Noah King builds and advises on data infrastructure for ecommerce and advertising. Their work focuses on connecting platforms such as Shopify, Meta, and Google to privately owned data warehouses, so brands and agencies can build their own analytics and reporting tools.
Dashboard is easy; data extraction, permissions, secure storage, reconciliation are the real work.
Analytics subscriptions are rent on data you could own.
GoMarble AI review praised creative analysis but questioned budget recommendations, including ROAS for lead generation.
Check first: Confirm current BaseStack stage, paid format, and audience breakdown before booking.
Main ideas
The dashboard is easy; the plumbing underneath is the real work · Analytics subscriptions are rent on data you could own · Meta's 2025 algorithm overhaul should change campaign structure and measurement
Noah King writes about the data infrastructure behind ecommerce and advertising analytics: how brands can connect platforms such as Shopify, Meta, and Google to data they control, then build reporting and other tools around it.
They also explain changes in Meta advertising and evaluate marketing software.
In their posts, they described BaseStack by Atten-X as an early-alpha project for owned data infrastructure.
King makes the hidden work behind analytics easier to understand.
In a post about building a marketing data stack, they distinguish the visible dashboard from the harder foundations: data extraction, permissions, secure storage, and reconciliation.
Their guide to using Facebook’s CLI tool turns that idea into a practical sequence: extract ad data, store it in a private warehouse, then build dashboards, audits, or alerts.
They also connect infrastructure to day-to-day choices.
In a review of GoMarble AI, they praised its creative analysis while questioning its budget recommendations, including using ROAS for lead generation.
That mix of systems thinking and practical scrutiny makes their work useful to marketers weighing analytics tools, custom data infrastructure, and the trade-offs behind each.
Noah King's recurring argument is that the visible layer of advertising and ecommerce analytics—the dashboard, report, or AI answer—is the easy part.
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The hard part is the data plumbing: tracking pixels, API connections to Meta, Google, and Shopify, authentication tokens, app submissions and permissions, a warehouse the brand controls, and automated extraction.
From that follows a commercial position: a monthly analytics subscription is rent paid for access to a brand's own marketing data.
It also informs their media-buying view: Meta's 2025 algorithm overhaul changes how campaigns should be structured and measured.
King explains platform machinery in plain English, tests AI tools on live ad accounts, and says who those tools are not for.
The dashboard is easy; the plumbing underneath is the real work
Across several posts, King distinguishes the visible product from the infrastructure behind it.
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Building a dashboard or vibe-coding an analytics app is the fun, easy part.
The hard part is installing a tracking pixel, pulling data from the Meta, Google, and Shopify APIs, managing authentication tokens and app permissions, securing a data warehouse, reconciling data, and automating extraction.
Non-technical teams struggle with extracting, storing, and hosting data, King says, and getting permissions wrong is risky.
One post contrasts what a skilled developer would spend a weekend building—a Meta, Google, and Shopify dashboard—with the BaseStack install King is developing, which is meant to take about 20 minutes. King identifies three pieces as the moat: a Shopify app that installs a website pixel, subscribes to webhooks, and extracts customer and order information every fifteen minutes into a private warehouse; a Facebook app that requests only the minimum permissions needed rather than exposing the whole ad account; and a Google Cloud console app approved for one-click installation of Google ad tracking across multiple accounts.
Analytics subscriptions are rent on data you could own
King argues that paying platforms such as TripleWhale, Wicked Reports, or Polar Analytics $300 or more per month amounts to renting access to a brand's own marketing numbers.
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Once the underlying infrastructure exists, the brand owns its data and can build whatever dashboards it wants, attach any AI tool, create custom attribution models, feed reporting tools, and build business-specific workflows—without paying monthly rent for access.
A post built around the question "Why are we still paying $300+ every month for this?" lists the underlying stack: the tracking pixel, API pulls, authentication tokens, a secure warehouse, reconciliation, and automation.
King cites years spent building tracking pixels, server-side data connections, and software products used by more than 1,000 Shopify brands.
They offer an early alpha of BaseStack by Atten-X to a handful of agencies or brands willing to test it.
Meta's 2025 algorithm overhaul should change campaign structure and measurement
King describes Meta's 2025 changes as four AI systems working together as one stack, rather than four separate updates: GEM, a recommendation engine for context and relevance; Lattice, a ranking system that decides which ad wins each impression; Andromeda, a matching system that pairs people with ads; and Sequence Learning, which predicts multi-touch journeys.
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King calls it the biggest coordinated shift seen from Meta in ten years and says many brands have not adapted.
The recommended moves are to consolidate campaigns, sequence funnel messaging, and feed data signals back in.
Avoid over-segmenting ads, using too little creative variation, and focusing on last-click attribution.
The post pairs three things to do with three to avoid, cites Meta's own engineering post as the primary source, and says the longer conversation tailors the advice to enterprise businesses, large consumer brands, and small Shopify stores. King offers the full write-up to anyone who comments or sends a direct message.
Test AI marketing tools on a live account, then say who they are not for
King's product writing is hands-on and audience-qualified.
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They connect real accounts, run layered prompts, report what works and what doesn't, then identify who the tool suits and who it doesn't. The for-and-not-for framing recurs, rather than serving as a verdict on any one tool.
In a review labeled a paid partnership and sponsorship, with compensation for time, King connected a Meta ad account to GoMarble AI, a chat-based tool linking Meta, Google, and Shopify accounts for AI analysis and reporting.
An initial question about which campaigns to scale or pull back produced solid high-level reporting, tables, and recommendations, but also shaky assumptions, such as using ROAS on lead generation.
The tool suggested budget changes King says an experienced buyer would never implement so quickly.
A second set of prompts about creative diversity, why certain ads worked, and second-generation concepts performed much better, returning inline images of each creative, psychological insights, and a testing plan.
The post notes the $99 to $299 monthly price and says the tool suits teams seeking simple account connections and recurring automated reports, but not agencies and professional media buyers who need cohort-level nuance and conservative budget changes.
A stated priority: help agencies and brands own the stack, offered two ways
The priority across the material is helping brands and agencies control their own data infrastructure rather than buy a managed view of it.
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King offers two routes: consulting, drawing on years as a software founder in ecommerce data systems, and BaseStack, a product that automates the install. They say building private data warehouses is the most common request they receive and describe working with a small number of early testers rather than preparing a general launch.
In one post, King describes the Facebook CLI, launched in April to connect ads to an AI agent, and recommends daily extraction into a private warehouse, with custom dashboards, campaign audit tools, and Slack alerts built on top.
King notes this can save thousands per month compared with big data software, but is hard and risky for non-technical users.
In other posts, they say BaseStack installs the same kind of stack in about 20 minutes and invite referrals from people who want to replace TripleWhale, Wicked Reports, or Polar Analytics.
The posts do not carry publication dates, so the sequence between platform commentary, consulting, and the BaseStack alpha is unclear. They show the same position delivered in two forms—a consulting service and an early-stage product—with BaseStack newly in alpha and recruiting testers.
Answers the question newcomers ask first: can Noah King explain complex ad-platform machinery in terms a non-specialist can follow?
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Noah King breaks Meta’s 2025 ad changes into four named AI systems: GEM for context and relevance, Lattice for ranking which ad wins an impression, Andromeda for matching people to ads, and Sequence Learning for predicting multi-touch journeys.
They argue the systems now work as one stack rather than four separate changes.
The post turns the technical shift into three things to do—consolidate campaigns, sequence funnel messaging, and feed data signals back in—and three to stop doing: over-segmenting ads, using too little creative variation, and relying on last-click attribution.
The longer conversation tailors the advice for enterprise businesses, large consumer brands, and small Shopify stores.
The post points to Meta’s own engineering post as its primary source and offers the full write-up to anyone who comments or messages.
For brands planning a campaign
A durable format for B2B SaaS targeting performance marketers: translate a platform change into named mechanics, pair it with a clear do/don’t list for companies of different sizes, and gate the full version behind a comment or DM that captures demand.
Shows the consulting offer in concrete terms and lays out the three-step approach behind many of their other posts.
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Noah King describes a three-step pattern built on the Facebook CLI tool, which connects ad accounts to an AI agent: run a daily extraction, store the data in a private warehouse the brand controls, then build dashboards, campaign audit tools, and Slack alerts on top.
They make clear that step three is the fun, easy part; extraction, storage, and hosting are hard for non-technical teams, and poor permissioning is risky.
King identifies as a former software founder specialising in ecommerce data systems who now consults.
They say private data warehouses are their single most common request and claim they save thousands of dollars per month versus paid data software.
For brands planning a campaign
Useful for a webinar or guest post aimed at performance marketers who own budget: the piece names a specific tool, lays out a repeatable three-step process, and says who finds each step hard—a structure that invites qualified inbound requests rather than general interest.
Explains the commercial argument behind Noah King’s work: what owned data infrastructure replaces and why the replacement takes more than asking AI to build a dashboard.
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The post separates the visible layer from the hard work beneath it.
Building a dashboard is easy, Noah King argues, but the underlying infrastructure includes a tracking pixel, data pulled from Meta, Google, and Shopify APIs, authentication tokens, a secure data warehouse, data reconciliation, and automation.
Once that infrastructure exists, the brand owns its data, can attach any AI tool, build custom attribution models, and stop paying monthly rent for access to its own marketing numbers.
King cites years of work on this infrastructure, including tracking pixels, server-side data connections, and software products used by more than 1,000 Shopify brands.
They describe an early-alpha project called BaseStack by Atten-X that is recruiting a small number of agencies or brands to test.
For brands planning a campaign
A ready-made angle for a B2B SaaS audience: frame a recurring subscription cost as a symptom, list the unglamorous components that make replacement hard, and position the offer as infrastructure ownership—with a specific call to action rather than a broad invitation.
The clearest account of what the BaseStack product does and where its claimed advantage lies.
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Noah King contrasts vibe-coding an analytics dashboard with the work beneath it: app submissions, permissions, data extraction jobs, and databases.
A skilled developer would likely spend a weekend on that, the post says, while the BaseStack install process in development takes about 20 minutes.
The post names three parts of the moat: a Shopify app that installs a website pixel, subscribes to webhooks, and extracts customer and order information every fifteen minutes into a private warehouse; a Facebook app that requests only the minimum permissions needed; and a Google Cloud console app approved for one-click installation of Google ad tracking across multiple accounts.
It closes by inviting referrals from people who want to replace TripleWhale, Wicked Reports, or Polar Analytics.
For brands planning a campaign
For a partner considering a co-marketing or integration play, this post shows which technical claims need proof and which competitors the audience already compares against—useful when deciding whether a guest post should make the category case or the implementation case.
Shows how Noah King evaluates a tool as a practitioner and frames a paid partnership for an audience of ad buyers.
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Noah King reviews GoMarble AI, a chat-based tool that connects Meta, Google, and Shopify accounts for AI analysis and reporting.
They disclose that the post is a paid partnership and sponsored, with compensation for their time.
Connecting a Meta ad account took a few clicks.
A first question about which campaigns to scale or pull back produced solid high-level reporting, tables, and recommendations, but also shaky assumptions, such as using ROAS for lead generation, and budget changes the reviewer would never implement quickly.
A second prompt about creative diversity, why certain ads worked, and second-generation concepts performed much better, surfacing inline images of each creative, psychological insights, and a testing plan.
The post divides the audience into teams seeking connected accounts and recurring automated reports, versus agencies and professional media buyers who need cohort-level nuance.
It notes the $99–$299 monthly price and compares the tool to using a chat assistant for analysis, with native account connections as the advantage.
For brands planning a campaign
For a B2B SaaS assessing fit, this is a template for a paid review that still offers useful criticism.
The for/not-for split gives a realistic read on which buyer segments a hands-on tool review can move.
The disclosed sponsorship also makes clear that any partnership should be labelled before the post is cited as an independent endorsement.
How King communicates: structured, operator-level, and direct5
Thematic direction; chronology unverified5
Dashboards are easy; the data plumbing is hard3
Owning the data stack instead of renting analytics access2
BaseStack by Atten-X: a current product priority2
Meta's AI ad stack and adapting the playbook1
Testing AI ad-analysis tools and naming their limits1
Consulting for private data warehouses and owned analytics1
Meta's AI ad stack and adapting the playbook
Noah King explains Meta's 2025 overhaul as four systems working together: GEM for context and relevance, Lattice for ad ranking, Andromeda for matching people to ads, and Sequence Learning for multi-touch journeys.
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The post gives three moves to make—consolidate campaigns, sequence funnel messaging, and feed data signals back in—and three to avoid—over-segmenting, too little creative variation, and relying on last-click attribution. They offer the full conversation by DM or comment.
For brands planning a campaign
For a B2B SaaS company selling to performance marketers, this is a ready-made webinar or guest-post angle: what the platform change means, how campaign structure should change, and how measurement should move beyond last click. King can speak to enterprise, large consumer, and small Shopify contexts, according to the post.
Testing AI ad-analysis tools and naming their limits
In a review that discloses a paid partnership, King connected a Meta ad account to GoMarble AI and tested campaign scale or pullback questions, along with creative diversity analysis.
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They report solid high-level reporting and useful creative insights when given clear prompts, but flag shaky assumptions such as using ROAS on lead generation and budget changes an experienced buyer would not make quickly. The post defines who the tool suits—teams wanting simple connections and recurring reports—and who it does not—agencies and pro media buyers needing cohort-level nuance.
Hands-on test with a live ad account, layered prompts, and explicit audience-fit criteria.
The post discloses a paid partnership, but King still includes criticisms of the tool.
For brands planning a campaign
A B2B SaaS product in ad analytics or AI reporting can use this as a model for honest, audience-specific evaluation. King is likely to test a product with real account data and surface where it breaks, so a guest post or webinar should leave room for limitations rather than a pure endorsement.
Across several posts, King argues that the visible layer—a dashboard, a vibe-coded app, an AI chat—is not the difficult part.
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The hard part is the infrastructure underneath: tracking pixels, Meta, Google, and Shopify API connections, authentication tokens, app submissions, permissions, a secure data warehouse, data reconciliation, and daily extraction jobs. They say non-technical operators struggle with these steps and that mistakes in permissions are risky.
For brands planning a campaign
For an integration platform, data warehouse vendor, or analytics SaaS, King can lead a practical webinar on implementation reality: what to connect, what to store, what to automate, and where cost and risk sit. A B2B SaaS content team targeting performance marketers can use this angle to stand out from dashboard-only messaging.
Owning the data stack instead of renting analytics access
King frames platforms such as TripleWhale, Wicked Reports, and Polar Analytics as recurring rent—$300+ per month—and argues that once a brand owns the underlying infrastructure it can build custom dashboards, attribution models, reporting workflows, and AI queries without paying for access to its own marketing data.
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They say the infrastructure includes pixels, server-side connections, and software used by more than 1,000 Shopify brands.
For brands planning a campaign
This is a strong topic for a guest post or webinar on build-versus-buy economics in ad analytics. A subscription analytics vendor should expect King's default argument to favor customer-owned infrastructure, so the angle works best when the product complements ownership—such as warehousing, governance, or implementation—rather than charging for access alone.
King describes BaseStack as an alpha-stage way to install owned data infrastructure in about 20 minutes.
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The moat includes a Shopify app that installs a website pixel, subscribes to webhooks, and extracts customer and order information every 15 minutes into a private warehouse; a Facebook app that requests only minimum necessary permissions; and a Google Cloud console app approved for one-click Google ad tracking across multiple accounts. They are recruiting a handful of agencies or brands to test it.
For brands planning a campaign
This shows King's current build priority and likely content focus: practical installation, permissions, and data ownership.
A B2B SaaS company with complementary infrastructure—warehousing, BI, identity, or reporting—could explore a joint webinar or integration story.
A direct analytics subscription platform may find itself positioned as the incumbent they are arguing against.
Consulting for private data warehouses and owned analytics
King says that after years as a software founder in ecommerce data systems, they now consult, and that building private data warehouses is the top request.
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The post points to the Facebook CLI launched in April for connecting ads to an AI agent, then recommends a daily extraction process, storage in a private data warehouse, and custom dashboards, audit tools, and Slack alerts on top. They note this is hard and risky for non-technical users but can save thousands per month compared with big data software.
For brands planning a campaign
For agencies or brands that need implementation help, King offers a consulting path. For B2B SaaS, this is a potential partnership or co-marketing opening—especially for tools that make extraction, warehousing, or alerting easier—while also being a competing service for teams that would otherwise buy a managed platform.
How King communicates: structured, operator-level, and direct
King's posts use numbered steps, do-and-avoid lists, who-it's-for and who-it's-not-for sections, architecture details, and plain-English explanations of platform or infrastructure complexity.
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The calls to action are direct: comment a keyword or DM for the full conversation, the tool link, or the BaseStack alpha. The content is aimed at marketers, founders, agencies, and developers who need to make a practical decision.
Short, structured LinkedIn posts that lead with a strong claim, explain the mechanism, list actions, and qualify the audience.
For brands planning a campaign
A B2B SaaS content collaboration with King should expect tactical, example-heavy material and candid tradeoffs, not abstract thought leadership. Plan for a clear handoff—comment, DM, or demo—and be ready to let them name where a product fits and where it does not.
Noah King's posts cover two broad modes: explaining Meta's platform changes and testing an AI reporting tool, and building or installing owned data infrastructure and BaseStack.
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The apparent direction from platform commentary toward infrastructure and product work is unverified; ask for dated examples to establish the chronology.
For brands planning a campaign
For campaign planning, confirm dates and current priorities with King before choosing an angle. If their current focus is BaseStack and owned infrastructure, lead with implementation and data ownership; if the audience wants platform education, the Meta algorithm and tool-evaluation topics remain relevant.
King's posts document two commercial relationships directly: a paid, disclosed review of GoMarble AI, and BaseStack, a data-infrastructure product they describe building with Atten-X.
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Their published analysis concentrates on Meta's ad systems, and they position BaseStack against subscription analytics platforms such as TripleWhale.
GoMarble AI
Sponsor
Ran GoMarble AI, a chat-based tool that connects Meta, Google and Shopify accounts for AI analysis and reporting, through a review.
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King reported that it produced usable high-level reporting and creative analysis but made shaky assumptions in places.
They said it suits teams wanting recurring reporting on autopilot rather than agencies needing cohort-level nuance or conservative budget changes.
The post states GoMarble AI asked for the review and compensated them for their time.
Original disclosure
Disclosure: GoMarble AI asked me to review their product and compensated me for my time, so this post is #partner and #sponsored. The impressions, including my honest and unbiased critiques, are my own.
King presents BaseStack, by Atten-X, as a data-infrastructure offering that installs a Shopify pixel, connects Meta and Google ad accounts under minimum permissions, and extracts customer, order and ad data into a warehouse the client owns.
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They say an early alpha is running and are recruiting agencies and brands to test it. The posts describe King developing and alpha-testing the product but do not state an ownership or employment structure for BaseStack or Atten-X; buyers should ask how King is connected to each.
King's breakdown of Meta's 2025 ad algorithm overhaul—GEM, Lattice, Andromeda and Sequence Learning—is a recurring subject of their writing.
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They link to Meta's own engineering post and give brands tactics such as consolidating campaigns, sequencing funnel messaging and feeding data signals back in.
TripleWhale is named as the kind of subscription analytics platform brands pay $300 or more a month for, and the kind of tool BaseStack is built to replace.
Noah King's posts explain the data work behind ad analytics, not just the dashboard on top.
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Start with the explanation of Meta's 2025 ad algorithm overhaul, then read the Facebook CLI post for how to extract ad data into a warehouse the brand controls.
The GoMarble AI review shows how King tests an AI marketing tool on a live account and where they say it falls short.
Across the posts, the through-line is that pixels, API connections, permissions, warehousing, and reconciliation are the hard part, while the dashboard or AI answer is the easy part.
The work is written for performance marketers, ecommerce brands, and agencies.
Both publish operator-level LinkedIn analysis of ecommerce analytics and AI applied to marketing measurement.
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Dokes documents AI agent workflows running inside DTC brands and custom attribution models built by prompting Claude through Polar's MCP connection, while King writes about the data infrastructure beneath ad analytics and tests AI reporting tools on live accounts.
Dokes's material centers on running AI agents and attribution inside Polar's product environment, with concrete dollar-impact workflows and product launches. Noah King focuses on owning the underlying data stack—pixels, API connections, warehouses, permissions—and Meta's 2025 ad algorithm changes.
Both address ecommerce operators on LinkedIn with practical posts about systems, AI agents, and what changes when a brand outgrows its current setup.
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McDonnell writes about the operational ceiling around £80–100K per month and autonomous agents handling inventory reorders. King argues that the visible dashboard is easy; the underlying systems are the hard part.
McDonnell focuses on ecommerce operations and growth constraints, using agent scenarios such as supplier reorders and a revenue ceiling. King focuses on advertising data infrastructure and measurement, including Meta ad algorithm changes and replacing $300-a-month analytics subscriptions.
Both write for performance marketers about measurement and attribution on Meta, and both question platform-reported metrics in favor of cleaner data.
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Reeto documents predictive CAPI and incremental attribution tests at Angler AI, while King argues brands should own the extraction and warehouse layer rather than rent analytics access.
Reeto's posts promote and explain Angler AI's predictive CAPI and incremental attribution methodology, with A/B test results. King emphasizes building owned data infrastructure and critiques subscription analytics platforms, with Meta's 2025 algorithm overhaul as a campaign-structure topic.
For a B2B SaaS campaign aimed at performance marketers, Noah King’s posts show clear topic alignment.
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Five LinkedIn posts (27 October 2025 to 25 September 2026) address brand owners, in-house ecommerce marketers, and agencies: the GoMarble review sorts the audience into “teams that want a simple way to connect ad accounts” versus “agencies and pro media buyers who need cohort-level nuance,” while the BaseStack posts speak directly to developers and agency operators with comment-or-DM calls to action.
The only measured audience figure available is a LinkedIn follower total of 15,393, observed 9 October 2026.
The posts don’t break down followers by location, age, gender, job function, seniority, or employer, and publish no per-post engagement figures (reactions, comments, reposts, clicks) or newsletter and event list sizes.
A buyer therefore cannot confirm whether the following over-indexes on performance marketers rather than adjacent audiences such as Shopify developers, agency analytics staff, or general ecommerce operators.
Before committing to a guest post or webinar, request recent results for posts on comparable subjects (owned data infrastructure, Meta/Google/Shopify APIs, attribution tooling), including impressions, comments, and who commented; any audience breakdown they can share from their account analytics or newsletter list, covering job function, seniority, industry, and location; and registration or attendance figures if a webinar is the proposed format.
If none of that exists, treat the placement as a single measured test rather than a scaled commitment.
LinkedIn followers
15,393
LinkedIn account profile (digitalnoah), account-level total in the creator source ledger · 2026-10-09
The only documented paid partnership is a compensated vendor review.
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In November 2025, Noah King published a review of GoMarble AI, a chat-based tool that connects Meta, Google, and Shopify accounts for AI analysis and reporting.
They labelled it both #partner and #sponsored, stating the vendor asked them to review the product and compensated their time.
That sets the shape of a collaboration a buyer can plan around: a named product tested hands-on and reviewed in a LinkedIn post, with the arrangement disclosed near the top.
They also promote and recruit alpha testers for their own data-infrastructure product, BaseStack, and offer consulting on private ad-data warehouses; those are their own offers, not sponsorship formats a brand can buy.
No rate card, media kit, sponsorship policy, or partnership terms appear in the posts here; ask directly about rates and terms before planning a campaign.
Compensated vendor product review
Buyer requirement
Expect a disclosed #partner/#sponsored label and an honest critique alongside the promotion: the GoMarble review praised the creative analysis and testing plan but flagged shaky assumptions, including applying ROAS to lead generation and budget changes too aggressive to implement.
Budget for the reviewer’s time rather than a fixed ad placement, and expect the deliverable to run on LinkedIn rather than a hosted blog or landing page.
Provide hands-on product access, including live connections to platforms such as Meta, Google, or Shopify; the review depended on connecting real ad and ecommerce accounts.
Decide in advance whether candid criticism is acceptable: the disclosed review format depends on it.
Strong fit on topic and format, with two conditions before committing: the product must complement data ownership rather than charge for access to it, and the placement should be treated as a measured test because the readership of Noah King’s posts has not been established.
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Brief
A B2B SaaS company wants content that resonates with performance marketers and proposes a guest post or webinar on building owned data infrastructure for ad analytics (the brief attached to this record).
Reasons
The proposed subject is one Noah King already publishes about. Three of the five posts lay out the same stack a webinar would cover: a tracking pixel, Meta, Google and Shopify API connections, authentication tokens, permissions, a private data warehouse, reconciliation and scheduled extraction jobs, with the dashboard sitting on top as the easy part.
Noah King says private data warehouses are the single most common consulting request they receive, making the brief’s topic the centre of gravity of their current work rather than an adjacent one.
Noah King already uses the format a B2B SaaS content team would want. Their posts lead with a claim, break a platform change into named mechanisms, pair it with a do list and a do-not list, and distinguish who a tool suits from who it does not. A guest post or webinar can use that structure rather than reinventing one.
A concrete narrative beat is available: the claim that a skilled developer would spend a weekend building this plumbing, against a BaseStack install process designed to take about 20 minutes. The case is backed by named components, including a Shopify app that installs a pixel and extracts customer and order data every fifteen minutes, a Facebook app requesting minimum permissions, and a one-click Google ad tracking install.
The material cites years of experience building tracking pixels, server-side data connections and software products used by more than 1,000 Shopify brands. That operational detail gives a technical webinar substance beyond a vendor pitch.
The handoff mechanics are already in Noah King’s posts. Their calls to action are direct and specific, asking readers to comment a keyword or send a direct message for the full conversation, the tool link or the BaseStack alpha. A webinar registration or gated walkthrough fits a pattern the audience is already being asked to follow.
Conditions
Position the product on the ownership side of the argument. Noah King describes $300-plus monthly analytics subscriptions as rent and names TripleWhale, Wicked Reports and Polar Analytics as products BaseStack is meant to replace. A SaaS company selling warehousing, governance, business intelligence or integration work fits that frame; one selling dashboard access on subscription would be arguing against the host.
Allow for the research and setup time the documented paid format requires, and be prepared for published criticism. The one paid collaboration in Noah King’s posts was a hands-on review using live Meta, Google and Shopify connections. It was labelled as a partnership and sponsorship, and named where the product fell short.
Verify Noah King’s current priorities and dates directly before booking. The BaseStack posts describe an alpha recruiting a small number of agencies or brands; ask what stage the project has reached by campaign time.
Do not commit to a scaled spend on audience assumptions. The only measured audience figure in scope is a LinkedIn follower total of 15,393 observed 9 October 2026. There is no breakdown by job function, seniority, employer or location, and no per-post engagement figures, so the share of performance marketers in that following is unverified.
When to choose a specialist
Poor fit as specified. This is not a budget or reach problem: the brief asks Noah King to argue against their published position and reach an audience their writing says the format does not serve.
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Brief
A managed ad-analytics subscription platform in the TripleWhale, Wicked Reports or Polar Analytics category wants to pay for a LinkedIn post promoting its dashboard to agencies and professional media buyers, with final copy approval and a positive-only message.
Reasons
The positioning is reversed. Noah King argues publicly that brands are paying monthly rent for access to their own marketing data and should own the infrastructure instead. The competitors named in that argument are exactly this category of subscription dashboard. A paid post advocating a subscription would contradict the position the audience follows them for.
Copy approval and a positive-only message would remove what made Noah King’s one documented paid placement credible. The review was disclosed as a partnership and sponsorship and still included substantive criticism, including shaky assumptions such as applying ROAS to lead generation and making budget changes an experienced buyer would never make quickly. A brief that forbids that kind of comment calls for a different creator.
The requested audience is one Noah King’s own analysis says is badly served. Their review splits the market into teams wanting simple connections and recurring automated reports, versus agencies and professional media buyers who need cohort-level nuance and conservative budget changes. It places the reviewed AI tool on the wrong side of that second group. Buying a positive review to reach that group would ask the creator to contradict their own stated caveat.
The format assumptions are also mismatched. The paid format described in Noah King’s posts involved hands-on access to live ad and ecommerce accounts, compensation for the reviewer’s time rather than a fixed ad slot, and a LinkedIn post rather than a hosted landing page. A copy-approved promotional post is a different product from the one those posts support.
Audience information is too thin to justify the spend: the posts provide no rate card, media kit or sponsorship policy, demographics or per-post engagement figures. The only figure in scope is a total follower count.
Better alternative
If the same platform still wants to work with Noah King, the credible route is a hands-on review that retains criticism, or a collaboration positioned around what the platform does that owned infrastructure does not solve, such as managed governance, reporting or support. Both keep the deliverable within the format supported by Noah King’s posts.
A worked campaign example
Angle
A B2B SaaS company co-hosts a 45-minute webinar with Noah King and publishes a companion guest post, both built around one answer delivered early: the dashboard is the easy part, and the hard part is the plumbing underneath it. Position the SaaS product as the layer that becomes useful once a brand owns its data, not as a replacement for ownership.
Audience
Performance marketers, in-house ecommerce marketers and agency operators who have been told to stop paying monthly analytics subscriptions but have not worked out what that involves.
Disclosure and access
If the work is compensated, expect the arrangement to be labelled near the top as a partnership and sponsorship, as in the GoMarble review. That review also depended on hands-on access to live ad and ecommerce accounts, so budget for account connections, a technical contact and the creator’s preparation time rather than a fixed placement.
Measurement
Run this as a measured test, not a scaled commitment. Track registrations, attendance, attendance by job function and seniority, keyword comments and direct messages, walkthrough downloads, and meeting requests; then decide whether to buy the next placement. This is the evidence needed to find out whether the following contains the performance marketers the brief targets.
Structure
Open with the claim: anyone can vibe code a dashboard; the pixel, permissions, API connections, warehouse and reconciliation are where projects stall.
Walk through the stack in order: tracking pixel, Meta, Google and Shopify API connections, authentication tokens, secure data warehouse, data reconciliation, daily extraction, then dashboards, campaign audit tools and Slack alerts.
Name the risks plainly: poor permissioning is risky, and non-technical teams can’t easily handle extraction, storage and hosting alone.
Make one concrete comparison: a weekend of skilled developer time versus a designed install process that takes roughly twenty minutes. Explain what each install component actually does.
Close with a paired do-and-do-not list, in the style of their Meta algorithm post: run a daily extraction, store data in a warehouse you control, and build audit and alerting tools on top. Don’t over-segment campaigns, run too little creative variation or rely on last-click attribution alone.
Add a who-it’s-for and who-it’s-not-for section so the audience can self-sort, following the pattern in the GoMarble review.
End with a specific handoff: ask readers to comment with a keyword or send a direct message for the walkthrough recording and a system diagram, rather than offering a general invitation to learn more.
Title
Own your ad data: what sits beneath a dashboard that survives
Why it works
Apply Noah King’s existing publishing structure to the company’s reader task rather than asking for a new format. The do-and-do-not framing, named mechanisms, plain-English explanation of platform machinery and audience self-sorting all appear in their posts, making the deliverable recognisable to the audience. It also gives the sponsoring SaaS a defensible role alongside their ownership argument, so the collaboration doesn’t read as an advertisement for the position they publicly argue against.
For a B2B SaaS company seeking a guest post or webinar for performance marketers focused on ad-data infrastructure, David Dokes is the stronger alternative when the product is an AI agent, attribution or analysis layer on top of ecommerce and ad data—not a way to own the underlying stack.
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Their posts draw on production examples: a field guide to fifteen AI-agent use cases inside DTC brands, including Amazon DSP incrementality work that cut spend 98% with sales down 2.5%, Google Ads audits, failed-payment detection, Klaviyo campaign drafts, supplier email drafting, and daily actuals-versus-forecast reporting.
Another post shows a custom attribution model built by prompting Claude through Polar's MCP, visualising buying journeys and assigning credit by prompt.
For a campaign showing performance marketers what an AI workflow does in a live ecommerce business, this is closer to the buyer's task than another explanation of pixels, warehouses and permissions.
David Dokes treats AI and attribution as the product layer, not data ownership, private warehouses or escaping $300-a-month analytics subscriptions.
A campaign that needs to persuade audiences to own the plumbing would need to supply that argument separately.
Dokes also publishes in a vendor context for Polar, so a sponsoring SaaS should check for category conflicts and not assume they will use the critical-review format seen in independent creator posts.
Ask for audience size and composition, rates, and willingness to appear in a paid webinar.
For a B2B SaaS company seeking a guest post or webinar for performance marketers focused on ad-data infrastructure, Charley McDonnell is the better alternative when the product addresses ecommerce operating systems, automation, creative production or revenue-ceiling problems rather than ad-data ownership.
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One post describes autonomous AI agents handling low-inventory detection, supplier lead-time checks, stock-out calculations, purchase-order generation and supplier messages, arguing that agents have moved from experimental to functional.
The other identifies a revenue wall around £80–100K per month, where rising CPA and falling performance are internal systems problems, not external algorithm problems: creative production must increase velocity, manual optimisation must become systematic, gut-feel decisions must become data-driven frameworks, and campaign structures should simplify rather than add complexity.
This offers a useful guest-post or webinar frame for a SaaS selling operations, workflow, creative operations or automation to growing ecommerce brands.
Charley McDonnell does not centre ad-data infrastructure, tracking pixels, API connections, warehouses or attribution reconciliation.
The campaign would need to translate the owned-data argument into an operations argument or accept a different angle.
The posts use UK revenue examples in pounds and focus on ecommerce operating-model change, not performance-marketing measurement.
Ask for audience size and job-function mix, rates, and evidence of paid collaboration experience.
For a B2B SaaS company seeking a guest post or webinar for performance marketers focused on ad-data infrastructure, Reeto M. is the better alternative when the sponsoring SaaS sells measurement, attribution, predictive CAPI, incrementality testing or customer-acquisition efficiency to performance marketers.
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Their posts address the measurement problem directly: platform-reported and blended new-customer CAC, TripleWhale, Northbeam MTA and CDP alternatives, a brand spending seven figures per month across Meta, Google and affiliate, and an experiment switching from an advanced CAPI provider to Angler AI's predictive CAPI while holding Google and affiliate spend roughly fixed.
Another post discusses Meta's incremental attribution reporting, two-cell A/B tests against Elevar, Blotout, Triple Whale Sonar, Shopify, Segment or homegrown setups, and using incremental attribution to optimise campaigns.
For a campaign whose reader task is choosing an attribution or incrementality approach, this is closer than a general owned-stack pitch.
Reeto M.'s material focuses on Angler AI's predictive CAPI and incrementality position, so a competing or adjacent SaaS may face a direct category conflict.
The content centres on measurement methodology rather than building and owning the underlying data warehouse.
It assumes a relatively sophisticated performance-marketing reader and uses technical terms such as marginal CAC, pLTV and incremental attribution, narrowing its appeal compared with a plain-English infrastructure explainer.
Ask for audience size and composition, commercial terms, and whether Reeto M. accepts sponsored content.
This profile draws on five LinkedIn posts published from Noah King's account: an explanation of Meta's 2025 ad algorithm overhaul, a review of GoMarble AI that the post itself labels a paid partnership and sponsorship, a guide to using the Facebook CLI to feed a private data warehouse, a description of what BaseStack by Atten-X installs, and the argument that $300-a-month analytics subscriptions amount to renting access to a brand's own data.
The posts do not include publication dates, so the page treats them as examples of recurring positions rather than a dated sequence, and it makes no claim to cover King's full output.
No video, transcript, podcast or third-party coverage appears here.
Posts from David Dokes, Charley McDonnell and Reeto M. provide similar-creator comparisons.
The only audience figure reported here is an account-level LinkedIn follower total of 15,393 on 9 October 2026; the posts here do not provide demographics, per-post engagement figures, a rate card or a sponsorship policy.
The campaign scenarios on the page are illustrative, not requests or endorsements from King or any brand.