# Dan Shering

Dan Shering focuses on ecommerce and the digital shelf for consumer packaged goods brands, writing about product content, AI-driven shopping and ways to improve ecommerce operations.

- Profile: https://b2binfluencer.marketing/creators/dan-shering/
- LinkedIn: https://www.linkedin.com/in/dan-shering
- LinkedIn followers: 6,166
- Topics: artificial-intelligence, ecommerce, agentic-commerce, ecommerce-operations
- Based on 11 dated posts, latest 2026-10-06

## Summary

Dan Shering helps brands connect CPG ecommerce, digital shelf content and AI-driven discovery to practical operations.

- Best for: CPG ecommerce technology vendors targeting European ecommerce and commercial teams with thought leadership or advisory.
- Not for: Consumer food or household brands seeking paid short-form video for shopper purchases at supermarket launch.
- Check first: Confirm deliverables, rates, audience engagement, job role mix, and any media kit.

### Takeaways

- Retail now has two shelves: one shoppers browse, one AI agents read and recommend from.
- In Europe, AI's commercial return today is operational, not consumer-facing.
- Award-winning creative still has to earn its place on the product page.

## Main ideas

- **Two shelves now exist, and AI agents judge products by different criteria**: Dan Shering argues that retail has split into the shelf people browse and an invisible second shelf that AI agents read, interpret and recommend from. The products are the same; the criteria are not. In this account, agents recommend products based on data depth, content authority and trust signals rather than titles and bullet points. The posts treat this as a shift already underway, not a forecast. Shering cites a growth figure but notes that AI search volume is still relatively small, so the case rests on conversion rate and direction of travel rather than current size.
- **In Europe, AI's commercial return today is operational, not consumer-facing**: In a follow-up to the Shoptalk post, Dan Shering draws an unglamorous distinction: the consumer-facing AI story is coming and already arriving in parts of beauty and FMCG, but right now the money is in operations. The teams getting results use AI to close the gap between what their data tells them and what they can act on. Readiness for the consumer-facing shift depends on fixing the plumbing first: poor data quality and manual content workflows undercut any discovery advantage.
- **Stop asking what happened; ask what can be done immediately**: Dan Shering argues that European CPG teams are asking their technology the wrong question. A retrospective dashboard reports a problem after the sales are already lost, and fast reporting stops being an edge when marketplaces update continuously and small teams can only touch a fraction of the catalogue by hand. The priority is execution efficiency: shortening the distance between detecting a problem and fixing it.
- **Award-winning creative still has to earn its place on the product page**: As Cannes Lions opened, Dan Shering asked whether work that wins a Lion actually converts on the digital shelf. The gap is organisational: large CPG companies treat brand creative and product content as different disciplines. A campaign can collect accolades while the product page does not rank for the brand's own name, and a polished film can be disconnected from what a shopper sees at the point of purchase. Shering frames this as an organisational-design issue, not an attack on creativity, and says brands that treat the product page as part of the brand consistently outperform. The post also flags interest in the difference between what senior marketing leaders say publicly and what their commercial teams say privately.
- **AI breaks the usual 18-to-24-month lag between US and European retail**: Dan Shering's baseline is that US retail trends normally take 18 to 24 months to reach Europe, giving teams time to see what works before committing. AI is the exception: European shoppers are already asking ChatGPT and Gemini what to buy while brands treat AI-driven discovery as a 2027 problem. Because arrival timelines differ by trend, the preparation is broadly the same: make product content clean and structured enough for an AI shopping agent to evaluate.

## Work to read

- [The digital shelf is splitting in two](https://www.linkedin.com/posts/dan-shering_digitalshelf-agenticcommerce-cpg-activity-7472545501176025088-792n)
- [Why AI breaks the usual lag between US and European retail trends](https://www.linkedin.com/posts/dan-shering_digitalshelftrends-agenticretail-ecommerce-activity-7505269895551602688-E1of)
- [Does a Cannes Lion convert on the digital shelf?](https://www.linkedin.com/posts/dan-shering_canneslions-cpg-digitalshelf-activity-7475042969738330112-GDhU)
- [AI's biggest commercial return in Europe is operational, not consumer-facing](https://www.linkedin.com/posts/dan-shering_ecommerce-agenticcommerce-emea-activity-7473321503657324544-9OtO)
- [From dashboards that report to actions that execute](https://www.linkedin.com/posts/dan-shering_digitalshelf-ecommerce-cpg-activity-7508867219691323392--MiF)

## Partnerships

Dan Shering's posts offer thought-leadership commentary on CPG ecommerce, the digital shelf and AI-driven product discovery, published on LinkedIn. They link to their own articles ("my latest piece," "my latest article"), not to a media kit, rate card or partnership page. Industry events such as Shoptalk Europe and Cannes Lions appear as places to meet customers and partners; one post invites people who missed them at an event to reach out. The posts do not specify deliverables, sponsorship placements, speaking slots or commercial terms. Buyers considering a thought-leadership content partnership or advisory engagement should ask directly about scope, format and terms.

## Alternatives

- Andri Sadlak: Choose Andri Sadlak over Dan Shering when a campaign needs to reach both AI product discovery and creator marketing, or when the partner's buyers work in the US Amazon ecosystem as well as European retail. The posts cover Amazon Accelerate, where Alexa for Shopping is described as becoming a product discovery and recommendation layer, Seller Assistant moving from answers to actions, and Amazon connecting seller data to external AI agents. They frame creator content as a long-lived searchable source that shoppers and AI systems can discover later, and ask whether content answers a real, high-intent shopping question and whether AI recommendations and revenue can be measured per piece of content. That gives a campaign a second axis: selling creator-led content as an input to agent recommendation, rather than a separate discipline. The concept named in the posts—Recommendation Share—gives the series a measurable hook. The posts also state who the writer wants to help: challenger brands with good products earning more recommendations.
- Guru Hariharan: Choose Guru Hariharan over Dan Shering when the partner sells retail media or Amazon advertising alongside product content and needs hard numbers to make the content-and-media argument. The posts report Amazon's advertising revenue for a single quarter, then the first performance readout on AI-powered Sponsored Prompts: shoppers who interact with them convert 48% higher and spend 21% more per transaction. They use those figures to argue that content quality now directly determines ad performance, rather than acting as an abstract flywheel. The posts also make an organisational point for media sellers: the content team and the media team feed the same AI engine, and if they sit in different meetings, dashboards and reporting lines, the lift is left on the table. A series for retail media buyers or teams deciding how to split budget between content and media would start from a stronger quantified position here than from Dan Shering's posts, which describe the split shelf but do not attach conversion figures to ad formats.
- Max Sinclair: Choose Max Sinclair over Dan Shering when the campaign needs to ride a live platform change and the partner sells catalogue restructuring or agent-readiness. The posts are built for immediate action: one covers Walmart rejecting titles over 90 characters, the move to a strict concatenation structure of brand, product line, key features, size and count, and the stated reason—that Walmart is standardising content so its AI shopping agent Sparky can read it. It also gives growth figures for Sparky's users, units and average order value. The other covers the OpenClaw creator joining OpenAI to build personal agents, noting the project's GitHub traction within two weeks of launch and connecting it to Instant Checkout and OpenAI's Agentic Commerce Protocol. For a partner fixing product titles, attributes and feeds ahead of an agent rollout, this offers a faster, more concrete route to a buyer than an event-reflection post. The alert format is also easy to adapt for a sponsored series.

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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/