# David Dokes

David Dokes writes about AI tools for ecommerce teams, including agents for marketing workflows and custom models for ad attribution through Polar.

- Profile: https://b2binfluencer.marketing/creators/david-dokes/
- LinkedIn: https://www.linkedin.com/in/david-dokes
- LinkedIn followers: 23,871
- Topics: artificial-intelligence, ecommerce, ai-agents, dtc-ecommerce, marketing-measurement, marketing-attribution
- Based on 10 dated posts, latest 2026-09-23

## Summary

Most useful for DTC ecommerce teams wanting practical AI agent workflows for marketing analytics, attribution, and saving money.

- Best for: A DTC ecommerce brand around $5M to $800M GMV considering a talk or advisory session.
- Not for: A consumer brand seeking a paid creator placement for product demos, reach, or engagement.
- Check first: Confirm whether they accept sponsorships or paid placements, plus rates, deliverables, and audience terms.

### Takeaways

- One post lists 15 AI agent use cases already running in production DTC brands.
- An agent found $16K per day of Amazon DSP spend was not incremental.
- An apparent retention dip traced to a 3.6x spike in failed payments.

## Main ideas

- **The limit on AI agents is imagination, not capability**: Dokes argues that teams are not short of AI tools: nearly every ecommerce team they meet is already building dashboards on Claude or ChatGPT. But very few can name a task an agent could take on Monday morning. The framing is blunt: companies do not have an AI problem, they have an imagination problem. The useful work is cataloguing concrete tasks and the data each one needs, then handing them over, rather than buying more capacity. That is why they publish a series of real agent workflows instead of general advice.
- **Sort agent work by whether it unlocks money or removes work**: The examples in Dokes’s field-guide post fall into two buckets. “Unlock” cases surface money the team could not see — non-incremental ad spend, wasted search terms, a retention problem that is really a payments problem. “Reduce” cases take over work a person used to do, such as drafting campaigns or checking supplier documents. Each use case documents what the person asked, what the agent did, the output, the data and tools it needed, and where a human stayed in the loop. That structure turns a vague pitch into a task a brand can budget for.
- **Attribution should fit how customers buy, not a template or a black box**: Dokes frames the choice brands face on attribution as two bad options: rigid templates such as first-click, last-click, linear, or U-shaped, which are understandable but fixed, or black-box models that claim to adapt but offer little visibility or control. Either way, budget decisions rest on a model that does not fit the business or that nobody can explain. Their alternative is to build the model around the actual buying journey and describe the credit rules in plain language rather than configure a preset, so the result can be shown to the people who approve spend.
- **Metric definitions are company context, not a personal file**: An AI can get the calculation right and still use the wrong metric. Ask about conversion rate and you may mean Shopify’s figure while the assistant returns the one from Google Analytics. Dokes argues that a preference should not live in someone’s private assistant project or a file on their laptop; it needs to be shared across the company, with permissions controlling who can create and edit it. This makes agent setup a governance decision about a single source of truth, often the reason AI answers disagree with the numbers leadership trusts.
- **Prompt it, and let the output land where the team already works**: Across the three posts, the same delivery pattern repeats: prompt an assistant to build a custom attribution model or ask Polar to create a label, connect it to an ecommerce data platform, and send the result to a dashboard, marketing tool, or Slack message. Dokes argues that the hard part of adoption is connecting existing systems and writing clear instructions, not learning a new interface.

## Work to read

- [15 AI agent use cases from DTC brands, already running in production](https://www.linkedin.com/posts/david-dokes_15-ai-agent-use-cases-from-dtc-brands-already-activity-7508197686408372224-OU81)
- [Introducing Custom Attribution Models in Polar](https://www.linkedin.com/posts/david-dokes_introducing-custom-attribution-models-in-activity-7506748022370914304-4hUF)
- [Introducing Labels](https://www.linkedin.com/posts/david-dokes_introducing-labels-give-ai-context-on-which-activity-7508591905518075905-Fdz0)

## Brand relationships

- **Polar** (disclosed_partnership)
- **Anthropic Claude** (mention)
- **VALABASAS** (mention)

## Partnerships

The three posts are product-launch and product-process write-ups, not a sponsorship offer. David Dokes writes from inside the team that builds Polar, describing Polar Operator, custom attribution models, and shared metric Labels. Their posts do not specify rates, deliverable types, disclosure practices, or audience terms. For a buyer, the practical path is an advisory or vendor conversation about the product processes they describe, not an assumed creator placement. Ask directly whether they offer paid collaborations and, if so, for those details.

## Alternatives

- Noah King: Choose Noah King when the brand’s first question is which AI analytics or reporting tool is worth paying for, rather than how to run agents inside its own team. The reviewed work is a hands-on test of a commercial AI reporting tool: connecting Meta, Google, and Shopify accounts in a few clicks, asking which campaigns to scale or cut, then probing creative analysis and next-generation ad concepts. The write-up sorts the audience explicitly — teams wanting ad accounts, ecommerce data, and AI in one place, versus agencies and media buyers needing cohort-level nuance — and names the monthly price band. The same creator translates platform shifts, with a long plain-English walkthrough of Meta’s 2025 ranking, matching, and sequence systems and do-and-don’t lists aimed separately at enterprise businesses, large consumer brands, and small Shopify stores.
- Will O'Brien: Choose Will O’Brien when the brand’s core problem is that it does not trust the numbers it is deciding from. The reviewed work is measurement criticism with the mechanics attached: a performance lead at a large apparel brand calling affiliate ROAS "fake," the last-click and self-reported attribution chain behind a 12x blended figure, the structural split between the team running affiliate and the team running measurement, and the reported result that 20%–50% of affiliate spend was not incremental once tested. A second post applies the same reasoning to AI-assisted discovery — why recommendation-driven visits arrive as direct traffic, and why branded search becomes a downstream signal of AI visibility rather than an upstream one.
- Martin K.: Choose Martin K. when the urgent question is whether AI agents can find and buy the brand’s products at all. The posts cover the Universal Commerce Protocol and agentic commerce as a readiness checklist: activating the business-agent setting so inventory is treated as writable rather than read-only, enabling direct checkout so an agent can inject orders into the store backend, rewriting product taxonomy with conversational attributes so agents can match products to queries, and piloting direct offers. A companion post states the measurement consequence directly — an agent that skips the landing page and the pixel makes paid-driven sales look like direct traffic — and points to intent-level measurement and incrementality partners as the response.

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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/