# Aabhas Vijay

Aabhas Vijay shares SEO and answer engine optimization (AEO) advice, including how businesses can improve their visibility in AI search and measure the results.

- Profile: https://b2binfluencer.marketing/creators/aabhas-vijay/
- LinkedIn: https://www.linkedin.com/in/aabhasvijayseoaeo
- LinkedIn followers: 5,658
- YouTube: https://www.youtube.com/channel/UCONnAoZwqpeY33s4Oyu5Vcg
- Topics: artificial-intelligence, ecommerce, seo, ai-search-visibility, answer-engine-optimization
- Based on 12 dated posts, latest 2026-10-07

## Summary

Aabhas Vijay is most useful for hands-on AEO strategy sessions that test buyer questions and measure AI citations.

- Best for: B2B SaaS marketing teams weighing an AEO talk or workshop on AI search visibility.
- Not for: Brands seeking a sponsored LinkedIn series with committed impressions, audience demographics, or lead targets.
- Check first: Confirm whether they have audience demographics or recent workshop results for an AEO talk.

### Takeaways

- Start AEO with buyer decisions and questions AI systems research, not just target keywords.
- Test five to ten real buyer questions in ChatGPT and check whether web search fires.
- Track AI citations, referral traffic, and conversions as three separate stages.

## Main ideas

- **Plan AEO from buyer decisions, not keywords**: This is the argument the other posts build on. Aabhas Vijay says the keyword is a useful organizing unit for traditional SEO but a misleading starting point for answer engine optimization: AI systems do not research topics the way keyword tools frame them. A page aimed at one phrase can miss the surrounding material a model needs to assemble an answer. The proposed path is decision, then questions, research queries, evidence and content—the reverse of the familiar keyword-to-article route. Vijay draws out two consequences: keyword targeting tends to produce pages that exist because a tool suggested them, and strong keyword rankings do not carry over into AI visibility, since the same prompt can return different results depending on the model, context and retrieval process.
- **Check whether AI search retrieves in your category at all**: Before any content spend, Vijay runs a retrieval check: pick five to ten real buyer questions rather than category terms, run them in ChatGPT, and observe whether a live web search fires and what the model consults afterwards—comparison and listicle pages, industry publications, competitors or brand websites. The point is to map a niche's retrieval ecosystem, not just check whether a brand appears. If a category never triggers web search, rewriting pages, adding FAQs and changing headings will not earn citations; the answers are coming from training data. Vijay also expects a single buyer question to fan out into several searches behind the scenes, treating the resulting queries as multiple opportunities rather than one.
- **Keep AI citation metrics separate from traffic and conversions**: Vijay's response to a Microsoft Clarity update is a reporting argument, not a content one. Clarity now splits citations into branded and non-branded buckets: branded means the AI looked up the company by name, while non-branded means it found the company while researching a broader topic. The two measure different things: brand pull and category authority. A citation counts as branded even when the user never typed the brand name, because the AI still searched for specific product names behind the scenes. The update also added Share of Authority, the percentage of citations a domain earns against competitors in queries where it already appears. Vijay's directives follow: keep the two citation types separate, review by topic and page rather than by totals, and treat citations, traffic and conversions as three distinct stages, since conversions still live in the CRM.
- **Treat structured data as the conversion lever, not just a visibility lever**: Using Shopify's Q2 data, Vijay argues that a small AI referral share can carry a disproportionate amount of revenue, and that winning it requires catalogue work. AI collapses discovery and consideration into one conversation, then drops a high-intent buyer onto a product page. That page needs to lead with specifications, comparisons and trust signals for visitors who have already done part of the deciding. Vijay recommends mining real shopper language from AI queries, Search Console and on-site search; converting it into product attributes such as use case, compatibility, fit, materials and best-for scenarios; rebuilding high-intent product pages; collecting and marking up reviews; and measuring AI and organic side by side by conversion and average order value. The same enrichment serves both channels, so the choice is not AI or organic.
- **Meet Google's AI-content guidance with review, not speed**: Vijay summarises Google's revised documentation dated 1 October 2026 as a publishing constraint on AI-assisted content. The requirement to manually fact-check and review AI-generated material before publication now extends to metadata—including title tags, meta descriptions, structured data and image alt text—not just the visible body. Vijay links the change to the September 2026 spam update and its emphasis on human effort, originality and people-first quality. The post recommends validating markup against Google's guidelines, noting that Google Merchant Center requires specific IPTC metadata for ecommerce, and adding experiences data to pages. Review and validation belong between drafting and publishing, and the same rules apply to parts of a page readers do not see.

## Work to read

- [Why starting with keywords can wreck an AEO strategy](https://www.linkedin.com/posts/aabhasvijayseoaeo_how-to-build-your-aeo-strategy-activity-7513571971100577794-Y_co)
- [How to test whether ChatGPT actually searches the web for your category](https://www.linkedin.com/posts/aabhasvijayseoaeo_whenever-i-join-my-consultation-calls-the-activity-7503430678986821632-hO7v)
- [Reading Microsoft Clarity's August 3 AI visibility update](https://www.linkedin.com/posts/aabhasvijayseoaeo_the-august-3-update-by-microsoft-clarity-activity-7493996096247971840-PyqH)
- [What Google's October 2026 AI-content guidance changes for publishers](https://www.linkedin.com/posts/aabhasvijayseoaeo_things-you-need-to-know-about-google-searchs-activity-7512092604440715264-_mhq)
- [Using Shopify's Q2 AI referral data to argue for AEO investment](https://www.linkedin.com/posts/aabhasvijayseoaeo_shopify-just-published-data-that-should-make-activity-7496532843339161600-x5_b)

## Partnerships

Aabhas Vijay’s posts show a practitioner working directly with clients, describing what they check on consultation calls before building an answer engine optimization (AEO) plan, and inviting readers to follow their weekly SEO updates. They do not document a sponsorship or media-partnership offer. No rate card, media kit, audience specification, sponsored-format list, editorial calendar, or official partnership inquiry route appears in the posts; the only verified contact point is their LinkedIn profile. Buyers should ask directly about availability, formats, and pricing rather than assume a standard package exists.

## Alternatives

- Bennett Cohen: Choose Bennett Cohen when the team's real blocker is channel selection and proof that AI work is paying off, rather than a structured AEO exercise. One post walks through a founder who spent six months on "CRM software" with about 25 backlinks against a top result with more than a million, then shows the alternative: niche buyer-intent terms such as "CRM for solar installers" at far lower difficulty, competitor-comparison searches ("[Competitor] alternatives," "[Tool A] vs [Tool B]"), and exact phrases mined from customer conversations. The second post answers the measurement question a SaaS marketing lead is likely to ask next, triangulating AI-referred traffic (citation clicks carry utm_source, trackable in analytics), lifts in direct and branded traffic from people who saw the brand in a model and searched later, a self-reported "how did you hear about us?" field, and periodic manual spot checks. That combination suits a team seeking quick revenue-linked targets and a lightweight way to show whether AI search effort is working, without commissioning a workshop.
- Mohsin Nisar M.: Choose Mohsin Nisar M. when a team's positioning need is about strategy rather than process. The stronger post reframes the question from "how do I optimize for ChatGPT?" to "why would an AI recommend me over my competitors?" and argues that the differentiator is solution specificity: not "best coffee machine" but "best coffee machines under $300 with a grinder that fits a small kitchen," with Amazon's product data, Reddit threads, and consistent category terminology cited as what actually surfaces. The follow-on post is a product-page optimization framework for the move from strategy to pages: entity-attribute-value structure, query-intent mapping across transactional, commercial, informational and navigational searches, schema markup, image optimization, reviews and user-generated content, technical checklists, product-variant handling without duplicate content, and a discontinued-product strategy to preserve link equity, all explicitly including AI search and conversational commerce readiness. For a SaaS team with technically thin pages or inconsistent category language, this maps more directly to the fix than a general AEO diagnostic.
- Azhan Shafique: Choose Azhan Shafique when AI describes the business wrongly or the team is tempted to let an AI tool produce its keyword list. One post is a diagnostic case: a surgical instrument manufacturer with strong backlinks, ISO certifications and regulatory references that ChatGPT and Google AI still misunderstood. The problem was resolved not by building more links but by fixing the information layer—clear statements of who the company is, what it manufactures, its location, product categories, certifications, expertise and history—then re-indexing important pages and seeing accurate identification appear in AI answers. The conclusion, that the goal is entity understanding rather than mentions, applies directly to a SaaS brand that AI answers confuse or ignore. The second post addresses a real internal failure mode: a prospect who thought Claude replaced an SEO agency and received high-volume keywords with no difficulty or intent data. Shafique argues for low-competition terms, intent checked one by one, and topically organized pages, with AI as speed and human judgment as the strategy layer. That framing can help convince a skeptical internal stakeholder.

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