# Noah King

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.

- Profile: https://b2binfluencer.marketing/creators/noah-king/
- LinkedIn: https://www.linkedin.com/in/digitalnoah
- LinkedIn followers: 15,393
- Topics: artificial-intelligence, ecommerce, performance-marketing, attribution
- Based on 12 dated posts, latest 2026-09-25

## Summary

Most useful for explaining owned data infrastructure and scrutinizing analytics tools for ecommerce and advertising.

- Best for: Brands or agencies wanting owned ad analytics infrastructure, or SaaS on the ownership side.
- Not for: Managed dashboard subscriptions seeking positive-only promotion, or campaigns needing proven audience breakdowns.
- Check first: Confirm current BaseStack stage, paid format, and audience breakdown before booking.

### Takeaways

- 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.

## Main ideas

- **The dashboard is easy; the plumbing underneath is the real work**: Across several posts, King distinguishes the visible product from the infrastructure behind it. 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.
- **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. 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.
- **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. 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.
- **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. 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.
- **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. 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.

## Work to read

- [Meta’s 2025 ad algorithm overhaul, explained in plain English](https://www.linkedin.com/posts/digitalnoah_meta-completely-overhauled-its-ad-algorithms-activity-7388610270555856896-4UM2)
- [The Facebook CLI tool most ad buyers are missing—and the data warehouse behind it](https://www.linkedin.com/posts/digitalnoah_most-ad-buyers-are-sleeping-on-the-facebook-activity-7492952318523568128-AolD)
- [Why brands pay $300 a month for analytics—and what it takes to stop](https://www.linkedin.com/posts/digitalnoah_a-lot-of-brands-pay-300-a-month-for-platforms-activity-7506331509033963520-JQ77)
- [BaseStack’s moat: a 20-minute install versus a weekend of developer work](https://www.linkedin.com/posts/digitalnoah_you-know-whats-easy-vibe-coding-a-meta-activity-7509321985173250048-xy_f)
- [A hands-on review of GoMarble AI, with the sponsorship disclosed up front](https://www.linkedin.com/posts/digitalnoah_partner-sponsored-activity-7396261628402434048-m590)

## Brand relationships

- **GoMarble AI** (sponsor)
- **BaseStack** (disclosed_partnership)
- **Meta** (mention)
- **TripleWhale** (mention)

## Partnerships

The only documented paid partnership is a compensated vendor review. 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.

## Alternatives

- David Dokes: 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. 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.
- Charley McDonnell: 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. 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.
- Reeto M.: 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. 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.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/