Cohort and Retention Intelligence Without an Analytics Engineer

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.

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.