# Samuel Adeitan

Samuel Adeitan shares practical advice on digital and performance marketing, including paid ads, ecommerce website choices, lead quality, attribution, and the data systems that connect ad platforms with CRM and sales outcomes.

- Profile: https://b2binfluencer.marketing/creators/samuel-adeitan/
- LinkedIn: https://www.linkedin.com/in/samuel-adeitan-a7130229b
- LinkedIn followers: 347
- Topics: ecommerce, paid-ads, lead-generation, marketing-automation, attribution, conversion-tracking
- Based on 15 dated posts, latest 2026-10-09

## Summary

They are most useful for technical advice on tracking, lead quality, and CRM outcomes.

- Best for: Performance agencies fixing server-side tracking and offline conversion pipelines for ecommerce clients.
- Not for: Paid creator partnerships for broad reach and awareness through their own channels.
- Check first: Confirm whether paid partnerships, deliverables, rates, and audience demographics exist.

### Takeaways

- Ad platforms find exactly the customers you ask them to find.
- Send qualified CRM milestones back to ad platforms instead of counting every form fill.
- Attribution has to survive the gap between click and conversion.

## Main ideas

- **Ad platforms find exactly the customers you ask them to find**: Adeitan argues that advertising algorithms optimize for the finish line they’re given, and many brands hand them the wrong one. Track a form fill and the platform finds people who fill in forms; track a short call and it finds people willing to sit on hold. Falling cost per lead and rising lead volume can describe a campaign getting worse, as sales rejects most of what arrives. Adeitan treats clicks, form submissions, and calls as intermediate steps, not revenue, and says campaigns should be judged on whether they produce qualified pipeline and collected money.
- **Close the data loop: send qualification and revenue back to the platform**: Adeitan’s remedy is to close the backend loop. Ad networks cannot know what a good customer looks like unless the business tells them. Capture click identifiers when a form is submitted, attach them to the lead record in the CRM, then send verified milestones — a sales-qualified lead, a booked consult, a closed deal — back to Google or LinkedIn as offline conversions. Adeitan argues that spend shifts toward accounts that actually buy when the bidding engine receives pipeline data instead of raw submissions.
- **Attribution has to survive the gap between click and conversion**: Adeitan’s most detailed failure case concerns long consideration cycles. Privacy settings such as Safari ITP limit how long a tracking cookie lives, so a prospect who clicks an ad on Monday and returns through a direct bookmark to submit a form on Thursday can arrive with no attribution. To the media buyer, it looks like a cold direct visit; to the ad platform, the conversion never happened, so the bidding model gets no signal. Adeitan calls asking buyers how they heard about the company guesswork, not attribution. They propose storing click IDs and traffic parameters in first-party HttpOnly cookies or a server-side store at the first landing hit, carrying them across sessions, and injecting them into hidden form fields or webhook payloads when the conversion happens.
- **Late conversion feedback is worse than useless**: Adeitan treats feedback speed as a separate problem from whether the loop exists. Bidding models make auction decisions in milliseconds, so data that reaches the platform two weeks later describes a market that has moved on. By then, the budget has already gone to impressions chosen with incomplete signals. Adeitan contrasts manual CSV uploads every fortnight or month with real-time webhook pipelines from the CRM. Tracking offline conversions and trickling them back slowly, they argue, gives the appearance of measurement without improving what the algorithm buys next.
- **Audit tracking before scaling the budget, and judge it on net margin**: Adeitan’s pre-holiday argument is that default pixel settings let platforms take the path of least resistance: chase existing customers and brand searchers who would have bought anyway, claim full credit for multi-touch purchases, and optimize for top-line revenue rather than net margin. Reported returns and real profit then diverge. Adeitan recommends auditing three technical layers before increasing spend: deduplicate browser and server events so purchases are not counted twice; pass new-versus-returning customer signals so bidding targets net-new buyers; and reconcile settled store orders against ad platform click data to confirm cash collected.
- **Agency data pipelines should not be billed by the task**: Adeitan extends the measurement argument to infrastructure. They frame the real cost of scaling an agency as per-task automation, not software licences: every filtering, formatting, checking, or routing step in a Zapier or Make flow is a billed operation. A bulk import or outbound surge can burn through an account’s quota in hours and freeze operations until the business buys an enterprise plan. Adeitan recommends self-hosting the automation engine — n8n on a dedicated server or container — and using lightweight scripts for routine string and deduplication work, with a local queue in front of volume spikes so downstream API rate limits hold.
- **The wider advisory side: platform and budget choices**: Adeitan also writes for founders, freelancers, and small businesses about platform and budget decisions. They position Shopify as an all-in-one ecommerce build and WordPress as the more customizable option for content-led brands, and say they advise clients based on their goals and technical capacity. Another post promises a roundup of common mistakes across Meta Ads, Google Ads, SEO, and Shopify builds. These pieces show who Adeitan writes for and how they frame early decisions; ask for a worked example if you need to assess how they apply that advice.

## Work to read

- [Before Black Friday: three tracking audits that decide whether a 5x ROAS is real](https://www.linkedin.com/posts/samuel-adeitan-a7130229b_performancemarketing-metaads-ecommerce-activity-7503822151033110528-kJvv)
- [Attribution that survives a three-day gap between click and form fill](https://www.linkedin.com/posts/samuel-adeitan-a7130229b_performancemarketing-googleads-metaads-activity-7508181039320563712-El15)
- [Why cheap form fills wreck B2B accounts, and what to send back to the platform](https://www.linkedin.com/posts/samuel-adeitan-a7130229b_b2bmarketing-googleads-leadgeneration-activity-7508952591888740352-dFPn)
- [The hidden cost of running agency data pipelines on per-task platforms](https://www.linkedin.com/posts/samuel-adeitan-a7130229b_revops-marketingautomation-n8n-activity-7511102722780114944-05Mm)
- [Offline conversion data that arrives two weeks late is teaching the algorithm the past](https://www.linkedin.com/posts/samuel-adeitan-a7130229b_if-your-offline-conversion-data-arrives-two-activity-7507837329303150593-bQm8)
- [Record-low cost per lead is not a win](https://www.linkedin.com/posts/samuel-adeitan-a7130229b_performancemarketing-b2bmarketing-leadgeneration-activity-7507246160344985600-3o3M)
- [Shopify or WordPress: advising clients on ecommerce platform choice](https://www.linkedin.com/posts/samuel-adeitan-a7130229b_shopifyvswordpress-websitestrategy-ecommerceconsulting-activity-7326756702765166592-I72s)

## Brand relationships

- **Shopify** (mention)
- **WordPress** (mention)
- **Meta Ads** (mention)
- **Google Ads** (mention)
- **n8n** (mention)

## Partnerships

Samuel Adeitan's eight posts offer working advice and client-facing commentary on performance marketing, tracking, and marketing operations; they do not present sponsored content or brand collaborations. They include no sponsorship disclosure, media kit, rate card, partnership page, published terms, or official route for brand inquiries. In two posts, they invite readers to send a direct message about choosing between Shopify and WordPress or to ask which campaign mistake they are making. These are consulting-style conversation starters, not stated partnership formats or deliverables. For a paid collaboration, ask directly about scope, deliverables, disclosure language, and timeline.

## Alternatives

- Bivas Kumar Roy: Choose Bivas Kumar Roy when the agency's main symptom is disagreement between ecommerce platforms: Shopify reporting 82 purchases while Meta Ads reports 103, or when a client's purchase count, attribution window, or CAPI deduplication is suspect. They approach the comparison as an agency would triage it: first decide whether the issue is tracking, attribution, or both, then audit tracking, deduplication, data quality, consent, and attribution in that order. The posts name checkpoints an engineer can work through — compare attribution windows and click-through versus view-through conversions across Meta, GA4, and Shopify; use identical Event IDs between browser Pixel and server CAPI events; verify deduplication in Events Manager; trigger Purchase from a transaction ID; filter internal, test, gift, and $0 orders; check payment redirects from PayPal or Klarna; and test consent states for EU traffic. That makes them a strong alternative when the work starts from a live purchase-count discrepancy in a Shopify and Meta ecommerce stack, especially in an EU consent context.
- Reeto M.: Choose Reeto M. when the need is not a defect audit but a measured test of a predictive or server-side conversion product against the client's existing setup. Their posts describe work at Angler AI with a brand spending seven figures a month across Meta, Google Ads, and affiliate channels, where more than half of daily orders came from returning customers and the goal was to spend on new customers. The posts name the testing approach: a two-cell A/B test running an Angler predictive CAPI side by side with the client's incumbent setup, which may be Elevar, Blotout, Triple Whale Sonar, Shopify, Segment, or a homegrown build; and later, Meta's incremental attribution reporting used to measure incrementality inside that test. They are a better choice when the agency wants to test whether a new CAPI or predictive signal actually improves blended new-customer CAC and marginal new-customer CAC, rather than only fix implementation errors, or when the client wants an incrementality read on 7-day click and view-through conversions.
- Nishant Walchale: Choose Nishant Walchale when the agency needs to address ad-policy risk or Google's first-party data direction alongside tracking repair. One post breaks down Meta's Health & Wellness policy update of 26 December 2024 for DTC health, wellness, and cosmetics brands, listing what is restricted, what is allowed with 18+ targeting, and what falls outside scope; it also warns that borderline pixel setup can cause ad rejection or silent learning-phase disruption. The other describes a Google office discussion and Google's push toward first-party data infrastructure, naming Journey-Aware Bidding, Smart Bidding Exploration, and demand-led budget pacing, and recommending that brands own customer data, structure server-side tracking, feed platforms better conversion signals, connect CRM and offline conversions back into ad platforms, and stop relying only on browser-side attribution. That makes them a better fit when an agency needs a category-compliance read for a regulated vertical, or a strategic explanation of where Google's bidding and signal changes are heading that it can use with clients.

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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/