# Will O'Brien

Will O’Brien writes about ecommerce marketing measurement, including attribution, incrementality and AI-driven product discovery. O’Brien’s posts also cover early-career recruiting and how employers reach students.

- Profile: https://b2binfluencer.marketing/creators/will-obrien/
- LinkedIn: https://www.linkedin.com/in/willobriennyc
- LinkedIn followers: 3,824
- Topics: artificial-intelligence, ecommerce, recruiting, seo, ai-search-visibility, marketing-measurement, attribution
- Based on 28 dated posts, latest 2026-09-16

## Summary

Will O'Brien is most useful for explaining ecommerce measurement limits, especially attribution, incrementality, and AI product discovery.

- Best for: A B2B ecommerce analytics or measurement company sponsoring thought leadership on AI search and incrementality.
- Not for: Consumer lifestyle or packaged-goods brands seeking high-reach influencer campaigns for immediate short-form video sales.
- Check first: Ask for audience demographics, engagement data, sponsorship terms, disclosure, and results from comparable posts before contacting.

### Takeaways

- A $400M+ apparel brand's 12x affiliate ROAS was called fake; an incrementality test found little lift.
- Adding ad platforms' claimed revenue can exceed the company's actual sales, so reconcile with P&L.
- AI recommendations can send shoppers to brands and appear as direct traffic.

## Main ideas

- **Marketing measurement should survive finance's addition**: O'Brien argues that ad platforms can each claim credit for the same order under different windows, so their combined revenue can exceed what the business actually sold. The problem becomes acute when the numbers go to a board deck, a new CFO, or diligence. Their fix is not to declare a winner among platforms, but to build one attribution view with assumptions written down and platform claims shown beside the P&L, so marketing and finance can defend the same number.
- **Test whether a channel creates sales or just gets last-click credit**: O'Brien separates a channel's usefulness from the accuracy of the credit it receives. Last-click systems can credit an affiliate because its link is the last touch before checkout, even when the customer was already likely to buy. They argue the fix is to measure the channel against an incrementality baseline and audit whether the spend produced lift, not to assume the channel should be cut.
- **ROAS needs margin and business context to mean anything**: A ROAS number alone cannot tell a brand whether it made money. O'Brien argues that break-even depends on margin: at 25% margins, 4x is break-even; at 50%, 2.1x can be fine. They also use industry and revenue-band comparisons to show that the same metric can mean different things across company types.
- **AI discovery is an unmeasured channel**: O'Brien argues AI-generated answers can drive purchases without appearing as referrals. A model recommends a product, the shopper types the brand name into a browser, and the visit is recorded as direct traffic. They say this makes branded search a downstream signal of AI visibility and direct traffic a channel where AI demand can land, not just a residual bucket. They also point to Reddit and long-form community content as sources AI models cite.
- **Search Console for social content gives discovery data, not the revenue half**: O'Brien describes Google Search Console reporting for social content that connects Instagram, TikTok, X, or YouTube to search performance data. They say it can show whether social posts are seen and clicked in Search and Discover, but answers only the discovery question. Tying that traffic to revenue remains an attribution problem.
- **Plan Q4 and BFCM around new and returning customers before spend locks**: O'Brien argues BFCM should be decided in September and executed in November. Without server-side tracking that tells platforms who is new and what a customer is worth, the ad account can optimize to blended ROAS and direct the largest media outlay at people already likely to buy. They also warn against mechanically matching ad budget to revenue split, because new and returning customers need different plans.
- **Early-career recruiting starts before the application opens**: O'Brien argues students form employer preferences through clubs, peer prep groups, private Slack and Discord channels, and alumni conversations long before a job is posted. Job boards, career centers, and resume volume capture intent that is already shaped, so employers that wait for the cycle are late. Their phrase for the advantage is access beats awareness.
- **Skills-based hiring should focus on observable behavior**: O'Brien argues the credential era is fading as employers turn to observable skills signals: how students complete technical challenges, navigate ambiguous prompts, show progression, communicate in peer-led prep and apply feedback. They say the highest-potential candidates often emerge outside traditional prestige channels.
- **Enterprise buyers are consolidating vendors and asking specific questions about AI**: O'Brien argues vendors pitching best-of-breed solutions to enterprise accounts are pitching against a consolidation mandate. The driver is often contract surface area and risk posture, not just cost. On AI, they say buyers ask about context, trust, action and adoption rather than the model itself.

## Work to read

- [AI is now the fastest-growing surface for product discovery in D2C](https://www.linkedin.com/posts/willobriennyc_ai-is-now-the-fastest-growing-surface-for-activity-7472313386727350272-3ixK)
- [Reddit gets cited more than any other source in AI search results](https://www.linkedin.com/posts/willobriennyc_reddit-gets-cited-more-than-any-other-source-activity-7488331107516641280-aSjM)
- [A performance lead at a $400M+ apparel brand called O’Brien’s affiliate ROAS “fake”](https://www.linkedin.com/posts/willobriennyc_a-performance-lead-at-a-400m-apparel-brand-activity-7470574115356299264-LOI0)
- [Add up the revenue every ad platform claimed it drove last quarter](https://www.linkedin.com/posts/willobriennyc_add-up-the-revenue-every-ad-platform-claimed-activity-7503551401097142272-Kdes)

## Brand relationships

- **Triple Whale** (disclosed_partnership)

## Partnerships

The posts don’t disclose sponsorship formats, rates or partnership terms. They do show commercial work O’Brien promotes for Triple Whale: several posts speak from inside the company ("At Triple Whale, we tracked $2.88 billion in BFCM revenue across brands on the platform last year"), publish Triple Whale benchmark data on Meta costs across more than 60,000 brands, cite Ramp procurement data naming Triple Whale the most adopted marketing measurement tool, and offer a free break-even ROAS calculator. This is a measurement vendor publishing its own research and tools, not a documented third-party sponsorship offering open to other brands. For a sponsored thought-leadership series, contact O’Brien or Triple Whale directly about an editorial or co-marketing partnership. Ask about available formats, deliverables, disclosure practices and pricing before committing budget.

## Alternatives

- Mohsin Nisar M.: When the series should focus on hands-on AI visibility work rather than measurement auditing. The two posts argue that AI answers reward clear category positioning, specific outcomes and consistent terminology. They also offer a product-page optimization framework covering entities, query intent, schema and AI search readiness. For a sponsor whose audience is ecommerce SEO and content teams looking for a page-level checklist, this is closer to the daily job than an attribution argument.
- Martin K.: When the series should focus narrowly on agentic commerce: AI agents that complete a purchase and leave the sale looking like direct traffic. One post offers a Shopify Universal Commerce Protocol readiness checklist; the other frames the resulting intent-versus-transaction attribution gap. For a sponsor looking to own the agentic-commerce readiness conversation now, this is a sharper topic match than a broad measurement series.
- Maria Christopoulos Katris: When the same AI-attribution argument needs to reach talent-acquisition and employer-branding buyers rather than ecommerce operators. Both posts argue that measuring employer-brand and talent-attraction investment breaks when discovery moves into AI, and propose a visibility, reputation and outcomes framework to replace source-level attribution. A sponsor whose product sells into recruiting as well as marketing could make that case to this audience.

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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/