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Python Notebook · Query
Query
Monthly Signups
Tracking signup velocity since the January product launch.
Signups grew 150% since launch, with the strongest acceleration in Q2.
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Python Notebook · Analysis
Revenue Analysis — Q4 2025
Investigating the revenue dip in November. We examine three hypotheses: seasonal churn patterns, the impact of the Nov 3rd pricing change, and a shift in acquisition channel mix.
Feb 28, 2026 · 2:41 PM · 36 cells · 8 visualizations
Revenue Trajectory
Tracking weekly revenue from October through January to pinpoint exactly when the dip began and how steep the recovery has been.
Weekly Revenue (Oct 2025 – Jan 2026)
Churn Rate by Plan Tier
Breaking down post-pricing-change churn by plan reveals the Starter tier absorbed nearly all of the shock — while Enterprise and Team plans remained stable.
30-day churn rate after Nov 3rd pricing change (%)
Key Finding
The November dip correlates with the Nov 3rd pricing change. Starter plan cohorts show 2.3× higher churn in the 30 days post-change. However, post-change cohorts show 18% higher average revenue per customer, suggesting the new pricing will net positive by Q2 2026.
What will you ask your data?
“Show me monthly revenue by acquisition channel for the last 6 months. Break it down by plan tier and highlight any channels with declining ARPU.”
Try this prompt“Connect to our Snowflake warehouse and build a dashboard showing daily active users, retention by weekly cohort, and feature adoption rates. Publish it so the team can bookmark it.”
Try this prompt“Pull our Stripe charges and subscriptions data. Build a churn analysis notebook with cohort retention curves, a summary of which pricing plans retain best, and exportable charts for the board deck.”
Try this prompt“I uploaded a CSV of 2,000 NPS survey responses. Analyze sentiment by question, cluster the open-text answers into themes, and create a shareable report with the top 5 action items.”
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