Documentation Index
Fetch the complete documentation index at: https://docs.datagenie.ai/llms.txt
Use this file to discover all available pages before exploring further.
The problem
By the time a renewal conversation opens, the signal that would have predicted churn is weeks old. Usage drops, support ticket spikes, NPS declines, and billing friction happen long before the customer formally disengages — scattered across product, support, billing, and CRM systems.What DataGenie autonomously detects
Engagement drops by cohort
Active-user declines per plan, industry, or cohort — surfaced as autonomous stories.
Support & billing friction
Ticket-volume spikes, first-response SLA misses, and billing-dispute anomalies connected to the same customer segments.
Multi-source early warning
Usage (product DB) + Tickets (support) + Billing (finance) connected via Nirvana — no ETL.
Cohort-level forecasts
Wisdom projects churn trajectories for specific cohorts and runs what-if scenarios.
How to set it up
Onboard each source independently
Product usage, support tickets, billing — each becomes its own Dataset via GO.
Compose a Nirvana dataset
Nirvana virtually joins the three sources at the aggregate level. No ETL, no fact-to-fact joins.
Monitor with Top Stories
Stories connect usage drops to ticket spikes to billing friction — the same customer segments showing up across multiple KPIs.
Relevant features
HyperConnected
Multi-source monitoring without ETL.
Wisdom Scenario Planning
Model intervention impact before acting.
Alerts
Route churn-risk stories to CS.