Erik Hettinga works on ecommerce SEO and data infrastructure at Cook and Pan, a cookware brand. Their posts describe using Shopify and other business data to improve search and sales analysis, and building AI tools that let teams ask questions across those data sources.
Erik Hettinga is most useful for practical Shopify SEO, ecommerce data work, and AI data tools.
Best for
Shopify ecommerce brand in Netherlands or Belgium wanting joined-up search, product and revenue data.
Not for
A consumer label wanting broad English reach, video, lifestyle content, or paid placement across channels.
Key takeaways
Combining Search Console, Shopify, Triple Whale and Merchant Center in Databricks can reveal SEO gaps.
A read-only AI gateway lets teams query business tools with role-based access and security checks.
AI agents choosing and checking out could change what a store pays Shopify for.
Check first: Confirm audience data, post performance, sponsorship terms, pricing, and disclosure practice before contacting them.
Main ideas
Join search, product and sales data to find SEO gaps · Make business data easy to ask about, but limit what users can do with it · AI shopping could weaken the value of a store platform
Erik Hettinga is an ecommerce SEO practitioner at Cook and Pan whose posts connect search performance with the data and systems behind an online store.
The work ranges from bringing search, sales and product data together to building an AI gateway that lets colleagues ask questions across business tools.
They also write about how AI-led shopping could change the role of ecommerce platforms.
Follow their work for practical, inside-the-store perspectives on Shopify SEO, ecommerce data and AI—not just general predictions about the technology.
Hettinga makes technical ideas concrete by starting with operational problems.
One post explains how combining Search Console, Shopify, Triple Whale and Merchant Center data in Databricks can reveal high search demand with weak visibility, missed category revenue or gaps between searches and the product range.
Another describes a read-only AI gateway across the company's tools, with permissions that vary by user and a security audit covering token management and prompt injection.
Together, these examples show a focus on making data useful while addressing access and security.
Hettinga also brings that practical lens to future-facing questions, asking what Shopify fees mean if an AI agent chooses and checks out before a shopper visits a store.
A masterclass announcement signals a teaching role; ask for the session materials to assess the teaching.
Erik Hettinga treats ecommerce SEO and AI as connected data and infrastructure problems.
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The posts favor joining business data before querying it, giving teams controlled access to that data, and questioning how AI-led shopping could change what ecommerce platforms charge for.
Join search, product and sales data to find SEO gaps
Hettinga argues that connecting a language model directly to many tools is slow and uses more tokens.
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Bringing the data together first makes it possible to relate what people search for to what a shop sells and earns.
Hettinga describes combining Search Console, Shopify, Triple Whale and Merchant Center data in Databricks to spot high search demand with low visibility, categories missing revenue, and gaps between search demand and the product range.
Make business data easy to ask about, but limit what users can do with it
Hettinga describes one gateway where people can ask questions across sources in plain language, rather than hunt through separate platforms.
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Access remains controlled: the data is read-only, and different roles see different information.
The gateway post names Shopify, Databricks, Klaviyo, Google Ads and other sources, and says staff can use Claude, ChatGPT or Gemini to ask questions. It also describes role-based access and a security audit covering token management and prompt injection.
AI shopping could weaken the value of a store platform
Hettinga argues that if an AI agent compares products and pays before a shopper visits a brand's site, a shop may still pay its platform's monthly and per-order fees while losing the platform's role in persuading customers.
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The post raises this as a question about platform economics, not a settled prediction.
Hettinga imagines AI-led shopping with a shared cart and checkout, then asks how durable a paid platform such as Shopify is if it mainly passes along a price.
This is the fullest account of a tool Hettinga built and the problems it solves.
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To see what their day-to-day work looks like beyond SEO advice, start here.
At a cookware brand, data sits across Shopify, Databricks, Triple Whale, Klaviyo, Google Ads, Meta, DatAds, Atria, Intelligems, heatmaps, Microsoft Clarity and other tools.
Anyone asking “how did that campaign run?” or “what is that collection doing?” had to wait for someone with the right login or open ten tools.
Hettinga describes building a gateway called 'Eigen MCP' that gives the whole team one secure address for plain-language questions across all those sources, accessible from any AI client.
They name Claude, ChatGPT and Gemini.
Data can be read but never changed; permissions follow the user, so an external agency sees a different slice from internal staff; and a security audit covers everything from token management to prompt injection.
The post’s point: nobody needs to know where the data lives any more — they can just ask.
For brands planning a campaign
Useful when a brand wants to judge whether a creator understands the plumbing behind AI-assisted analytics, not just the reports it produces. It also offers a concrete example for scoping an internal data-access project and seeing how one practitioner handles permissions and read-only access.
It shows how Hettinga thinks about ecommerce’s direction and is the post most likely to change how a brand frames its own AI-commerce risk.
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The argument: shopping increasingly means telling an AI what you want and having it compare, choose and pay.
Cart and checkout become universal across brands.
Google, ChatGPT, Claude, Meta and TikTok are all building toward the same protocol (UCP), and everyone with access to a customer wants the transaction itself.
Then comes the arithmetic: the brand pays Shopify a monthly fee plus a per-order fee for a platform built to persuade.
If the customer never reaches the site because an AI agent has already chosen, that fee is still paid — now for a system that only passes along a price.
The post asks how durable a paid platform like Shopify will be under that shift.
For brands planning a campaign
A hook for content about AI search and agent-mediated commerce that moves beyond visibility to cost structure. Brands planning around AI assistants can use it to ask where their platform fees still buy persuasion and where they buy only a transaction.
The clearest short explanation of the data model behind the SEO work, and readable without a data-engineering background.
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Hettinga says they see many AI setups where a language model connects directly to various tools, making it slow and token-intensive.
Their approach collects data from Search Console, Shopify, Triple Whale and Merchant Center in Databricks, connecting search behaviour, assortment and revenue.
This reveals where search demand is high but visibility is low, which categories are leaving revenue on the table, and where the assortment fails to match what people search for.
The stated payoff is greater speed and fewer tokens.
For brands planning a campaign
Relevant to a brief on ecommerce SEO and data infrastructure: the method is concrete enough to discuss, and the questions it answers — demand versus visibility, assortment versus search — map directly onto a brand’s growth agenda.
The clearest evidence that Hettinga teaches in public.
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The post also states the stance that shapes the rest of the work: practical, technical and from inside a store.
An announcement for an SEO Brein Masterclass, Thursday at 12:00.
The framing is explicit: no tricks or standard checklist, but a technical and strategic look at getting a grip on structure, scale and future-proofing within Shopify.
The session is described as an exchange of knowledge, not a pitch.
The author presents it, and Jeroen Stikkelorum hosts; the stated position is that this comes not from an agency but from someone who works at a webshop as an SEO specialist.
For brands planning a campaign
Useful if you need teaching, workshop or training content rather than reach. Ask for a recording, slide deck or follow-up write-up to assess the session’s content; the post contains only the announcement.
Short, but it names the employer and the scale they operate at, giving context to the other posts.
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A brief post recording a Shopify Milestone Award for 100,000 orders, credited to colleagues at Cook and Pan and described as a milestone reached together.
For brands planning a campaign
Background rather than a case study. It supports the practitioner-at-scale angle behind the other posts, but is a thank-you note with no method, numbers or examples; don’t treat it as proof of how the results were achieved.
Dutch-language practitioner posts from inside a single ecommerce business5
One internal gateway instead of an AI tool per data source1
Combining Search Console, Shopify and ad data in Databricks to find SEO gaps1
AI agents choosing and paying before a customer reaches the shop1
Shopify SEO taught from inside a shop, not from an agency1
One internal gateway instead of an AI tool per data source
In a post tagged #automatiseerik, Hettinga describes building an internal gateway (described there as "onze eigen MCP") so the whole team can ask questions in plain language across many business data sources at once, from any AI client they already use, including Claude, ChatGPT and Gemini.
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The sources named in the post include Shopify, Databricks, Triple Whale, Klaviyo, Google Ads, Meta, DatAds, Atria, Intelligems, a heatmap tool, Microsoft Clarity and The Cirqle.
The stated design choices are that the data can be read but never changed, access is logged with role separation so an external agency sees different data than internal staff, and the build includes a security audit covering everything from token management to prompt injection.
Hettinga’s point: nobody on the team needs to know where a dataset lives; they just ask the question.
A single secure access layer in front of all tools, rather than wiring a model directly to each platform, with read-only permissions and per-role views of the same data.
For brands planning a campaign
For a brief on making a brand’s data usable by AI tools, this is concrete, first-hand experience.
A brand can assess Hettinga’s approach to access control, read-only safeguards and per-role reporting.
Because it’s one person’s account of an internal build, ask what they designed, coded or oversaw.
Combining Search Console, Shopify and ad data in Databricks to find SEO gaps
A second technical post argues that connecting a language model directly to many tools is slow and burns tokens, and that combining the data first is better.
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Search Console, Shopify, Triple Whale and Merchant Center data are brought together in Databricks, linking search behaviour, product range and revenue. Hettinga’s examples of what that reveals: where search demand is high but visibility is low, which categories are leaving revenue on the table, and where the assortment does not match what people search for.
Pre-joining data sources in a warehouse, then querying the combined set, instead of letting a model call each platform separately.
For brands planning a campaign
Hettinga frames ecommerce SEO as a data-join problem rather than a keyword checklist. A brand with fragmented analytics can use that framing for a campaign about uniting search and sales data, but should ask for the underlying category-level examples.
AI agents choosing and paying before a customer reaches the shop
Hettinga predicts people will stop shopping on websites: they tell an AI what they are looking for, and it compares, chooses and pays, while cart and checkout become universal across brands.
Read more
The post names Google, ChatGPT, Claude, Meta and TikTok as all building toward the same protocol, referred to as UCP, and notes that anyone with access to a customer will want the transaction as well.
The argument turns to cost: a brand already pays Shopify monthly plus a fee per order for a platform built to persuade.
If the customer never opens the site because an AI agent has already chosen, the brand still pays that per-order fee for a system that only passes on a price.
Hettinga calls this uncomfortable and asks how durable a paid platform like Shopify remains.
This post is a provocation and an open question about platform economics, not a recommendation or tested prediction. Read it as Hettinga’s argument to debate, not settled fact about how AI shopping will work.
For brands planning a campaign
For a brand worried about being reduced to a price in AI-mediated shopping, Hettinga offers a working insider’s critique rather than a vendor pitch, making it suited to campaign angles about visibility and margin under AI agents. The argument cuts against paid ecommerce platforms, so a platform-side sponsor should expect a critical frame.
Shopify SEO taught from inside a shop, not from an agency
Hettinga announces a Thursday 12:00 "SEO Brein" masterclass, presented by them and hosted by Jeroen Stikkelorum.
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The session promises no tricks or standard checklist, but a technical and strategic look at how to get a grip on structure, scale and future-proofing within Shopify. Hettinga frames it as knowledge exchange from someone who works at a webshop as an SEO specialist, rather than advice from an agency.
Live teaching and peer exchange, positioned against agency-style checklists and shortcuts.
For brands planning a campaign
This established teaching format gives brands an option beyond LinkedIn posts: live-session content. Before booking, test whether the practitioner-inside-a-shop framing fits the platform and category you care about.
Dutch-language practitioner posts from inside a single ecommerce business
The material available for Hettinga is a set of Dutch-language LinkedIn posts written as one person’s working notes.
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Three are technical explainers on data infrastructure, AI access to business data and Shopify SEO; one is a masterclass announcement; one thanks colleagues at Cook and Pan for a Shopify Milestone Award marking 100,000 orders.
The recurring hashtags are #automatiseerik, #ecommerce, #SEO, #AI, #MCP, #datainfrastructuur and #buildinpublic.
In one post, Hettinga describes moving from selling pans to building data infrastructure, consistent with the cookware employer named in the milestone post.
For brands planning a campaign
A brand seeking a Dutch-language voice with hands-on ecommerce credentials and a build-in-public tone will find relevant material here, particularly for technical or data-led campaigns. Ask for work from other formats and categories before treating the scope of Hettinga’s expertise as established.
Erik Hettinga's posts tie them to one employer and the platforms their work runs on.
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They say they work for a cookware brand and thank their colleagues at Cook and Pan, the only business they are shown belonging to.
Shopify appears twice in a commercial context: as the platform behind Cook and Pan's 100,000-order milestone award, and as a system they question paying a per-order fee to if AI agents take over checkout.
They presented a Shopify-focused SEO masterclass for SEO Brein.
Other brand names in the posts—Databricks, Triple Whale, Klaviyo, Google Ads, Meta, TikTok, DatAds, Atria, Intelligems, Microsoft Clarity and The Cirqle—are data sources they plug into an internal gateway for their team.
Cook and Pan
Employment
One post says they work at a cookware brand and describes building the company's internal AI data gateway.
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A second thanks 'my amazing colleagues at Cook and Pan' for a Shopify Milestone Award marking 100,000 orders, placing them on that team.
Shopify is the platform named in Cook and Pan's milestone award post for 100,000 orders.
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In a separate post, they argue that if AI agents handle comparison, choice and checkout across brands, the monthly and per-order Shopify fees buy less because customers may never reach the store's own site.
Erik Hettinga's posts are short Dutch-language working notes on ecommerce data, AI access to business data and Shopify SEO.
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For the technical argument, start with the post on joining Search Console, Shopify, Triple Whale and Merchant Center data in Databricks, then read the gateway post on giving teams controlled, read-only access to many sources.
The AI-shopping post poses the commercial question: what happens to a paid store platform if an AI agent chooses and pays before a customer visits?
The masterclass announcement and the Cook and Pan milestone post show the in-house practitioner and live-teaching side.
Use the tags to follow these subjects and formats, and the similar creators for related reading on AI adoption, platform tactics and enterprise technology.
Both write Dutch-language LinkedIn posts about how AI is changing everyday commercial work.
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Hettinga connects data sources for AI queries and ecommerce search; Hassels Mönning describes adopting AI tools in B2B marketing, sales and customer success, and asks what adoption requires beyond a licence.
Hettinga writes about technical ecommerce infrastructure and Shopify SEO from inside a webshop. Hassels Mönning covers B2B commercial processes, ideal-customer selection and AI adoption in MKB teams.
Both explain platform changes for ecommerce sellers in practical terms.
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Hettinga asks how AI-led shopping and Shopify economics affect a brand's data and platform spend; Stupar turns ChatGPT Ads, Shopify integrations and TikTok Shop moves into actions for merchants. Both use concrete platform examples and address sellers directly.
Hettinga builds internal data, AI access and SEO infrastructure. Stupar analyses advertising and retail platform tactics, creator commerce, average order value and launch formats.
Both publish practitioner LinkedIn posts about AI and enterprise technology, with concrete examples.
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Hettinga covers data access and ecommerce search; Nagy covers SAP security events, Microsoft Sentinel, Security Copilot and agentic AI for defenders. Both explain how new tools change team workflows.
Nagy focuses on SAP security and cybersecurity in enterprise IT; Hettinga on ecommerce data infrastructure and Shopify SEO. Their shared ground is technical AI adoption, not the same market.
Erik Hettinga's audience is represented by a single measured figure: 2,300 LinkedIn followers, recorded on 9 October 2026.
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That tells a buyer the account has a small, practitioner-scale following — a realistic fit for a niche B2B placement rather than a large reach buy — but it says nothing about who those followers are.
If the need is a Shopify ecommerce brand looking for people who work on data infrastructure and AI search visibility, the content topic points in the right direction: all five posts are written in Dutch for ecommerce and SEO practitioners, covering Shopify, Search Console, Databricks, Triple Whale and Merchant Center, plus AI tooling built for teams.
That is content appeal, not measured audience composition, and Erik Hettinga's posts do not say where followers are located, their ages or genders, or their companies or job seniority.
Erik Hettinga's posts also do not say how many engagements or impressions posts receive, or how videos perform, so reach per post cannot be estimated.
Next step for the buyer: ask Erik Hettinga for (1) recent LinkedIn analytics for comparable posts — impressions, reactions, comments and follower growth — and (2) audience survey results or platform demographics covering geography and job function, since professional attributes such as employer and seniority are not something a follower count can verify.
Treat the 2,300 figure as the only audience number currently safe to plan against.
Erik Hettinga's five LinkedIn posts show a practitioner working on ecommerce SEO and data infrastructure for cookware brand Cook and Pan, announcing a Shopify-focused SEO masterclass, and crediting colleagues on a 100,000-order milestone.
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Shopify, Databricks, Triple Whale, Klaviyo, Google Ads, Meta, DatAds, Atria, Intelligems, Microsoft Clarity and The Cirqle appear as parts of a working data and marketing stack; the posts disclose no sponsorships or endorsements involving these tools.
One public appearance is described: an "SEO Brein" masterclass at 12:00 on a Thursday, presented by they and hosted by Jeroen Stikkelorum.
It is framed as a technical and strategic session on Shopify structure, scale and future-proofing rather than a vendor pitch.
The posts list no rates, package tiers, deliverables, exclusivity terms or booking contact.
For a Shopify brand planning a campaign, a paid partnership would need to be negotiated directly rather than selected from a published menu.
Ask for a current media kit, rate card or sample collaboration terms, including pricing and disclosure practice, before budgeting.
A Shopify-based ecommerce brand in the Netherlands or Belgium wants to join up its product, search and revenue data so the team can ask plain-language questions about it. It also wants to understand what AI-assisted search and shopping mean for how customers find the brand. The budget is for technical content and a working session, not mass reach.
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Reasons
The most detailed post describes building an internal gateway that lets the whole team query many business systems from any AI client. The data is readable but never changeable; access is logged and split by role, so an external agency sees a different slice from internal staff. The account also covers a security review of token management and prompt injection. These are the questions a technical buyer asks, answered in the build's own account.
A second post sets out the data model behind the SEO work: Search Console, Shopify, Triple Whale and Merchant Center brought together in Databricks to link search behaviour, assortment and revenue. The stated outputs match this brief: finding where search demand is high but visibility is low, which categories leave revenue on the table, and where the range does not match what people search for.
Erik Hettinga teaches in public about Shopify structure, scale and future-proofing, framing the subject as a technical and strategic session from someone working inside a webshop rather than an agency. That gives the brief a live or workshop element to build on, alongside written posts.
The AI-shopping argument—agents comparing, choosing and paying before a customer opens the site, and what a monthly plus per-order platform fee buys once that happens—lets a brand frame AI search visibility around cost and margin, not just traffic.
Caveats
Scale is practitioner-level: 2,300 LinkedIn followers recorded in October 2026. The posts include no impressions, reactions, comments or follower-growth data, so reach per post cannot be planned.
Audience demographics are unavailable: geography, job function and seniority are unknown. Ask for audience data before committing to a brief aimed at a specific buyer segment.
No sponsorship offer, rate card, package or booking contact is published. Negotiate directly and agree terms, pricing and disclosure practice in advance.
Verdict
Strong fit based on the posts.
When to choose a specialist
A consumer label—for example, a fashion or drinks brand—wants a broad English-language launch with high reach, video and lifestyle content, paid placement across several channels, and past campaign performance as proof before booking.
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Reasons
The audience figure is small for a reach buy: 2,300 LinkedIn followers. The posts include no engagement, impressions or video-performance data, so ask for performance figures before estimating the reach of a paid placement.
Every post is written in Dutch for ecommerce and SEO practitioners. Topics include data infrastructure, AI access to business data, Shopify SEO and one masterclass announcement. There is no lifestyle, product or consumer-facing material to point to.
No video, podcast, article, newsletter, slide deck or code repository was available, so multi-channel content production cannot be evidenced.
The 100,000-order milestone post names Cook and Pan and thanks colleagues, but gives no method, numbers or examples. It provides context on scale, not a case study a brand can cite as proof of results.
The material comes from one person's working notes inside a single employer, so experience across brands, categories and platforms is unproven. If the brief requires work with consumer brands across markets or outside Shopify, ask for examples.
No formats, rates or terms are published, so the campaign cannot be costed or compared with other candidates at selection stage. Ask for a proposal.
Caveats
The posts do not support a consumer campaign or paid collaboration. Ask Erik Hettinga directly about relevant experience and availability.
Verdict
Poor fit based on the posts.
A worked campaign example
Angle
"Where your shop loses demand between search and shelf" — a two-part engagement for a Shopify brand: a working session using the brand's own data, followed by a published breakdown of the findings.
Asks
Agree scope, fee, deliverables and disclosure in writing; no package, rate or booking route is published.
Confirm which data sources the brand can expose as read-only, and who can see each slice. The role-based access pattern described in the posts is a natural starting point.
Confirm the language and market for publication; the posts are in Dutch and aimed at ecommerce and SEO practitioners.
Caution
The AI-agent economics argument—that a brand keeps paying a per-order platform fee for a system that may only pass on a price—is a critical frame. It suits a brand debating its own platform costs, but not a piece sponsored by the platform itself.
Measurement
Search impressions and organic sessions for the named categories before and after.
Organic revenue and conversion rate for those categories over an agreed window.
Check a month later whether the brand’s team still uses the prompts from the session.
Steps
The brand provides read-only access or exports from Search Console, Shopify, Merchant Center, and its ad and analytics accounts for an agreed period.
A joint working session brings those sources together and produces a shortlist of categories and queries where demand outstrips visibility, along with any assortment gaps.
The creator writes a plain-language breakdown of three specific examples from the shortlist, including the queries and the pages or products involved, so readers can inspect the reasoning rather than simply trust the conclusion.
A live session walks the merchandising and SEO team through the same shortlist and leaves them with reusable prompts for asking their own questions of the data.
Publish a short methodology note covering the data used, the period, and what the analysis could not see.
Why
The approach follows the method Erik Hettinga describes publicly: first, combine Search Console, Shopify, ad, and analytics data; then query the combined set to find categories where demand is high but visibility is low, and where the range does not match what people search for.
When the need is less about joining a webshop’s search, product and revenue data, and more about the commercial foundations of an AI rollout.
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One post describes returning to study in autumn 2025, becoming certified as an AI Business Professional, and what changed: sharper conditions for the commercial process and the everyday gap between switching on Copilot, ChatGPT or Gemini for colleagues and getting them to adopt the tools.
The argument: holding a licence is not adoption.
A second post examines what makes an ideal B2B customer, including high-value solutions, international reach, attention to marketing, sales and customer success, culture fit and long-term client value, as well as when to end a loss-making client relationship.
For a Benelux B2B client deciding which customers to pursue and what must be in place before AI helps sales and customer-success teams, this speaks more directly to the decision than a method for joining shop data.
The posts focus on B2B client selection and AI adoption in sales and marketing, not ecommerce data infrastructure: Shopify, Search Console, Merchant Center, analytics integrations, queryable internal gateways or AI search visibility. A brand seeking to join its demand and revenue data or hold a working session on its shop’s search visibility would be hiring for a technical build, not a commercial-process discussion.
When the AI-search need is really about paid AI ad placements and social commerce launches, not joining the brand’s own data.
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One post walks through OpenAI placing ChatGPT Ads inside Shopify and HubSpot: US merchants installing an app from the Shopify App Store, international markets following six days later, HubSpot tagging URLs so attribution can be argued with numbers, product data becoming the ad creative, reach capped to free and lower-tier users, and testing across client accounts from a dated start.
Another explains what Sephora launching new products on TikTok Shop ahead of its own site means for sellers: locking top creators into retainers rather than affiliate links, a higher average-order-value ceiling as prestige retail enters the feed, and exclusivity windows as a TikTok tactic.
For a brand deciding where to test AI ad inventory and how to move before a category gets expensive, this material is a closer fit.
The posts offer English-language channel commentary and agency testing, not a data-joining method a brand’s team can reuse.
They cover no Shopify or Search Console pipeline, assortment-gap analysis or Benelux market framing.
They address a launch-and-budget question; a brand seeking to understand its own search demand and product data would get channel guidance, not the infrastructure discussion at the centre of Erik Hettinga’s material.
If the campaign targets an enterprise software or security buyer rather than a Shopify brand, the two posts cover an SAP security event in Frankfurt and a Microsoft Sentinel and Security Copilot use case combining detection data from a security vendor to assess and triage threats.
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They also argue that SAP security will be an intelligent ecosystem of specialised parts rather than one product doing everything.
The other post marks a personal cycling milestone around a company-sponsored ride, offering a glimpse of the wider professional network but no buyer-relevant method.
For a brand selling enterprise security, ERP protection or agentic defence tooling and seeking a practitioner who works inside that ecosystem, this is a relevant campaign.
This is not a fit for a Shopify ecommerce campaign: the posts cover no ecommerce, SEO, product data, assortment or retail AI search visibility. Choosing this creator means changing the subject of the campaign, not swapping one ecommerce data practitioner for another.
Erik Hettinga's posts cover a Cook and Pan Shopify milestone for 100,000 orders, an announcement of a Shopify SEO masterclass, a note on joining Search Console, Shopify, Triple Whale and Merchant Center data in Databricks, a description of an internal read-only AI data gateway, and an argument about AI agents and per-order platform fees.
No publication dates are given in the posts, so Hettinga's work cannot be tied to a timeline or compared across older and recent material.
The one audience figure, around 2,300 LinkedIn followers, was recorded in October 2026; no engagement, demographic, video or campaign-performance data is available, and the posts here contain no sponsorship formats, rates, packages or booking terms.
Similar-creator comparisons draw on posts by Paul Hassels Mönning (February 2025 and April 2026), Marko Stupar (September 2026) and Christoph Nagy (July and September 2026), judged on the ideas and formats in those posts rather than fame or follower counts.
Work history, employer details and credentials beyond what the posts state were not independently verified.