# Mitesh Patel

Mitesh Patel shares marketing advice for ecommerce brands, including ways to improve visibility in traditional and AI-driven search and use AI tools to streamline creative work.

- Profile: https://b2binfluencer.marketing/creators/mitesh-patel/
- LinkedIn: https://www.linkedin.com/in/miteshpatelbusinessgrowth
- LinkedIn followers: 12,458
- Topics: artificial-intelligence, seo, ai-search-visibility
- Based on 9 dated posts, latest 2026-09-15

## Summary

Useful for ecommerce brands connecting AI search visibility with practical marketing content workflows.

- Best for: DTC skincare or ecommerce brand improving AI-assisted product discovery through search and content.
- Not for: Campaigns needing proven reach, guaranteed performance, or ready-made paid-content package.
- Check first: Confirm scope, evidence, deliverables, fees, audience demographics and campaign results.

### Takeaways

- Search visibility now takes three separate efforts: SEO, AEO and GEO.
- AI validation is a step in the buying journey; share of model measures it.
- Claude Skills can be set up once and reused for research, briefs, copy.

## Main ideas

- **Search visibility now takes three separate efforts, not one**: Patel splits search visibility into layers with different jobs. SEO is the familiar work of ranking product pages on Google through keywords, backlinks and optimized descriptions. They keep it as the foundation rather than declaring it obsolete. AEO means appearing in the AI Overview answer at the top of a results page, so a brand can rank third and still be absent from the only part of the page many shoppers read. GEO is the newest layer: getting an assistant such as ChatGPT to recommend a product by name when someone asks for a recommendation instead of a list of links. Each layer gets its own tactic: keep optimizing product pages and site speed for SEO, build FAQ pages, comparison guides and "best X for Y" articles for AEO, and earn mentions in reviews, Reddit threads, niche publications and expert roundups for GEO.
- **AI validation is now a step in the buying journey, and "share of model" is how to measure it**: Patel argues the funnel is no longer a straight line from awareness to search. A validation step now sits in the middle, where buyers use an AI assistant to synthesize and compare options before searching. They call the metric for that step "share of model": a brand missing from the model’s answer is unlikely to win the impressions and clicks that follow. Reviews and customer retention feed the answers, making existing customers material for future recommendations. They present the pattern as holding across ecommerce, media, fintech, pharma and B2B SaaS, and call it the blueprint for 2026 and beyond.
- **AI-assisted creative work should be set up once and reused, not re-explained in every session**: Patel describes a recurring cost for ecommerce and agency teams using AI for creative work: every new conversation starts from zero because brand guidelines, campaign requirements and context have to be pasted in again. They estimate this costs about 20 minutes before any real work begins. The fix is to write the creative process once as a Skill, a simple markdown file the assistant reads automatically when a matching task appears. Skills compose, so research, campaign planning and copy steps trigger each other and build inside one conversation instead of living in disconnected projects. They can also be shared across a team for consistent output.
- **Recurring priority: name the new layer, give each layer an action, then hand over a free playbook**: All three posts follow the same shape. Patel opens with a short claim that current practice is outdated, such as "there are now three ways to show up in search" or "your marketing funnel has a new layer," then names the layers or numbered stages and ties a short list of actions to each one. Headings and arrows make the framework scannable, with everyday examples a brand would recognize. The posts close with a free playbook distributed through a comment keyword, a format that appears in the Claude Skills post. This structure captures how Patel thinks: diagnose the new step, make it nameable and give the reader something to do about it.

## Work to read

- [Three ways to show up in search: SEO, AEO and GEO](https://www.linkedin.com/posts/miteshpatelbusinessgrowth_there-are-now-three-ways-to-show-up-in-search-activity-7486737018467766272-R_XR)
- [The modern marketing funnel and "share of model"](https://www.linkedin.com/posts/miteshpatelbusinessgrowth_your-marketing-funnel-has-a-new-layer-i-activity-7429880041704046592-vqy4)
- [Claude Skills as a reusable creative process for DTC teams](https://www.linkedin.com/posts/miteshpatelbusinessgrowth_%F0%9D%90%82%F0%9D%90%AB%F0%9D%90%9E%F0%9D%90%9A%F0%9D%90%AD%F0%9D%90%A2%F0%9D%90%A7%F0%9D%90%A0-%F0%9D%90%80%F0%9D%90%9D%F0%9D%90%AC-%F0%9D%90%9C%F0%9D%90%A8%F0%9D%90%A9%F0%9D%90%A2%F0%9D%90%9E%F0%9D%90%AC-%F0%9D%90%A8%F0%9D%90%9F-activity-7434129700114915328-mecj)

## Brand relationships

- **Google** (mention)
- **Anthropic** (mention)

## Partnerships

Mitesh Patel's three LinkedIn posts don't identify a sponsorship or brand-partnership format. The closest thing to a commercial interaction is a self-service offer in the Claude Skills post: readers are asked to like the post and comment "SKILLS," after which they receive a free playbook by direct message. Patel notes that readers need to follow the account to receive the message. This shows how inbound requests are handled—through comments, direct messages, and the LinkedIn profile—but it's distribution of their own resource, not a disclosed paid placement, rate card, or sponsored-content format. The posts don't state fees, deliverables, timelines, exclusivity terms, usage rights, or approval steps, and don't link to a media kit or partner policy page. Ask whether they accept sponsorships or paid work, and confirm the budget, scope, turnaround, and terms in writing before committing.

## Alternatives

- Mohsin Nisar M.: Choose Mohsin when the buyer task is a detailed ecommerce product-page and GEO audit. A DTC brand wanting to know how product data, entity attributes, query intent, schema, reviews and internal linking can be made understandable to AI systems gets a closer match here than from Patel's search-visibility posts. Mohsin's posts give a product-page optimization framework and a reputation-engine argument, with category examples such as Amazon, Reddit and DocuSign. It is stronger for a team that needs page-level checks before it briefs a talk or consulting session.
- Nativ Yanko: Choose Nativ when the campaign decision is about DTC creative output and paid-ad testing rather than search visibility. The posts describe a Claude Skills setup for DTC creative teams, covering top-of-funnel scripts, hook generation, briefs pushed into Notion, organic concept translation and static ad copy. They also make a new-customer acquisition argument built around NCPA rather than blended ROAS, with a collagen-brand example. For a campaign need involving creative operations help or a performance-creative session, this is closer to the operational task than Patel's search-focused framework.
- Azhan shafique: Choose Azhan when the buyer task is entity-first diagnosis: a brand's site already has authority, but AI tools describe it inaccurately, and the team needs to fix crawlability, indexing and clear entity information before creating more content. The surgical-instrument example shows a practical sequence: improving who-and-what content around the company, reconnecting official pages, and then checking AI search again. The keyword-research post is also a useful caution for DTC teams tempted to let an AI tool generate a high-volume keyword list without difficulty or intent checks.

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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/