Calculate campaign payback by cohort

Tie acquisition campaigns to paid conversion, revenue, and retention so marketing and finance can see which campaigns pay back.

Updated · published

markdown for agents →

difficulty advanced · time to value 10 minutes · execution on demand

Start from this

Calculate campaign payback by signup cohort: campaign, spend if available, paid conversions, revenue, retention, and payback period.

Why this matters

Campaign dashboards stop at leads. Product-led teams need to know which campaigns create activated, retained, paying users.

What you get

  • Campaign cohorts by signup week
  • Paid conversion and revenue
  • Retention by campaign
  • Payback estimate when spend is available

Walk through it

You

Which campaigns actually paid back?

Agent

I’ll group signup cohorts by campaign and join revenue events.

Endpoint: POST /v1/projects/:project_id/analytics/query
Exact shape: /v1/openapi.json?path=%2Fv1%2Fprojects%2F%7Bproject_id%7D%2Fanalytics%2Fquery&method=post
Custom HogQL goes in the OpenAPI-defined `query` field.
Concept fields:
  query: "SELECT properties.utm_campaign AS campaign, countIf(event = 'signup_completed') AS signups, countIf(event = 'subscription_started') AS paid, sumIf(toFloat(properties.amount), event = 'subscription_started') AS revenue FROM events WHERE event IN ('signup_completed','subscription_started') AND timestamp > now() - INTERVAL 90 DAY GROUP BY campaign ORDER BY revenue DESC"

The output

The agent returns campaigns ranked by quality and revenue, not just signup count.

Setting it up

Persist campaign attribution onto signup and revenue lifecycle events. If spend is not in Agentry, the agent can still rank revenue and conversion.

Variations

  • “Only compare campaigns from last quarter.”
  • “Show campaigns with high signups but low paid conversion.”
  • “Publish this as a finance review dashboard.”

Adapt this workflow in your own agent.

Ask your agent to map the starter prompt to your saved signal map, live events, and the current HTTP API before answering.

Agentry gives coding agents production context from errors, product analytics, and deploys through one HTTP API. Fetch and read https://agentry.sh/install.md completely, then install Agentry's full first-time telemetry baseline in this repository. I authorize device-code login, saving the owner key only to ~/.agentry/credentials.json, creating one Agentry project for this repository, and read-only inspection of shipped source plus hidden CI/provider configuration. Deeply analyze the product's business logic and complete value flow before choosing telemetry. Save the complete source-backed plan and show me its exact contents and hash. Until I explicitly approve that plan or request a replacement, do not edit app/CI source, place runtime/CI credentials, commit, exercise product traffic, start proof, push, or deploy. After approval, I authorize only the reviewed targets: place the required scoped browser/server/CI credentials through the established environment or secret mechanism, preserve existing telemetry, implement and test the baseline, commit it, push that reviewed commit when the shipped CI/provider path requires it, exercise safe proof paths with test/non-customer data, and perform one deployment through the reviewed shipped CI/provider path. Ask first if proof would charge money, contact a third party, change real customer data, or require new external access. After the plan is saved, immediately before every onboarding state-changing POST, GET current onboarding state, perform only its single returned next_action, then read state again; do not batch or infer later stages. Continue until status is verified, installation_complete is true, and next_action is null. Keep all secrets, source snapshots, proof markers, and scratch files outside the repository.

+ Full access
5.5 Extra High
  1. 1. Open your repo in Codex, Claude Code, Cursor etc.
  2. 2. Paste the install prompt.
  3. 3. Your agent reads the install doc and shows you an implementation plan for approval.