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Research and Intelligence

Run automated cohort and retention analyses

AirOps MCP queries your data warehouse to build cohort retention tables, calculates week-over-week and month-over-month retention curves, and surfaces the product events most correlated with long-term retention. Outputs are formatted as readable summaries for leadership and detailed breakdowns for product teams — all delivered automatically.

Process

#1

Connect your data warehouse

Link Snowflake or BigQuery. AirOps needs a user events table with timestamps and a user identifier to build cohort tables.

#2

Define your cohort parameters

Set your cohort grouping (signup month, acquisition channel, plan type) and the retention milestones to track (Day 7, Day 30, Day 90).

#3

Identify your key activation events

Select the product events to correlate with retention — feature usage, integration setup, or any custom event in your warehouse.

#4

Review the first cohort output

Validate the cohort tables and retention curves. Confirm the activation event correlations make sense before scheduling delivery.

#5

Schedule recurring delivery

Set the delivery cadence and destination — weekly to a Slack channel, monthly to a Notion page, or both.

Cohort tables built automatically from raw warehouse data; Retention curve analysis with trend narratives; Early churn signals surfaced before customers cancel; Scheduled delivery to Slack, Notion, or email

Key benefits

Publish 10,000+ pages from a single workflow run

Cohort tables built automatically from raw warehouse data; Retention curve analysis with trend narratives; Early churn signals surfaced before customers cancel; Scheduled delivery to Slack, Notion, or email

Connected tools

Ahrefs
Snowflake; BigQuery; Amplitude; Mixpanel; Notion
Ready to build?

Start this workflow in AirOps — no code required.

Try this use case
data-reporting; data-anomaly-detection; support-ticket-routing

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