Detect lifecycle email fatigue

Correlate email volume, opens, clicks, unsubscribes, activation, and churn to find where lifecycle messaging helps or hurts.

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markdown for agents →

difficulty intermediate · time to value 7 minutes · execution on demand
#analytics #email #lifecycle #churn #marketing #retention

Start from this 

Analyze lifecycle email fatigue: sends, opens, clicks, unsubscribes, activation, and churn by sequence and user cohort.

Why this matters

Lifecycle email can activate users or annoy them into leaving. The difference is visible when email events are joined with product behavior.

What you get

  • Sends, opens, clicks, and unsubscribes by sequence
  • Activation after email clicks
  • Churn or inactivity after high-volume sequences
  • Recommendations to pause, rewrite, or segment messages

Walk through it

You

Are our lifecycle emails helping or hurting?

Agent

I’ll compare email engagement with activation and churn signals.

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.sequence AS sequence, countIf(event = 'email_sent') AS sent, countIf(event = 'email_clicked') AS clicked, countIf(event = 'email_unsubscribed') AS unsubscribed FROM events WHERE event IN ('email_sent','email_clicked','email_unsubscribed') AND timestamp > now() - INTERVAL 30 DAY GROUP BY sequence ORDER BY sent DESC"

The output

The agent returns sequences to keep, improve, or stop. It should segment by lifecycle stage before making broad recommendations.

Setting it up

Forward email provider webhooks into Agentry as analytics events. Keep distinct_id stable with product events.

Variations

  • “Show onboarding emails only.”
  • “Find messages with high clicks but low activation.”
  • “Draft improvements for the worst-performing sequence.”

Related workflows

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Email click to first in-app action conversion

Join UTM-tagged email opens to the in-app action they were meant to drive. Grade onboarding emails by behavior change, not click rate.

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Prepare a cancellation survey for human review

Verify the cancellation signal, review a minimal survey and targeting rule, and summarize bounded responses without inventing benchmarks or contacting customers.

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Measure landing-page source quality

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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.
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