# Nick Jordan

Nick Jordan shares practical ways to explain organic and AI search performance to finance leaders, including comparing organic cost per click with paid search and reporting AI search visibility alongside established channels.

- Profile: https://b2binfluencer.marketing/creators/nick-jordan/
- LinkedIn: https://www.linkedin.com/in/nickfromseattle
- LinkedIn followers: 18,520
- Topics: artificial-intelligence, seo, ai-search-visibility, marketing-measurement
- Based on 10 dated posts, latest 2026-10-07

## Summary

Nick Jordan is most useful for making organic and AI search spend defensible in CFO budget reviews.

- Best for: A B2B SaaS, agency, or SEO/AI visibility platform campaign that needs a marketing lead to defend search spend.
- Not for: Campaigns aimed at finance leaders, CFO leads, performance guarantees, or consumer audiences.
- Check first: Ask for audience composition data and pricing, deliverables, and lead benchmarks before scoping.

### Takeaways

- Report LLM visibility beside PPC and affiliate, compare conversion rates, track branded search and direct traffic.
- Calculate organic cost per click and compare with paid clicks for same high-intent keywords.
- One campaign's non-branded organic plus AI search traffic converted at 8%, same as paid.

## Main ideas

- **Organic and AI search lose the budget review because they lack a single defensible number, not because they underperform**: Jordan's recurring diagnosis is a reporting mismatch. The paid team arrives with one number that reads as a return; the organic lead brings activity metrics and an unfinished AI search plan. They argue that the debate never reaches channel performance because it stalls on whether organic can be defended in the format finance already accepts. The fix: change what goes on the slide rather than argue that the channel is misunderstood.
- **Prove AI search by putting it next to paid channels and showing parity, proxies, and the paid team's language**: Jordan sets out four moves. Stop reporting LLM visibility separately; put it on the same slide, in the same format and metrics, as PPC and affiliate so a CFO can compare it with channels that already have budget. Show conversion-rate parity for a like-for-like comparison. Track proxies: someone who sees a brand recommended in ChatGPT may later search for it or type the URL, showing up as branded search or direct traffic. Use the word ROAS rather than ROI to shorten the budget conversation. A reader's reply raised the objection Jordan was answering: there is no clean way to prove it yet, and a channel you cannot defend does not get budget. Jordan called the objection fair before laying out the method. They also note that AI search attribution is not perfect yet, nor is it supposed to be in a multi-touch world. The claim is not that measurement is solved, but that "we can't prove it" usually means the data has not been placed next to channels that already have buy-in.
- **Run the cost-per-click comparison between paid and organic; nobody else has**: Jordan's most concrete priority is a ten-minute calculation: total organic clicks over twelve months divided by total retainer spend, reported in one spreadsheet column. The argument rests on identical keywords across the two channels and a wide gap in cost. Jordan says the number has never been used because paid cost per click appears on a slide reviewed every Monday, while organic cost per click has never appeared on one. The recommendation aims to shift a retainer or budget conversation from opinion to comparison, not create a new measurement category.
- **Supply the operating method, not more AI search commentary**: Jordan separates public discussion about AI search and organic decline from the work of acting on it. The complaint is not that the discourse is wrong or that the underlying shift is overstated—they call the discussion useful—but that people who never have to defend a channel in a review consume almost all of it. Jordan argues that the loudest takes come from people who have not sat in that room. Their response is to provide reporting formats and benchmarks rather than another take on the landscape.

## Work to read

- [Put AI search data on the same slide as paid and let the numbers argue](https://www.linkedin.com/posts/nickfromseattle_recently-wrote-a-post-about-how-the-loudest-activity-7453098840657809410-myCS)
- [Turn a retainer into a cost-per-click number](https://www.linkedin.com/posts/nickfromseattle_a-client-asked-me-to-justify-the-retainer-activity-7453459465598799873-SXEI)
- [The QBR room is the real audience for AI search talk](https://www.linkedin.com/posts/nickfromseattle_nobody-cares-more-about-your-traffic-than-activity-7447660026485465088-nwKC)
- [The two-line exchange that shows why a traffic metric fails](https://www.linkedin.com/posts/nickfromseattle_the-qbr-slide-your-cfo-actually-wants-to-activity-7450560104933867520-SEro)

## Brand relationships

- **ContentDistribution** (disclosed_partnership)

## Partnerships

Nick Jordan's posts offer no partnership or sponsorship details. The four posts cover defending organic and AI search budgets: justifying a channel in a QBR, benchmarking organic cost per click against paid, and reporting AI search visibility alongside paid and affiliate. None includes a sponsor disclosure, rate card, media kit, audience spec, or partnership terms, and no official inquiry route appears in the posts. Brands interested in working with Jordan should ask directly about availability, audience details, rates, and terms.

## Alternatives

- Josh Henry-Hicks: Josh Henry-Hicks is the better choice if the CFO-facing decision concerns DTC or ecommerce new-customer acquisition, not B2B SaaS organic and AI-search budgeting. The posts offer a media-buying diagnostic not found in Nick Jordan's posts: Josh Henry-Hicks audits around 20 brands a month and reports that 90% have a shrinking share of new customers. The advice is to separate new-customer creative from warm audiences, add credibility proof, move to manual campaigns, aggressively exclude existing and engaged segments, use ViewContent as the conversion event for high-intent traffic, and tighten brand exclusions and break out manual search and shopping when running PMAX. For a campaign where the CFO asks why new-customer revenue is stalling and the response requires channel-structure fixes, this is a closer method match.
- Jonathan Bland: Jonathan Bland is the better choice when the buyer task is a B2B demand-generation budget dispute centered on multi-touch attribution: a CFO or CEO wants a clear answer about which channel produced a deal, and the marketer must explain why last-touch attribution alone is a weak way to judge performance. The posts offer guidance that appears in Nick Jordan's posts only as a caveat: use three to five attribution models simultaneously rather than choosing one; LinkedIn is not Google and paid social is not paid search; audience size matters less than targeting high-fit ICP segments; 99% of people do not click ads, but that does not mean they do not consume them; and simpler campaign structures are easier to manage and communicate. The personal Avoma buying journey gives a concrete, inspectable example of several touchpoints before conversion.
- Aabhas Vijay: Aabhas Vijay is the better choice when the campaign decision turns on how AI search retrieval works and how to earn citations, rather than translating organic performance into a CFO-ready budget case. The posts offer an AEO-specific method: do not start with keywords; test whether ChatGPT searches the web for the category; use 5-10 real buyer questions; inspect query fan-outs; map cited sources; and account for ChatGPT averaging 7.6 fan-out searches per prompt. Aabhas Vijay also cites a 22,881-citation analysis finding that 34.5% of AI citations went to domains with DA below 40, supporting the practical argument that specificity and usefulness can matter alongside authority. For a campaign focused on AI visibility or an AEO audit, this is a closer method match than Nick Jordan's CFO-defense framing.

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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/