Measure referral quality, not just invites

Track invite sent, invite accepted, activation, and retention so product teams know whether referrals create valuable users.

Updated · published

markdown for agents →

difficulty intermediate · time to value 5 minutes · execution on demand

Start from this

Analyze referral quality: invites sent, accepted, activated, and retained users by inviter cohort and channel.

Why this matters

Referral programs can inflate signup volume without producing retained users. Quality matters more than invite count.

What you get

  • Invite send and acceptance rates
  • Activation and retention of referred users
  • Best inviter cohorts and channels
  • Whether referral rewards are attracting quality users

Walk through it

You

Are referrals bringing good users?

Agent

I’ll follow referred users from invite to activation.

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.channel AS channel, countIf(event = 'referral_invite_sent') AS sent, countIf(event = 'referral_invite_accepted') AS accepted, countIf(event = 'activated') AS activated FROM events WHERE event IN ('referral_invite_sent','referral_invite_accepted','activated') AND timestamp > now() - INTERVAL 60 DAY GROUP BY channel ORDER BY accepted DESC"

The output

The agent returns referral channels and inviter cohorts ranked by activation quality.

Setting it up

Emit referral lifecycle events with stable inviter and invitee identifiers. Preserve referral source onto activation.

Variations

  • “Show referrals by power-user cohort.”
  • “Find inviters whose referrals activate above average.”
  • “Publish referral quality by channel.”

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.