Both filters should be considered together. A category with high AI Visibility Rate and low Gini is structurally easier than one with low visibility and high Gini.
Do not chase generic category prompts. Build a niche prompt map across industries, company sizes, and workflows. Identify prompts where AI gives inconsistent or low-confidence recommendations. Publish and earn third-party signals around those niches. Track niche prompt appearances before expecting movement in generic prompts.
Speed matters. Fix brand definition consistency across your website, review profiles, and documentation. Build comparison and alternative pages for the most common buying prompts. Earn third-party mentions in category-relevant sources. Track generic, niche, and comparison prompts weekly. The window is open but temporary.
No. It means the standard GEO checklist has low ROI when applied to generic category prompts. In Monopoly categories, effective GEO focuses on niche targeting, adjacent categories, and differentiation-based strategies rather than direct competition for primary recommendation slots.
Individual brands cannot change the structural concentration of a category. However, brands can choose which categories and niches to compete in. Category choice is itself a strategic decision.
The exact timeline varies. Current data shows GEO (Gini 0.572) and Email Marketing (Gini 0.568) as least concentrated, but this is a snapshot that will evolve. Categories with heavy GEO investment from a few brands may consolidate faster.

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Sources

  1. DecaGEO AI recommendation tracking data, week of May 17, 2026. ChatGPT (GPT-5.4), US region.
  2. G2.com category listing counts, retrieved May 19, 2026.
  3. DecaGEO, “Two Filters, One Invisible Wall,” May 2026.
  4. DecaGEO, “AI Visibility Rate: Why AI Ignores Most Software,” May 2026.