# Adam Riemer

Adam Riemer writes and advises about SEO, affiliate marketing, and ecommerce, including how AI affects product discovery, content, and conversions.

- Profile: https://b2binfluencer.marketing/creators/adam-riemer/
- LinkedIn: https://www.linkedin.com/in/adam-riemer-9623732
- LinkedIn followers: 5,220
- Median of last 8 posts: 14 reactions, 2 comments (checked 2026-11-02)
- Topics: artificial-intelligence, ecommerce, seo, ai-search-visibility, affiliate-marketing, ecommerce-conversion
- Based on 9 dated posts, latest 2026-10-03

## Summary

Adam Riemer is most useful for practical advice on SEO, affiliate marketing, ecommerce, and AI search changes.

- Best for: A B2B ecommerce software brand seeking co-created content on product discovery and conversion.
- Not for: Cybersecurity vendors targeting IT directors, or campaigns needing video and webinar hosting.
- Check first: Confirm collaboration terms, rates, and format directly with Adam Riemer.

### Takeaways

- A traffic drop is usually deindexing, not a site-wide penalty.
- Product page copy and user experience still decide the sale when AI reads feeds.
- Use AI to screen affiliate fraud, but keep a human in the loop.

## Main ideas

- **A traffic drop is usually deindexing, not a site-wide penalty**: Riemer argues that when traffic falls after a Google spam update, the cause is often that specific pages were removed from the index, not that the whole site was devalued. The pages that disappear tend to be ones written for search engines rather than readers: keyword-focused pages, programmatic SEO output, and AI-written content at scale. Because Search Console still shows data for those URLs, a deindexing problem can look like a ranking problem. His method is to sample the URLs that lost traffic, run a site: search for each, and if the page no longer appears, that is the answer. Then he spiders the site for shared traces, such as recurring AI phrasing in headers or programmatic templates, and sorts every page into four choices: delete it if it does not help a user, combine fragments into a fuller resource written for people, refresh a genuinely useful page with detail only someone with direct experience would know, or leave a good page alone. He calls this one of the easiest recoveries available, though time-consuming.
- **Product page copy and user experience still decide the sale, even when AI reads your feeds**: Answering a reader who asked whether updating product and service pages still matters now that AI systems use product feeds and schema, Riemer says yes, emphatically. Structured data and feeds do not cover everything that makes a product page convert; the copy and the experience still shape the purchase. He also warns against companies going AI-first while treating marketing basics — PPC, SEO, email, SMS, content marketing — as optional.
- **In AI search, pay for repeated exposure from a trusted creator, not a single placement**: Riemer argues that paying for a placement or one specific article does not work. LLMs customize answers for each individual user while search engines show the same results to the masses, so value comes from repeat exposure with an author or creator your buyers already trust and engage with. A one-off or two-off mention reads as pay-to-play and goes stale quickly. Repeated, relevant references signal real trust and belief in the product, and can also be reused in advertising to convert more traffic into customers.
- **Companies should actively correct what AI models say about them**: Riemer frames the problem of ChatGPT, Claude, and similar systems showing incorrect or outdated information about products, services, or personal brands, and describes three things he is trying as a way to keep those outputs current: reduce support questions and costs, make sure people asking know whether the company is or is not a solution for them, and surface the company for a specific user type. He extends the stakes beyond marketing — investors may look for company leadership through AI answers, and a job seeker whose AI record is wrong could lose a chance with a recruiter.
- **Use AI to screen affiliate fraud, but keep a human in the loop**: Riemer wrote about three ways to use AI to detect and prevent affiliate fraud, prompted by a reader question about low- and no-value partners. He shares what a prompt needs to get started and a sample prompt, but is explicit about the limits: just because a system like ChatGPT or Claude says something is bad does not make it true, misfires happen, and manual review stays part of the process.
- **Affiliate management is a career with defined tracks, and it is how his own career grew**: Asked repeatedly by recruiters and HR professionals what the career path looks like for an affiliate manager, Riemer maps three tracks: corporate desk jockey, specialized skill sets up to VP level, and executive leadership, plus a fourth that is not a standard career track — entrepreneurship, whether starting an affiliate company or a marketing agency.
- **Your best sellers are not necessarily the products you sell the most of**: Riemer starts a conversion argument from a merchandising problem: increasing average order value, driving more conversions, and moving product that expires. The first step, in his framing, is knowing what your best sellers actually are — and they may not be the items you sell in the greatest volume.

## Work to read

- [When a traffic drop is deindexing, not devaluation](https://www.linkedin.com/posts/adam-riemer-9623732_seo-aio-geo-activity-7515709955384004609-fQLn)
- [Product page copy still matters when AI reads your feeds](https://www.linkedin.com/posts/adam-riemer-9623732_does-my-product-page-copy-still-matter-if-activity-7507778457154744320-V7sA)
- [Why paying for one AI-era placement rarely works](https://www.linkedin.com/posts/adam-riemer-9623732_affiliate-marketing-in-the-age-of-ai-search-activity-7517863298579554304-br52)
- [Using AI to spot affiliate fraud, with a human in the loop](https://www.linkedin.com/posts/adam-riemer-9623732_ai-chatgpt-claude-activity-7515428727221243905-Mc5r)
- [When AI gets your product facts wrong](https://www.linkedin.com/posts/adam-riemer-9623732_ai-llms-chatgpt-activity-7510640409249742849-sRRr)
- [Finding your real best sellers before Q4](https://www.linkedin.com/posts/adam-riemer-9623732_ecommerce-shopify-cro-activity-7505725711802888192-66tK)
- [Updating the title tag guide for AI-era search](https://www.linkedin.com/posts/adam-riemer-9623732_title-tags-and-seo-everything-you-need-to-activity-7508448392730251264-YB3W)

## Brand relationships

- **Search Engine Journal** (disclosed_partnership)
- **Search Engine Land** (disclosed_partnership)

## Partnerships

No partnership or sponsorship offer is documented in the material reviewed for Adam Riemer. His posts address paid placements from the brand's side of the table: he argues that paying for a single placement or article does not work, and that brands should pay for repeat exposure only when the specific author or creator can influence the audience they sell to. Those posts describe how he thinks buyers should spend, not what he sells. The closest documented collaboration is a podcast interview he recorded with Jon Clark and Joe DeVita after a break from speaking, where he was the invited guest rather than the seller of a placement. He also writes regular Ask an SEO columns for Search Engine Journal and updated Search Engine Land's title-tag guide — editorial assignments, not sponsored formats. His other reviewed posts cover affiliate program payment questions, AI-assisted detection of low-value or fraudulent affiliates, affiliate manager career paths, and ecommerce conversion work. Because no official inquiry route, media kit, or published terms were found, this section sets no inquiry link and leaves formats empty rather than presenting inferred offers as available. A buyer would need to define any arrangement directly with Adam Riemer; nothing in the reviewed material indicates a published menu to choose from.

## Alternatives

- Rasmi Ranjan Nayak: Choose Nayak when the campaign's hard problem is measurement: proving which on-site searches and product pages actually produce revenue. The reviewed posts show work building a GA4 BigQuery attribution model that maps search terms to search sessions, orders, and revenue, and a GA4 ecommerce implementation that separates product detail pages from listing pages so the right events fire. For a software company whose buyers need to act on discovery and conversion data, that sits closer to the buyer's daily problem than a general SEO column.
- Brian Aldrich: Choose Aldrich when the campaign centres on paid search, Google Shopping, and product feeds. The reviewed post analyses Google's AI-driven shopping updates, including agentic checkout, selectable product comparisons, and local inventory feeds, and the other post is a hiring notice for a part-time paid search specialist working in Google Ads, Microsoft Advertising, Shopping, and data feeds. If the software company's buyers care most about Shopping performance and feed quality, this is a nearer match than affiliate or SEO recovery content.
- Christopher Reid: Choose Reid when the campaign needs hands-on AI adoption training for retail teams alongside the AI shopping angle. The reviewed posts explain Shopify's Canvas tool and ChatGPT's Try On feature in plain language and close with an offer of a one- or two-hour workshop built on a team's own emails, documents, and data, plus a direct contact address. A software company that wants a training-led co-marketing format rather than a published playbook has a documented route here.

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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/