# Clwyd Probert

Clwyd Probert writes about digital marketing for small businesses, with a focus on improving visibility in AI-powered search and building communities around a brand.

- Profile: https://b2binfluencer.marketing/creators/clwyd-probert/
- LinkedIn: https://www.linkedin.com/in/clwyd-probert
- Topics: artificial-intelligence, seo, answer-engine-optimization, ai-search
- Based on 10 dated posts, latest 2026-10-01

## Summary

Clwyd Probert is most useful for explaining AI search visibility and community-led marketing to small businesses.

- Best for: A B2B software or martech brand targeting small and midsize businesses around AI search visibility.
- Not for: Consumer brands or enterprise vendors needing video, paid partnership terms, guaranteed reach, or measured conversion.
- Check first: Confirm benchmarks, Hampton customer example, audience data, and partnership terms before contacting.

### Takeaways

- Probert offers a five-step AEO playbook: robots.txt, Q&A content, strong answers, structured data, SEO basics.
- Probert cites click-through falling from 28.5% at position 1 to 2.5% at position 10.
- Probert connects visibility to naming a useful methodology, sharing it openly, and building a community.

## Main ideas

- **Optimise for answers, not links**: Probert's central argument is that AI answer engines change the contest. In traditional search, clicks thin out fast down the page, and Google's rankings rewarded established players with backlinks and authority. AI systems such as ChatGPT, Claude, and Perplexity surface only two or three mentions instead, with no page two. Probert treats that as a leveller: AI discovery prioritises the quality and relevance of an answer over accumulated authority, opening room for smaller businesses. The practical instruction is to optimise for answers rather than links.
- **A five-step playbook for AI visibility**: Probert's method is a named sequence of tactics rather than a general call to 'do AEO'. Enable AI discovery by fixing robots.txt so AI crawlers can reach the content; reformat content into question-and-answer style, which AI systems extract; apply a 'deserve to rank' test — if the answer is not the best on the subject, it will not appear; add structured data for pricing, reviews, and inventory; and keep SEO foundations intact because AI systems still lean on search engines. Probert presents the playbook as grounded in Whitehat's analysis of more than 100 campaigns and warns that those who execute will win visibility while others disappear.
- **Urgency built on shared benchmarks**: A recurring habit across the posts is anchoring the argument in a few striking numbers repeated from piece to piece: falling click-through rates, a 20–40% drop in organic search traffic across industries, more than 800 million people using AI platforms weekly, and the 73% robots.txt gap. Probert uses these figures to argue that the shift is already underway rather than looming, and that delay carries a cost. Verify the statistics with Probert before repeating them; the posts do not name their sources.
- **Community and category creation as the growth engine**: The fourth post widens the frame beyond search. Probert argues community is the new competitive advantage, and that HubSpot did not build a £25B company by pushing software but by starting a movement — Inbound Marketing — which it never trademarked. The framework has four moves: identify an inefficient market where something should happen but does not; codify a methodology with a name, principles, and frameworks; open-source the thinking, since sharing earns trust; and foster community rather than only customers. This sits alongside the AEO advice as the demand-side complement to the visibility work.
- **AEO extends SEO rather than replacing it**: Although the headlines push toward answer engines, Probert does not declare search dead. Keeping SEO foundations is one of the five playbook steps, and a separate post is titled around the idea that SEO is evolving. The position is that AI visibility depends on existing search infrastructure — crawlers, structured data, ranking signals — while adding answer-shaped content and testing on top of it. That framing matters for anyone deciding whether AEO work is a replacement programme or an extension of existing SEO effort.

## Work to read

- [From search engines to answer engines: AI discovery as a leveller](https://www.linkedin.com/posts/clwyd-probert_aiseo-marketingstrategy-digitaltransformation-activity-7381611255301718017-ZGYA)
- [The AEO Playbook: 5 Tactical Steps for AI Visibility](https://www.linkedin.com/posts/clwyd-probert_aiseo-marketingstrategy-digitaltransformation-activity-7382337022604095488-bcWX)
- [SEO isn’t dead, it’s evolving: the 2006 analogy for AEO](https://www.linkedin.com/posts/clwyd-probert_seo-isnt-deadits-evolving-activity-7380887965595639808-2Szu)
- [Community is the new competitive advantage](https://www.linkedin.com/posts/clwyd-probert_inboundmarketing-communitystrategy-marketingleadership-activity-7383423802644307968-9Yas)

## Brand relationships

- **Whitehat** (mention)
- **HubSpot** (mention)

## Partnerships

Clwyd Probert's posts do not document any partnership, sponsorship or paid-collaboration format. Across four LinkedIn posts published between 6 and 13 October 2025, they publish educational commentary on answer engine optimisation and community-led marketing. Three posts end with a shortened link to an off-platform resource; none is a sponsorship or partnership enquiry route. The posts include no rate card, deliverable list, disclosure of a paid collaboration or contact channel for partnership enquiries. A brand looking to commission them would need a direct conversation to establish format and terms.

## Alternatives

- Abdul hadi: Choose this alternative when the job is to win an internal argument before publishing tactics. The two posts aim to persuade skeptical decision-makers with numbers. One lays out a direct comparison of conversion rates: 1.8% from Google organic search against a claimed 14.2–16.0% from ChatGPT, 10.5–12.4% from Perplexity and 5.0–16.8% from Claude, with industry breakdowns of 18.7% for B2B SaaS and 21.3% for professional services. It then explains the mechanism in three parts — trust transfer, intent precision, and reduced decision fatigue — and contrasts two-to-three-word Google queries with twelve-word AI queries. The second post summarises published research that trust in an AI system links its recommendation to an actual purchase, and that AI tools reshape the set of brands a buyer considers. For a B2B SaaS company whose budget holder still sees AI search as a novelty, this framing does work Probert's posts do not: it gives the buyer a quantified reason to act.
- Mohsin Nisar M.: Choose this alternative when the campaign is e-commerce or product-page specific, or needs a positioning diagnosis rather than a general AEO playbook. One post argues that the useful question is not “how do I optimize for ChatGPT” but “why would an AI recommend me over my competitors,” and makes the case that AI systems reward clear category positioning, credible social proof, specific outcomes and presence across multiple platforms. It grounds that argument in named examples — merchants winning on detailed product data, forum threads surfacing because real buyers describe real use cases, and a signature-software company owning its category because its terminology is consistent across pages, content, mentions and citations — and cites a projection that LLM-driven traffic will pass traditional organic search by early 2028. The second post is a product-page optimisation framework covering entity-attribute-value structure, query-intent mapping, schema markup, review and user-generated-content strategy, technical checks, variant handling and AI search readiness. If the campaign targets a product-catalogue or e-commerce SaaS problem, this material maps to it more directly than Probert's small-business framing.
- Gabriel Marguglio: Choose this alternative when the buying decision involves CRM data readiness alongside AI visibility, or when the audience is HubSpot-centred. One post separates internal context from external context: internal context is the CRM data, processes, customer history, sales conversations and support tickets that let an AI agent reason and act with human oversight, while external context is what AI systems already understand about a company's products, expertise and reputation. It sets out an evolution from humans searching links, to humans asking questions and evaluating synthesised answers, to agents doing the research, comparison and shortlisting before a human decides — a useful frame for a B2B SaaS campaign about where answer-engine work is heading. The second post argues that the two sides are complementary: AI without context is just automation, and visibility without a system to convert it does not produce growth. It supports this argument with the author's account of an agency journey and a client review of a CRM onboarding. For a company selling into that ecosystem, or one whose AEO blocker is messy customer data, this is the closer match.

---
AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/