Identify early behaviors associated with retention

Compare predefined first-window product behaviors with later retained activity, quantify associations, and design a validation step without declaring a magic cause.

Updated July 10, 2026 · published May 16, 2026

difficulty advanced · time to value 15 minutes · execution human review required

Compare predefined behaviors in the first 24 hours with a source-backed retained-activity event in a later window. Show cohort sizes and retention rates, control obvious segments, and label every result as association until validated.

Why this matters

Teams want an early behavior that signals a user has reached value. Historical retention differences are useful clues, but they are vulnerable to selection bias: already-motivated users often both complete more actions and retain.

Use this workflow to identify candidate behaviors and design validation. Do not turn the highest retrospective rate into a causal product rule.

What you get

Cohort sizes and later retained-activity rates for predefined early behaviors
Baseline and major-segment comparisons
Coverage, confounding, and identity caveats
A frozen candidate definition and validation plan

Walk through it

You

Which first-day behaviors are associated with meaningful activity four weeks later?

Agent

I will verify the signup, candidate, and retained-activity events, then compare complete historical cohorts. I will not call the strongest row causal.

GET /v1/projects/:project_id/signal-map
GET /v1/projects/:project_id/onboarding
# Require status: "verified"; use live event/property reads for the selected signals.
GET /v1/projects/:project_id/event-names

Endpoint: POST /v1/projects/:project_id/analytics/query
Query plan:
  - choose complete signup cohorts with a full later observation window
  - mark predefined candidate events completed within the first 24 hours
  - define retained activity from one approved value event in the later window
  - return cohort size and retained count by candidate and major segment
  - include the all-signup baseline; return no raw identities

{
  "rows": [
    { "candidate": "integration_connected", "cohort_size": 142, "retained_users": 87, "retention_pct": 61.27 },
    { "candidate": "team_member_invited", "cohort_size": 98, "retained_users": 54, "retention_pct": 55.10 },
    { "candidate": "all_signups", "cohort_size": 620, "retained_users": 211, "retention_pct": 34.03 }
  ]
}

The output

Early-behavior association review — <cohort and observation bounds>

Observed
- <candidate>: <cohort size>, <retained count>, <rate>
- baseline: <rate>
- segment stability: <result>

Limitations
- <selection bias, identity gaps, sparse sample, instrumentation change>

Candidate to validate
- frozen behavior and window: <definition>
- validation: later holdout cohort or prospective experiment

Setting it up

Define retained activity as a source-backed value event, not “any event.” Use a stable identity across signup, early behavior, and the later window. Exclude internal, test, synthetic, and automation traffic and preserve exact cohort bounds so the analysis can be reproduced.

Do not instrument every conceivable action solely to search for a lucky correlation. Start from a small set of plausible value behaviors derived from the product’s business logic.

Variations

“Repeat the frozen definition on the next complete cohort.”
“Compare the association by plan or acquisition source without pooling unlike users.”
“Use eight-week retained activity and report how the result changes.”
“Design an experiment to test the onboarding intervention; do not launch it.”

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