Cohort and Retention Intelligence Without an Analytics Engineer
A growth team got live cohort retention and revenue-at-risk tracking across three disconnected systems — in 3 weeks, without a data engineering ticket.
Multi-channel e-commerce, weekly live cohort refresh, zero data engineering tickets.
At a Glance
Key Metrics
Data Freshness
30–60d → weekly
How stale the data behind a budget decision was allowed to get.
Time to Live
22 days
Read-only connection to first live cohort dashboard.
Engineering Tickets
Zero
Engineering tickets filed to keep it running.
The Situation
A mid-market e-commerce operator had transactional data in their database, customer attribute and acquisition data in a marketing platform export, and subscription/renewal event data in a separate system. The growth team needed weekly cohort retention rates, revenue-at-risk from churning segments, and acquisition channel attribution — all cross-source. The analytics engineering backlog meant these reports were built once and never maintained. Decisions on acquisition budget allocation were being made from data that was 30–60 days stale.
Data sources
Transactional DB
Order and purchase history
Marketing Platform Export
Customer attributes & acquisition source
Subscription System
Renewal and churn events
Failure events
What Was Breaking
Why manual operations couldn't scale
Retention math required three systems that never lined up.
Transactions, acquisition and attribution data, and subscription events lived in three places with no shared pipeline connecting them.
Reports were built once and abandoned.
Each cross-source question competed with the analytics engineering backlog — by the time a report shipped, the business question behind it had usually already shifted.
Budget decisions ran 30–60 days behind reality.
A channel whose retention had quietly collapsed a month ago could still be receiving budget today, because nothing was flagging the drop.
The Approach
Connect your sources
Transactional DB, marketing export, and subscription system connected read-only — no engineering ticket.
Configure cohort rules in plain English
Retention windows, attribution logic, churn thresholds — written by the growth lead, validated by Autonmis.
Autonmis runs the pipeline weekly
Cohort construction, retention calculation, and dashboard publish run automatically. Query anytime on demand.
After
Transactional DB
Order and purchase history
Marketing Platform Export
Customer attributes & acquisition source
Subscription System
Renewal and churn events
Autonmis
Governed Intelligence Layer
Knowledge Base
rules · thresholds · logic
Connected to all three sources. The Knowledge Base was configured with cohort definitions, retention window rules, revenue attribution logic, and churn threshold definitions — written in plain English by the growth lead, validated against the schema by Autonmis. A multi-step pipeline ran weekly: source refresh, cohort construction, retention calculation, revenue-at-risk roll-up, dashboard publish. The growth lead could ask Autonmis "which acquisition channel has the worst 90-day retention in the last three cohorts?" and receive a grounded, SQL-executed answer with the underlying data visible and auditable. No ticket to data engineering. No waiting for the next sprint.
The Workflow
Results
30–60 days stale → weekly refresh
Data freshness for retention analysis
Same-day refresh available on demand
Audited against the analytics-engineering-backlog cadence measured before connection.
Zero
Engineering tickets required after initial setup
Growth lead runs queries and pipeline independently
Confirmed across the period since go-live.
Live from week 3
Growth team self-serve query capability
Cross-source questions answered with auditable SQL
22 days
Time to first live cohort dashboard
From disconnected sources to production intelligence
Eliminated
Decisions made from stale data
Acquisition budget now allocated on current cohort data
Governance Note
The growth lead's cohort and churn definitions live in the Knowledge Base, not in one analyst's spreadsheet formulas — so 'which channel has the worst 90-day retention' gets the same validated answer whether it's asked in week one or week twenty.
Implementation
Time to live
3 weeks
Sources connected
3
Engineering dependency
Zero
Ready to see it in your stack?
We can scope your use case to a live workflow
in the first session.
Three sources. No engineering dependency. First automation in under three weeks.
Book a 30-minute callContinue reading
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