5 Claude Cowork Marketing Workflows

The previous chapters were about the why and the how. This chapter is the what. Specifically, what to build first.

Every CPG marketing team has a set of high-volume tasks that consume disproportionate time. PDP audits. Content briefs. Competitive monitoring. Persona work. Performance reporting. These five workflows are the ones we install first for every brand we work with, in roughly this order. Each one stands alone. Each one compounds with the next.

For each, the trigger, the inputs, the steps Cowork runs, the MCP connections needed, and the deliverable. The point isn't that you copy our exact workflows. The point is that you see the shape of what's possible, then build the version that fits your team.

Workflow 1: PDP Audit Pipeline

The problem this solves

You need to audit a hundred product pages against a defined rubric (we use the 10-element PDP visibility rubric, but the same shape works for any scoring framework). Doing this by hand takes a full week. Doing it in Chat takes most of a day because you're re-prompting the rubric for every page. Doing it in Cowork takes an afternoon, including review.

The trigger

A client uploads their PDP catalog or shares the URLs, or you pull them from Shopify through MCP.

Inputs

• The list of PDP URLs to audit.
• The 10-element rubric as a Skill (or whatever rubric your team uses).
• Brand context from the client's Project.

Steps Cowork runs

1. Pull each PDP URL and scrape the rendered content.
2. For each page, run the audit Skill to score against ten elements: Hero Clarity, Key Facts, Product Truth, Intent Matching, Comparison Logic, Sensory Cues, FAQs, Trust & Reviews, Agentic Readiness, Frictionless Next Step.
3. Capture the evidence (or absence of evidence) for each score.
4. Compile results into a scored CSV with weak/strong/moderate ratings.
5. Draft a 2-page findings document with the three biggest patterns and the top fix recommendations.
6. Save both files to the client's Notion space.

MCP connections

• Shopify (optional, for pulling the URL list automatically).
• Notion (for delivering the report).
• Web fetch (for the actual PDP content).

Deliverable

A scored CSV plus a 2-page findings document, ready to walk through with the client.

The win

A week of manual auditing collapses to an afternoon. You can re-run the audit any time a client updates their pages.

Workflow 2: Content Brief Generator

The problem this solves

Writing a content brief is fundamentally a research and synthesis task. You need keyword research, search intent analysis, competitive overview, persona context, hook angles, and a 5-section outline. By hand, this is two to three hours of focused work per brief. At scale (we run waves of fifteen briefs at a time for some clients) that's a week of work.

The trigger

A new topic gets added to the content calendar, or you kick off a content wave with a list of topics.

Inputs

• The raw topic or topic idea.
• The target persona (from the client's Project).
• The content wave context (theme, business outcome, voice direction).
• The target keyword (or Cowork can pick one).

Steps Cowork runs

1. Run SEMRush keyword research to find the optimal target keyword and pull search volume, difficulty, and SERP features.
2. Draft a catchy title under 60 characters.
3. Analyze the top 10 ranking results to understand search intent and structure.
4. Generate three hook angle options for the writer to choose from.
5. Draft a 5-section outline with H2 headers, key points per section, and the recommended NLP keywords.
6. Create the brief record in Airtable and a corresponding page in Notion.

MCP connections

• SEMRush (for keyword research).
• Airtable (for the brief record).
• Notion (for the writer-facing brief page).
• Web fetch (for SERP analysis).

Deliverable

A complete brief in Airtable and Notion, ready for the writer, in roughly four minutes per brief.

The win

A week of brief writing collapses to a single afternoon. Briefs are also more consistent, because the Skill encodes the team's preferred brief structure.

Workflow 3: Competitive AI Secret Shop

The problem this solves

When customers ask ChatGPT, Gemini, or Claude for product recommendations in your category, your brand either shows up or doesn't. You need to know which queries trigger your brand, which trigger competitors, and how each system describes your product when it does mention you. Doing this manually means typing dozens of queries into three different AI tools and copying the responses, which nobody actually does consistently.

The trigger

Weekly cadence, or on-demand when the team wants a snapshot.

Inputs

• The category and the list of consumer-style queries to test (we usually start with 20).
• The brand and 3-5 competitors to track.
• The Project context for the brand.

Steps Cowork runs

1. Send each query to ChatGPT, Gemini, and Claude through their APIs.
2. For each response, extract which brands were mentioned, the order they appeared, and the descriptive language used.
3. Score share-of-voice across the three platforms.
4. Compare this week's results against last week's to flag changes.
5. Draft strategic recommendations: which queries you're losing, why competitors are winning them, what content gaps to fill.
6. Save the report to Notion and update the Airtable tracker.

MCP connections

• The relevant AI APIs.
• Notion (for the report).
• Airtable (for tracking results over time).

Deliverable

A weekly competitive intelligence report covering AI visibility, share of voice, and strategic recommendations.

The win

A workflow that almost no team does manually (because it's tedious) becomes a weekly automated practice. Brands using this consistently spot positioning gaps months before their competitors do.

Workflow 4: Persona Research Synthesis

The problem this solves

Real personas come from real customer voice. That means interview transcripts, support tickets, product reviews, and social comments. Synthesizing all of that into a usable persona document is a two-week project for a researcher. Most teams skip the work and end up with personas that read like demographic profiles instead of actual humans.

The trigger

A new persona project, or a refresh for an existing persona.

Inputs

• Customer interview transcripts (text or audio with transcripts).
• Recent product reviews pulled from Shopify, Amazon, or wherever they live.
• Support tickets or chat logs.
• Social comments on category-relevant posts.

Steps Cowork runs

1. Ingest all source material into the brand's Project.
2. Cluster quotes by theme: motivations, pain points, language patterns, objections, buying triggers.
3. Draft the persona document with demographic and psychographic detail, plus direct customer quotes as evidence for each claim.
4. Generate a voice samples section showing how this persona actually talks, not how marketing thinks they talk.
5. Draft an AI image prompt for the persona headshot, ready for image generation.
6. Save the persona to Notion and to the brand's Airtable Personas table.

MCP connections

• Notion (for the persona page).
• Airtable (for the persona record).
• Shopify or other source systems (for review pulls).

Deliverable

A complete, customer-voice-grounded persona document with verifiable quotes for every claim.

The win

A two-week project becomes a one-day project. The personas are also better, because they're grounded in real customer language instead of marketing's assumptions.

Workflow 5: Weekly Performance Digest

The problem this solves

Pulling weekly performance data from Shopify, Google Analytics, and Klaviyo, then formatting it into something a marketing lead can scan in two minutes, is the kind of work that takes an hour every Monday morning and produces a deliverable nobody loves. It's the perfect Cowork job. Routine. Multi-source. Predictable in shape.

The trigger

Every Monday morning, automatically.

Inputs

• Last week's Shopify performance data.
• Google Analytics traffic and conversion data.
• Klaviyo email and SMS performance.
• Optional social and paid media data.

Steps Cowork runs

1. Pull the relevant data through each tool's MCP server.
2. Compute week-over-week and year-over-year changes for the key metrics.
3. Flag any anomalies (traffic dips, conversion spikes, email performance changes).
4. Generate a 1-page summary with the top three insights and the top three things to look into.
5. Drop the digest into the team's Slack channel and a Notion archive page.

MCP connections

• Shopify, Google Analytics, Klaviyo.
• Slack (for delivery).
• Notion (for the archive).

Deliverable

A 1-page performance digest, posted to Slack and archived in Notion, every Monday by 9am.

The win

The hour-long Monday morning task becomes a four-minute review of an already-prepared digest. The marketing lead spends time interpreting and acting, not gathering and formatting.

The Through-Line

Look at the five workflows together and you see the pattern.

Each one is high-volume work. Each one is repeatable. Each one has knowable inputs and a defined output. Each one used to consume disproportionate team time. Each one runs through Cowork in a fraction of the time without sacrificing quality.

That's the template for finding workflows worth building. Look for the work that's high-volume, defined in shape, and currently eating a chunk of your team's week. Those are the workflows that earn their Cowork pipeline first.

The work that's strategic, judgment-heavy, and irregular stays in Chat, where it belongs.

What's Next

The final chapter is the one that turns all of this from theory into something running. We'll walk through building your first Cowork pipeline from scratch. Project setup, the first Skill, the first MCP connection, the test runs. You'll close the guidebook with infrastructure in place.