# Bennett Cohen Bennett Cohen shares practical advice for B2B SaaS marketing, especially SEO and AI search. Cohen's posts cover finding attainable, high-intent keywords, answering the smaller searches behind AI-generated answers, and measuring whether AI search brings in customers. Cohen also shares startup marketing roles and lessons from Cohen's company’s customer pipeline. - Profile: https://b2binfluencer.marketing/creators/bennett-cohen/ - LinkedIn: https://www.linkedin.com/in/bennett-cohen - LinkedIn followers: 11,217 - Topics: artificial-intelligence, seo - Based on 12 dated posts, latest 2026-10-07 ## Summary Practical SEO and AI search advice for B2B SaaS growth teams. - Best for: B2B SaaS teams seeking concrete SEO and AI search content. - Not for: Enterprise campaigns needing guaranteed impressions, demographics, or procurement-focused messaging. - Check first: Ask for recent performance, audience demographics, and named client results. ### Takeaways - A specific Shopify keyword reached number three in two weeks and drove 21% of demo requests. - Answer smaller fan-out queries behind AI answers, then measure referral, branded, direct, self-reported traffic. - Most of their last ten customers came through LinkedIn content, referrals, and an investor. ## Main ideas - **Choose specific search terms you can win, not just the biggest category phrase**: They argue that a company can waste months pursuing a broad term dominated by much stronger sites. They recommend starting with narrower buyer needs, comparison searches, and the language customers use. The goal is to connect realistic ranking opportunities to sales, rather than treat a category ranking as the finish line. - **Look for the smaller searches behind AI-generated answers**: They call these smaller searches “fan-out queries”: specific questions an AI system may search before composing an answer. Their advice is to address those questions, not rely on a single broad keyword, because appearing for them can lead to citations in AI answers. - **Measure AI search with several signals, not a visibility score alone**: They say there is no simple measure of AI-search success. They recommend combining referral clicks, changes in direct and branded traffic, self-reported attribution, and manual checks. How often a brand appears matters less than the intent and context of those appearances. - **Use customer-source data to see whether content is bringing people in**: They share a small CRM review to show how to examine acquisition channels, while leaving the scaling question open. The post shows their own company’s snapshot, not a general rule for how B2B companies acquire customers. - **Share practical signals from the startup marketing job market**: A separate post lists open marketing and growth roles, including the companies and responsibilities. It offers a view of hiring activity rather than a marketing method or evidence about search performance. ## Work to read - [A founder's failed 'CRM software' keyword chase—and what to target instead](https://www.linkedin.com/posts/bennett-cohen_a-founder-came-to-me-last-month-trying-to-activity-7413942348730114049-145v) - [Fan-out queries: the smaller searches behind AI answers](https://www.linkedin.com/posts/bennett-cohen_if-youre-starting-with-geo-ai-seo-optimize-activity-7452022141857980416-LQQI) - [How to measure whether AI search work is paying off](https://www.linkedin.com/posts/bennett-cohen_i-get-asked-almost-every-day-how-do-you-activity-7439322085220462592-U5yI) - [Where the last 10 customers came from](https://www.linkedin.com/posts/bennett-cohen_i-just-went-through-our-crm-this-is-where-activity-7407829192391802880-xUQ0) - [Marketing and growth roles at AI and SaaS startups](https://www.linkedin.com/posts/bennett-cohen_some-world-class-startups-hiring-for-marketing-activity-7443318709227708416-UhIU) ## Brand relationships - **Maintouch** (employment) ## Partnerships No partnership formats, sponsorship policies, rate cards, or partnership inquiry routes for Bennett Cohen appear in the posts here. The five posts read for this section are marketing writing about SEO and AI search, plus one hiring roundup. The closest thing to a commercial offering is Maintouch, the SEO and AI search automation product Cohen describes as their own company: one post lists a growth role at Maintouch alongside other startups and writes "us!", and two others say "that's how we've engineered Maintouch" and describe connecting Maintouch to a CRM to track AI-attributed leads. That is a product Cohen promotes, not a documented creator-partnership menu. Because no sponsorship offer is described in the posts here, this section carries no format entries. This is not a claim that Bennett Cohen never accepts sponsorships or paid partnerships; it means none appears in the posts here. A brand wanting to work with Cohen would need to establish terms directly, and the posts here do not show how that conversation starts. ## Alternatives - Aabhas Vijay: Aabhas Vijay is the better choice when the growth team's first question is whether AI search is winnable in their category, not which keywords to target. The posts offer a diagnostic sequence: pick 5–10 real buyer questions, run them in ChatGPT, watch whether web search fires and what sources get pulled, look for fan-out queries, and check whether the category triggers retrieval before rewriting pages. One post reports that ChatGPT averages 7.6 fan-out searches per prompt and cites an analysis of 22,881 AI citations in which 34.5% went to domains with DA below 40. If the B2B SaaS team needs a talk or workshop on mapping a retrieval ecosystem and reverse-engineering why answers form the way they do, this is a closer fit than Bennett Cohen's keyword-triage and measurement sprint. - Azhan Shafique: Azhan Shafique is the better choice when the company already has authority — backlinks, certifications, history — but AI tools misdescribe or fail to understand the business. One post describes a surgical-instrument manufacturer with strong backlinks and certifications where ChatGPT and Google AI surfaced inaccurate information; the fix was improving the information layer around who the company is, what it makes, its location, product categories, expertise, and entity information, then re-indexing key pages. A second post addresses the same decision from the other side: a prospect using Claude for keyword research got high-volume terms with no difficulty, intent, or ranking check. The argument is that AI speeds up research, but judgment still has to filter for winnable, converting terms. If the B2B SaaS company's need is an entity-understanding problem or leadership treating an AI keyword list as a strategy, this is a sharper match. - Mohsin Nisar M.: Mohsin Nisar M. is the better choice when the growth team's blocker is positioning, not publishing throughput. The posts argue that the right question is not how to optimize for ChatGPT but why an AI would recommend the brand over competitors. Specific positioning, consistent category terminology, credible social proof with metrics and timelines, and presence across multiple platforms are what the posts say AI answers reward. Docusign is cited as owning electronic signature through terminology consistency, while the complaint is that "full-service digital solutions" language gives AI nothing to retrieve. A second post applies the same logic to ecommerce product pages with an entity-attribute-value framework, query-intent mapping, schema, reviews, and AI-search readiness. For a B2B SaaS company whose AI answers are generic or whose message is undifferentiated, this is closer to the diagnosis than Bennett Cohen's post-level SEO and AI search tactics. --- AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/