Karl Simpson works in ecommerce, with a focus on Amazon, product data, merchandising, and the processes that help businesses manage product catalogues and prepare for AI-driven shopping.
Karl Simpson is most useful for explaining how product data and process affect Amazon and AI shopping.
Best for
A B2B ecommerce SaaS brand running content on catalogue governance and AI readiness.
Not for
A consumer home goods brand seeking paid social video with large consumer reach.
Key takeaways
Process, not better tools, often solves delayed launches and inconsistent supplier data.
Product information needs context, use cases and comparisons for AI shopping searches.
Brand knowledge of Amazon shapes the help it needs from agencies.
Check first: Confirm recent results for comparable topics and formats before booking.
Main ideas
Process comes before tools · Product data decides where a product can show up, including in AI shopping · How much a brand knows about Amazon shapes the help it needs
Karl Simpson writes about ecommerce, Amazon, product data and the processes behind online retail.
Their posts connect practical catalogue challenges—such as inconsistent supplier data and declining catalogue quality—to bigger shifts in how people find and buy products.
They present The Product Data Advantage as a publication about product data, ecommerce, merchandising, retail and AI.
Simpson makes emerging ecommerce changes relevant to day-to-day operations.
In a post about AI-assisted shopping, they contrast customers arriving at Amazon with a product in mind against shoppers asking AI how to solve a problem.
Their point for retailers: product information needs more than keywords; context, use cases and comparisons can help products surface in new kinds of searches.
In another post, they argue that process—not simply better tools—is often the answer to delayed launches, inconsistent supplier data and deteriorating catalogue quality.
They frame good process as a foundation for AI, automation and growth, not bureaucracy.
A reflection on moving from an in-house Amazon role to agency work adds a cross-business perspective: they describe learning the different products, needs and levels of platform experience across client accounts.
Readers interested in Amazon, product data and the operational side of AI in retail can follow their work for practical connections between these topics.
Karl Simpson writes about ecommerce operations, product data and how AI is changing the way shoppers find products.
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Three arguments run through these posts.
First, process, not software, limits most ecommerce businesses: launches slip, supplier data drifts and catalogue quality decays because ways of working are weak, and new tools do not fix the underlying problems.
Second, the structure and quality of a product record determine where a product can appear, and AI shopping raises the bar because sessions begin with a problem rather than a product.
Third, brands sit at very different stages of Amazon platform maturity, so the help they need differs.
Simpson's stated priorities are product data, merchandising, retail and AI, covered in free weekly writing on the Substack The Product Data Advantage.
Process comes before tools
Simpson argues that ecommerce teams over-attribute performance to software because they are already financially and emotionally invested in what they have bought.
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In their experience, growth and performance usually follow better process.
They make process the first pillar of what they call the PREPARED ecommerce operating system, and preview a linked article with four claims: process should enable rather than restrict people; supplier data is a process problem before it is a data problem; better process is the foundation for AI, automation and scalable growth; and every ecommerce business should understand seven operational processes.
Their definition is deliberately un-bureaucratic: "Good process isn't bureaucracy.
It's simply a better way of getting important work done, consistently."
The concrete failures Simpson lists are delayed product launches, inconsistent supplier data, catalogue quality that deteriorates over time, and teams spending more time working around problems than solving them.
They say these rarely turn out to be technology problems.
The linked article promises seven processes but the post does not list them.
Product data decides where a product can show up, including in AI shopping
Simpson sees AI shopping tools shifting the basis of competition for product records.
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Keywords and metadata still matter for search, but context, use cases, comparisons and problem solving matter more as AI-assisted buying spreads, because AI sessions start with a problem rather than a chosen product. The practical consequence is catalogue work: accurate specifications, data enrichment and product governance determine how widely a product can surface.
Simpson quotes a Nielsen IQ release, dated 5 May 26 in the post, reporting that 42% of consumers used at least one AI tool to shop in the past month, 17% used AI for product recommendations, 19% use subscribe or auto-replenish for repeat purchases, 10% used a voice assistant to purchase or reorder, 10% engaged an AI-powered shopping assistant, and 5% used fully autonomous agents to place orders.
They see this as evidence that autonomous ordering remains limited while AI-assisted decision-making is mainstream.
They contrast two journeys: on Amazon, shoppers usually arrive with an idea and weigh price, reviews, customer service and delivery time; on an AI platform, the starting point is "I have a problem, how do I fix it?"
How much a brand knows about Amazon shapes the help it needs
From inside Amazon marketplace work, Simpson observes that the hardest part of agency-side work is the range of client situations, not any single technical problem.
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There are many business types to understand quickly; each client is at a different stage of its Amazon journey, with its own commercial challenges and understanding of what successful selling on the platform takes. Advice and education need to match each client's maturity rather than follow one standard playbook.
Simpson contrasts one month on the agency side at Unified Retail with years as the in-house Amazon specialist for a single brand. They cite six years working directly on the platform and a 15-year ecommerce background as the experience they now apply across multiple retail and brand accounts.
Turning market signals into questions operators should answer about their own data
Across the posts, Simpson takes an external signal — a ChatGPT email about shopping, a research release, a platform change — and turns it into questions ecommerce operators can apply to their own catalogues, systems and processes.
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The posts make the argument actionable, then leave readers with a question that invites discussion.
The AI shopping post asks whether readers have noticed products appearing in new places online and whether their systems cover accurate product specs, data enrichment and product governance.
The process post asks which single ecommerce process most businesses would benefit from improving.
Simpson also runs a free weekly Substack, The Product Data Advantage, described as thought pieces and accumulated wisdom from over two decades in the trenches, covering product data, ecommerce, merchandising, retail and the encroachment of AI.
Why does Karl Simpson argue that better ecommerce performance comes from process rather than new tools?
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This is the clearest statement of the operating idea behind the rest of the work.
Simpson opens by challenging the assumption that better tools produce better performance and growth, and explains why that belief is hard to drop once a business has paid for the software.
In their experience, growth usually followed better process.
The examples are concrete: product launches delayed, supplier data inconsistent, catalogue quality deteriorating over time, and teams spending more time working around problems than solving them.
Simpson says these rarely turned out to be technology problems; they were more often process problems.
That is why process is the first pillar of what they call the PREPARED ecommerce operating system.
The post previews the linked article with four points, including why supplier data is a process problem before it is a data problem, how better process creates the ground for AI, automation and scalable growth, and the seven operational processes they believe every ecommerce business should understand.
It closes with a plain definition: "Good process isn't bureaucracy.
It's simply a better way of getting important work done, consistently."
The article is linked in the comments; the seven processes are promised rather than shown here.
For brands planning a campaign
This gives a content or webinar series a spine that does not depend on tool hype: process first, then AI and automation.
A session on catalogue governance could use the four preview points as an outline, with Simpson supplying the detail promised in the post.
The closing question—which single ecommerce process most businesses should improve?—works as an audience prompt for a live event.
How does Karl Simpson connect AI shopping tools to catalogue quality?
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This post answers directly with third-party figures and a side-by-side comparison of how people buy on Amazon versus through AI.
Simpson shares a ChatGPT email that arrived that morning, prompting users to use the platform for shopping, alongside consumer research attributed to Nielsen IQ and dated 5 May 26.
The figures they report: 42% of consumers used at least one AI tool to shop in the past month, 17% used AI for product recommendations, 19% used subscribe or auto-replenish for repeat purchases, 10% used a voice assistant to purchase or reorder, 10% engaged an AI-powered shopping assistant, and 5% used fully autonomous AI agents to place orders.
Simpson reads the data as showing that fully autonomous shopping is still limited, while AI-assisted decision-making is becoming mainstream.
The most useful part is the contrast between two purchase journeys.
On Amazon, shoppers usually arrive with an idea in mind, and the journey narrows to price, reviews, customer service and delivery time.
On AI platforms, the starting point is "I have a problem, how do I fix it?" and the tool drills into the specifics before tailoring a response.
The practical conclusion: keywords and metadata still matter for search, but context, use cases, comparisons and problem solving become more relevant, with direct implications for how product data is structured, enriched and governed.
Ask for the Nielsen IQ link and ChatGPT email before using the figures.
For brands planning a campaign
This is a strong hook for an AI-readiness article or webinar: it combines a citable set of statistics with a framework a brand can act on.
Problem-first journeys demand richer product data than product-first journeys.
The closing question about products appearing in new places online is a ready-made engagement prompt.
Where does Karl Simpson publish longer work, and what does it cover?
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This short post names the publication and its subject range in their own words.
This announces the first post on Simpson's Substack, The Product Data Advantage.
They describe the planned content as regular weekly material on product data, ecommerce, merchandising, retail "and of course the all pervasive encroachment of AI," all free.
The posts will take the form of thought pieces and accumulated wisdom from over two decades spent in the trenches.
The post invites readers to subscribe and share.
For brands planning a campaign
A named, active outlet gives a buyer a concrete placement route: a co-authored article series could run on The Product Data Advantage rather than being hosted from scratch. Check the publication's recent output against the stated weekly, free cadence before proposing a schedule.
What does Karl Simpson do, and how much hands-on platform experience informs the advice?
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This short update answers the background question the other posts assume.
Writing one month after moving to the agency side of Amazon, Simpson describes the shift as very different from working as an in-house Amazon specialist for a brand.
The differences are practical: understanding many business types quickly; working with clients at different stages of their Amazon journey, each with distinct commercial challenges and needs; and adapting to their varying understanding of what it takes to sell successfully on the platform.
Simpson notes six years working directly on the platform and a 15-year ecommerce background, and credits exposure to multiple retail and brand accounts as the source of their learning.
The post credits the team they joined, Unified Retail, and quotes Stephen Covey's phrase about sharpening the saw.
For brands planning a campaign
This is the credibility paragraph for a proposal: seller-side and agency-side Amazon experience across multiple client accounts, plus a long ecommerce background. Confirm their current role and dates before using any bio claim.
Turning platform and consumer signals into guidance for ecommerce operators3
Product data determines where products surface — including in AI shopping2
Process before tools: the PREPARED operating system1
Amazon channel knowledge from both sides of the table1
Product data determines where products surface — including in AI shopping
Karl Simpson argues that catalogue structure and quality determine where products surface, and that AI tools are changing the basis of that competition.
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In a post about ChatGPT's shopping guidance, they cite a Nielsen IQ study (dated 5 May 26 in the post) reporting that 42% of consumers used at least one AI tool to shop in the past month, 17% used AI for product recommendations, 19% use subscribe or auto-replenish for repeat purchases and 10% engaged with an AI-powered shopping assistant.
They read the figures as evidence that AI-assisted decision-making is mainstream, even as fully autonomous ordering remains limited.
The practical implication is a shift in product records: keywords and metadata still matter for search, but context, use cases, comparisons and problem-solving matter more as AI shopping sessions begin with a problem to solve rather than a product already chosen.
They contrast Amazon journeys, where shoppers arrive with an idea and weigh price, reviews, service and delivery, with AI platforms that start by exploring the problem.
Weekly Substack newsletter ("The Product Data Advantage") and LinkedIn posts translating third-party consumer research and platform changes into catalogue practice.
Simpson reports consumer statistics from a Nielsen IQ release. The underlying report is not included, so cite the figures to the post rather than re-derive them.
For brands planning a campaign
Directly usable for a B2B ecommerce SaaS content brief on catalogue governance and AI readiness: Simpson makes the case for auditing product specs, enrichment and governance, and can anchor an article or webinar on structuring product data for AI-driven discovery, not just marketplace search.
Process before tools: the PREPARED operating system
Simpson argues that ecommerce teams over-attribute performance to software when process is usually the constraint.
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Introducing the first pillar of the PREPARED ecommerce operating system, they describe businesses where launches slipped, supplier data was inconsistent, catalogue quality decayed and teams spent time working around problems.
These, they say, were usually process problems, not technology problems.
Their claims: process should enable, not restrict, people; supplier data is a process problem before it is a data problem; better process is the foundation for AI, automation and scalable growth; and every ecommerce business should understand seven operational processes.
The framing is deliberately anti-bureaucratic: "good process isn't bureaucracy, it's simply a better way of getting important work done, consistently."
A named, numbered framework (PREPARED) published pillar by pillar, with each post pointing to a longer article and ending with a question inviting the audience to name the process they would fix first.
For brands planning a campaign
Gives a SaaS content partner a ready-made structure for a seven-part article or webinar series mapped to Simpson's seven operational processes, starting with supplier data and catalogue quality as process failures rather than tooling gaps.
Amazon channel knowledge from both sides of the table
Simpson writes from inside Amazon marketplace work.
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Marking one month on the agency side at Unified Retail, they contrast that work with in-house brand roles: learning about many client businesses quickly, each at a different stage of its Amazon journey and with a different level of seller understanding.
They cite six years working directly on the platform and a 15-year ecommerce background, and describe exposure to multiple retail and brand accounts as a way to “sharpen the saw.”
The post offers a working observation about how support needs vary by client maturity, rather than a how-to.
Plain first-person field notes from current work, without promotional framing or a product pitch.
For brands planning a campaign
Useful credibility context for an Amazon-specific content programme and a source of realistic audience language: brands at different stages of Amazon maturity need different levels of seller education.
Turning platform and consumer signals into guidance for ecommerce operators
Simpson turns external signals — a ChatGPT email about shopping, a research release or a new platform feature — into questions ecommerce operators should be able to answer about their catalogues, systems and processes.
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The ChatGPT post asks whether readers have noticed products appearing in new places and whether they have the systems for accurate product specs, data enrichment and product governance. The process post asks which single ecommerce process most businesses should improve.
Signal-to-implication commentary: short observational posts ending with a direct audience question, alongside longer articles linked from the posts.
For brands planning a campaign
Suits a content partnership built around discussion prompts and comment engagement, not just broadcast articles. The closing questions lend themselves to a webinar Q&A, a LinkedIn live or an assessment checklist for a SaaS audience.
Karl Simpson's own posts document two explicit business connections: they joined the agency-side Amazon team at Unified Retail as a new team member, and they run The Product Data Advantage, their own Substack publication about product data, ecommerce, merchandising, and AI.
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Two other posts discuss ChatGPT's shopping prompts and Nielsen IQ survey data as topics, with no stated commercial tie to either.
Unified Retail
Employment
Simpson says that after one month working on the agency side of Amazon, they are “a new member of the Unified Retail team,” moving from an in-house brand Amazon role to agency work across multiple retail and brand accounts.
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The post also cites six years working directly on Amazon and a fifteen-year ecommerce background.
It presents the role as current at the time of writing.
Simpson describes “my new Substack for The Product Data Advantage” and invites subscribers to weekly free content on product data, ecommerce, merchandising, retail, and AI, drawing on their stated two decades in the field.
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The post presents the publication as their own, and the article they reference on the seven ecommerce operational processes appears there.
Karl Simpson's writing centres on product data, ecommerce operations, Amazon marketplace practice and AI's effect on how shoppers find products.
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They take a platform or research signal, explain its implications for catalogue work and leave readers with a question to test in their business.
Haider Ali writes about AI-assisted Amazon research and agentic commerce; Vincenzo Toscano covers Amazon advertising and European expansion; Brendan Ducie explains ecommerce and Amazon selling for newcomers.
They share a broad ecommerce and marketplace focus, with different emphases and teaching styles.
Both write about how AI is changing ecommerce and Amazon selling, translating platform changes and AI tools into practical implications for sellers.
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Haider Ali focuses on tactical AI-assisted research workflows, sharing copy-paste prompts for competitor analysis in Chrome and covering agentic commerce protocols. Karl Simpson focuses on the catalogue data, process and governance behind product visibility.
Both work in the Amazon and ecommerce ecosystem, speaking to brands and operators about marketplace strategy and pairing platform or event observations with practical advice.
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Vincenzo Toscano's posts centre on Amazon advertising, Q4 planning, EU expansion and industry events. Karl Simpson's writing centres on product data, catalogue governance and ecommerce process.
Both write about selling and marketing online, including on Amazon, for ecommerce audiences.
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Brendan Ducie's posts offer introductory explanations and cautionary notes about ecommerce and Amazon selling. Karl Simpson's work is more specialised in product data, AI-driven discovery and operational process.
Karl Simpson’s LinkedIn audience offers a small but relevant starting point for a B2B ecommerce campaign: the account had 3,638 followers on 9 October 2026, and posts cover Amazon, product data, ecommerce operations and AI-assisted shopping.
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That overlap supports testing a webinar or article series on catalogue governance and AI readiness.
Before booking, request recent results for comparable topics and formats.
If professional roles or seniority matter, ask for audience survey results or another documented source.
Karl Simpson's four LinkedIn posts, published between late November 2025 and late June 2026, and profile document no partnership offer, sponsor policy, rate card, media kit, paid brand engagement, or way to submit a partnership request.
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They do show a self-published newsletter, The Product Data Advantage on Substack, which they describe as free weekly content on product data, ecommerce, merchandising, retail, and AI.
This shows where their writing appears and that readers are not charged for it; it does not establish whether paid placements, sponsored articles, or consulting work are available.
The posts also provide no commercial terms, deliverables, exclusivity, turnaround, or pricing.
Ask Simpson directly via the LinkedIn profile listed under their name whether they accept sponsorships or paid work, including sponsored content, consulting, or webinars, and on what terms.
A B2B ecommerce SaaS company needs someone to lead content on product data and AI readiness, with a webinar or article series on catalogue governance and AI shopping.
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Simpson matches the substance of this need.
Across the posts, the recurring argument is that product catalogue structure and quality determine where products can surface, and that AI shopping tools are changing what that competition rewards.
In the ChatGPT shopping post, they contrast an Amazon journey, where a shopper arrives with a product in mind and narrows by price, reviews, customer service and delivery time, with an AI session that starts from a problem and drills into specifics.
Their conclusion: keywords and metadata still matter, but context, use cases, comparisons and problem solving matter more.
That speaks directly to product data, enrichment and governance, and gives a catalogue-governance programme an argument in Simpson's own words.
They also have a format that suits a series.
The Substack, The Product Data Advantage, is described in the launch post as regular free weekly content on product data, ecommerce, merchandising, retail and AI—a plausible co-publishing venue rather than something built from scratch.
The PREPARED framework offers a structural spine: process is named as the first pillar of an ecommerce operating system, with the claim that supplier data is a process problem before it is a data problem.
Each post ends with a direct question to the audience, usable as a webinar Q&A prompt or a short self-assessment for the SaaS audience.
Three points need checking before committing.
Ask Simpson for partnership terms, including rates, sponsor policy, paid-work disclosure and an inquiry route.
Their LinkedIn account had 3,638 followers on 9 October 2026, a small direct base, so plan to build the series audience through the host's distribution rather than assume reach.
Only four short text posts are available here; ask to review the linked PREPARED article and recent Substack issues before promising a weekly schedule.
Publication dates are not shown, so ask Simpson to confirm the timing of the agency move and how current the writing is.
When to choose a specialist
A consumer home-goods brand wants a creator to front a paid social video campaign: short product-in-use videos for TikTok and Instagram Reels, a large consumer reach target, disclosed sponsorship, fixed deliverables and a short turnaround.
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Simpson is a poor fit for this brief on three separate grounds, none of which is a judgment about capability elsewhere.
Format.
The posts are written social text aimed at ecommerce operators.
They provide no evidence of video, short-form consumer content or transcripts.
A campaign built on filmed product demonstrations is not supported by the material available here.
Audience.
The posts address people running or advising ecommerce operations—catalogue owners, Amazon sellers and merchandisers.
The consumer-appeal and reach assumptions behind a DTC video campaign are not supported by a 3,638-follower LinkedIn account, and the posts provide no audience data to inform consumer targeting.
Commercial mechanics.
The posts do not specify sponsorship format, rate card, deliverables, exclusivity, turnaround or pricing, and publish no partnership inquiry route.
A campaign with fixed paid deliverables cannot be planned on that basis.
Ask Simpson directly whether they take on paid video work and on what terms.
A worked campaign example
Worked campaign angle for the SaaS company's content need: a three-part article series plus a live webinar, co-published on The Product Data Advantage and the SaaS company's blog, with Simpson as named author or co-author.
Article 1 — "Problem-first shopping: why catalogue fields carry more weight than ad copy now."
Open with Simpson's comparison: an Amazon journey starts with a product already in mind and narrows through price, reviews, service and delivery, while an AI shopping session starts with a problem and drills into specifics.
The operator consequence: when the query is a problem rather than a product name, the catalogue record has to carry the use case, comparison and specification detail.
Simpson cites Nielsen IQ figures as reported in the post: 42 per cent of consumers used at least one AI tool to shop in the past month, 17 per cent used AI for product recommendations, 10 per cent engaged an AI-powered shopping assistant, and 5 per cent used fully autonomous agents to place orders.
Ask Simpson to verify the figures and provide the underlying Nielsen release before publication.
Article 2 — "Supplier data is a process problem before it is a data problem."
Build on Simpson's PREPARED argument: launches are delayed, supplier data is inconsistent, catalogue quality decays, and teams work around problems instead of solving them; these are usually process failures rather than technology failures.
The practical payload is a catalogue governance checklist readers can complete: who owns each product field, where supplier data is validated on entry, how often records are re-audited, and what happens when a spec is missing.
Simpson refers to seven operational processes but does not set them out in the posts; ask them to provide the processes rather than have the host invent them.
Article 3 — "Governance for AI surfacing."
Use Simpson's closing questions as the frame: are your products appearing in new places, and do you have accurate product specs, data enrichment and governance in place to show up as widely as possible?
End with a short maturity self-assessment.
Webinar, 45 minutes.
Simpson opens with the AI-shopping shift and the Amazon-versus-AI contrast.
The middle section is a live walk-through of the Article 2 checklist using one anonymised sample catalogue.
Close with Simpson's question to the audience— which single ecommerce process should most businesses improve first—as the Q&A prompt.
Promotion. Each article ships with a short LinkedIn post in Simpson's existing pattern: external signal, what it means for an operator, one direct question to the audience.
Conditions on this plan.
Confirm fees and terms directly; no partnership route or rate is published in the posts.
Confirm Simpson's current role and dates, which the posts do not provide.
Review recent Substack issues before locking a weekly cadence.
If the PREPARED article or newsletter output is thinner than the posts promise, keep the series host-led with Simpson as a contributing voice rather than a co-author.
Choose Ali when a campaign rewards a repeatable AI workflow an operator can copy the same week, rather than a governance argument.
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The posts show a working method: saving a prompt as a Gemini skill in Chrome so it runs across open Amazon tabs and returns listing comparisons, review themes, brand-store positioning and three content or product gaps formatted for a creative brief.
The setup steps and prompt are published for readers to reuse.
The second post is a plain-language explainer on agent-driven purchasing — the Universal Commerce Protocol, agent checkout, OpenAI ads via ACP and Amazon Rufus — arguing that sellers who cannot be found and bought from by an AI agent will miss the purchase.
For a SaaS series aimed at sellers who want a usable prompt and a straightforward account of how AI agents buy, this is a closer fit.
Both posts invite feedback, making them suitable for a webinar Q&A or working session.
The posts are tactic-first.
Ali supplies a prompt and a market explanation, not the organisational argument Karl Simpson makes: catalogue quality and supplier data are process problems before they are data problems, and governance determines where products surface.
Neither post explains who owns a product field, how records are re-audited or how enrichment is maintained.
A multi-part programme on catalogue governance would need that spine to come from elsewhere.
The posts also give no partnership terms, rate card, inquiry route or audience data.
Ask directly about commercial availability and reach.
Choose Toscano when the campaign should focus on live events and peak-season advertising rather than product data.
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The posts describe hosting a packed Amazon EU Accelerate meetup at the London Chamber of Commerce and Industry and keynoting the first Amazon Ads EU Business Accelerator, with Ecomcy named as an event sponsor and a podcast recorded with a Rising Stars brand.
The advice to the room is concrete and seasonal: stop treating Q4 advertising as “spend more and hope”; use AMC and DSP to see who actually buys rather than who clicks; build audiences before peak so cost-per-click spikes do not eat margin; and retarget into January because the sales cycle does not end on Black Friday.
A campaign on advertising strategy for European expansion, or a series needing a speaker who can carry a room, fits better than the catalogue-quality argument.
The focus is Amazon advertising and events, not catalogue structure.
The posts’ AI references — that AI is moving from buzzword to real application in advertising and operations — are conference takeaways, not a practical argument about product data, enrichment or surfacing in AI shopping.
A series on catalogue governance would need to supply that context.
The posts also promote the company and its events, with Ecomcy at the centre of both.
Settle early whether the campaign is with Toscano personally or with the company, and ask about fees and terms.
Choose Ducie when the audience is new to selling online and needs vocabulary and a cautionary framing rather than operator-level detail.
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One post explains the difference between digital marketing, online marketing and ecommerce, using a website sales example to distinguish marketing that builds a brand from the system that takes the order.
The other warns about buying and selling on Amazon: wholesale and retail arbitrage are brutally competitive, anyone can undercut your price, the only leverage in wholesale is logistics through your own warehouse, shipping or drop-shipping, and long-term success means selling on your own site or through affiliate sales.
The posts answer entry-level questions about what these terms mean and whether marketplace selling is worthwhile, in plain language.
The posts are dated well before the AI-shopping shift that dominates the other posts, and they do not address AI agents, product data, catalogue governance or how products get surfaced.
The reasoning stays general — undercutting is a risk, suppliers hold the terms — and does not anticipate the problem-first AI shopping journey that makes structured product data a campaign topic.
Using Ducie for a SaaS audience of practising operators would mean rebuilding the material around a different argument.
This page draws on four LinkedIn posts by Karl Simpson: a first-month reflection on agency-side Amazon work, the launch of The Product Data Advantage Substack, a post on AI-assisted shopping citing survey figures dated 5 May 26, and a post introducing process as the first pillar of the PREPARED ecommerce system.
The posts are short text posts and carry no publication dates, so they do not establish a timeline.
The linked PREPARED article and the Substack itself are unavailable here; no video, transcripts, long-form articles, sponsorship terms or audience data appear here.
A LinkedIn follower count of 3,638 was displayed 9 October 2026.
Comparison posts by Haider Ali, Vincenzo Toscano and Brendan Ducie appear in the similar-creator section, but do not describe Karl Simpson.