Free for 30 days on Scale.Start free
Run9 steps4-8 weeks (most of it is waiting on conversion + churn data)

Run a pricing experiment without breaking trust

A pricing experiment with a control + treatment cohort, statistically valid sample, grandfathered existing customers, and a defensible decision documented for the team. Either: a price increase that ships without churn, or a clear signal that the change isn't worth it.

A pricing experiment with a control + treatment cohort

A pricing experiment with a control + treatment cohort, statistically valid sample, grandfathered existing customers, and a defensible decision documented for the team. Either: a price increase that ships without churn, or a clear signal that the change isn't worth it.

Spin up an agent for the heavy lifting

Drafts the customer comms (in-app banner, email, FAQ) for grandfathered customers + new pricing rollout.

9 steps, 6 official links, 3 agent prompts

Every external doc the agent needs to cite is pre-loaded into the workspace's Pointers table. No hunting for the right URL mid-draft.

What's inside

Pre-loaded so day one is execution.

5Surfaces
9Steps
3Agent prompts
6Official links
6Tools mapped
Surfaces
  • tableCohorts
  • docPricing experiment plan
  • tableComms
  • tablePointers
  • docStatus
How the loop works

Your agent works. Dock shows you what happened.

Open this template and you get a workspace seeded with an agent prompt. Connect your agent — Claude via our MCP, Cursor, your own setup — and it reads, drafts, and posts updates as it goes. You watch Dock for the latest.

  1. 01

    Connect your agent

    Claim an agent invite at trydock.ai/agent-invites — your agent gets an API key scoped to this workspace. Paste the key into Claude Desktop, Cursor, or any MCP client.

  2. 02

    Your agent reads the workspace

    The agent prompt at the top of the workspace tells your agent its role, the cadence to follow, and the surfaces to update. No extra setup — open Dock and your agent already knows what to do.

  3. 03

    Watch Dock for the latest

    Your agent posts to the Status surface after every meaningful action — newest at top. Wire the workspace's webhooks to Slack or email to get pinged in real time.

Wire it up · Claude Desktop

Add Dock as an MCP server in 30 seconds.

{
  "mcpServers": {
    "dock": {
      "command": "npx",
      "args": ["-y", "@trydock/mcp"],
      "env": {
        "DOCK_API_KEY": "<paste from /agent-invites>"
      }
    }
  }
}

Drop into ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or the equivalent on Windows / Linux. Restart Claude Desktop. Ask Claude:“Read trydock.ai/<org>/run-a-pricing-experiment and follow the agent prompt.”

FAQ

Common questions on this template.

How big does a pricing experiment need to be?
Sample size depends on the effect size you want to detect. For a 10% conversion delta at 95% confidence with a 5% baseline conversion, you typically need 5000-10000 visitors per arm. For B2B with 100-500 visitors/month, that's 6-12 months — usually too long. Two fallbacks: (1) test bigger price changes (20-30%) which need smaller samples to detect, (2) skip the A/B and use gradual rollout by signup date with cohort comparison instead.
Should I tell customers I'm running a pricing experiment?
No, not while it's running. Do tell them when you ship the change publicly. Pricing experiments are short-term, opaque, and standard practice — disclosing them mid-flight invites confusion + competitor screenshotting. What's NOT okay: showing different prices to customers who can compare notes (B2B teams sharing internal docs, public Twitter, etc.). Limit the test scope to anonymous visitors.
How do I raise prices on existing customers without losing them?
Default: don't. Grandfather indefinitely. Existing customers churned to a price increase are 2-3x more painful than the incremental revenue. If you must raise on existing: 60-90 days notice, clear reason, generous transition (12 months at old price), and a manual outreach to top accounts. Even with all of that, plan for 5-15% additional churn in the affected segment.
Can my AI agents help run a pricing experiment?
Yes. Agents are good at: modeling pricing math against historical conversion + churn data, drafting customer comms across audiences, watching cohort metrics + flagging churn drift, surfacing which segments are price-elastic. Not good at: making the strategic call (ship / kill / iterate) or judging the soft signals (sales rep frustration, social-media tone). Use them to keep the analysis honest.
What's the most common mistake teams make with pricing changes?
Three in order: (1) raising prices on existing customers without grandfathering, then spending 6 months apologizing, (2) calling the experiment too early because week-1 results 'look great' and missing the month-2 retention drop, (3) skipping the comms work and surprising customers with a price change in their next invoice. All three are preventable; this template's middle steps exist to prevent them.

Open it. Hand it to your agent. Ship.

One click mints a fresh workspace in your org with the template body seeded. Your agents, your team, your edits from there.

About this template

Curated by the Dock team at . Every template is a real shared workspace we run with our own agents before publishing.

Reviewed regularly by the Dock team. Each playbook step links to the upstream tool's official docs so we can re-verify the rules as platforms change.