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The Recommendation Gap
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- FormatePub
- ISBN978-87-977387-0-2
- EAN9788797738702
- Date de parution29/06/2026
- Protection num.pas de protection
- Infos supplémentairesepub
- ÉditeurIndependently published
Résumé
There's a structure to how AI decides who to recommend and who to eliminate in your category. Not knowing it costs you deals. That's the Recommendation Gap. When a buyer asks ChatGPT, Claude, Gemini, or Perplexity, "who are the best providers of X, " the answer that comes back reflects a decision your brand was never part of. The model reads its training data, fetches what the web says about your category, weighs trust signals, surfaces five to seven names, and shapes the buyer's shortlist before they ever visit your site.
If your brand is not in that list, you're losing deals your dashboard doesn't show you, until the pipeline math catches up months later. This book shows you how to close that gap. You'll learn:- How to decode what AI currently says about your brand and audience: the elimination triggers that cut you, the hesitations that slow buyers down, the criteria they actually weigh- The full prompt sequence to run yourself, included in the appendix, with worked examples- The content-type map: which signal triggers which content (FAQ, methodology page, customer proof, trust story)- The Observe-Decode-Seed loop: a repeatable practice for turning AI's recommendation into the content you ship- A thirty-day starter sequence and a quarterly cadence for running the discipline continuouslyAlso included:- Why SEO controls only 4-7% of why AI cites a page, and what shapes the other 93%- The structural difference between a search engine and a recommendation engine, and why optimising for one no longer means optimising for the otherThe book is grounded in original research across thirteen industries and cross-validated against the most recent data from Profound, Ahrefs, Semrush, iPullRank, and seoClarity.
Recommendation Design is a discipline for B2B brands that need to be in the answer when buyers ask AI. It's the work that closes the gap between what AI recommends about your brand and what you intend to be recommended for. If your buyers are asking AI before they ask anyone else, and they are, the brand that closes the gap first wins the category.
If your brand is not in that list, you're losing deals your dashboard doesn't show you, until the pipeline math catches up months later. This book shows you how to close that gap. You'll learn:- How to decode what AI currently says about your brand and audience: the elimination triggers that cut you, the hesitations that slow buyers down, the criteria they actually weigh- The full prompt sequence to run yourself, included in the appendix, with worked examples- The content-type map: which signal triggers which content (FAQ, methodology page, customer proof, trust story)- The Observe-Decode-Seed loop: a repeatable practice for turning AI's recommendation into the content you ship- A thirty-day starter sequence and a quarterly cadence for running the discipline continuouslyAlso included:- Why SEO controls only 4-7% of why AI cites a page, and what shapes the other 93%- The structural difference between a search engine and a recommendation engine, and why optimising for one no longer means optimising for the otherThe book is grounded in original research across thirteen industries and cross-validated against the most recent data from Profound, Ahrefs, Semrush, iPullRank, and seoClarity.
Recommendation Design is a discipline for B2B brands that need to be in the answer when buyers ask AI. It's the work that closes the gap between what AI recommends about your brand and what you intend to be recommended for. If your buyers are asking AI before they ask anyone else, and they are, the brand that closes the gap first wins the category.