AI churn prediction software that scores renewal risk from every account signal and triggers intervention playbooks 90+ days before cancellation.
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60–70% of B2B SaaS churn is predictable 90+ days ahead — signals sit in usage, NPS, and billing long before cancellation (OpenView). Only 28% of CS teams run systematic prediction (Gainsight); for the rest, 70–80% of the intervention window closes before risk surfaces.
Most CS teams learn about risk from the customer — 30 days before contract end, when root-cause resolution is impossible. The AI agent surfaces risk 90–120 days early, when the full retention toolkit is still available.
Without AI, enterprise accounts get weekly reviews while SMB tiers — 40–60% of total count — get only reactive support, accumulating silent churn. The AI agent monitors every account continuously.
CRM renewal pipelines run on rep optimism, not signals. AI probability-weighted forecasts improve accuracy 15–25 points over stage-based estimates, reducing the planning variance that forces conservative headcount decisions.
Six capability pillars that help CS and revenue operations teams protect ARR and drive NRR above 110%.
Six capability pillars that help CS and revenue operations teams protect ARR and drive NRR above 110%.
Scores every account across 50+ usage, CRM, support, billing, and sentiment signals — delivering real-time renewal risk probability, not weekly batch snapshots.
Monitors DAU/MAU, feature adoption, seat utilisation, and workflow completion rates — flagging deviations before they become irreversible.
Configurable playbooks for each risk tier — usage recovery, champion departure, competitive defence, executive re-engagement, and renewal acceleration.
Tracks login frequency by role, executive vs. end-user adoption gap, multi-threading score, and single-contact dependency risk per account.
Native connectors for Salesforce, HubSpot, Gainsight, and Totango — bi-directional sync of account health, opportunities, contacts, and activity.
Real-time ARR risk dashboard: renewal pipeline by risk tier, AI probability-weighted forecasts, and intervention coverage by segment.
At $50M ARR with 8% churn, $4M lost requires $20M–$28M in new bookings to replace. The AI Renewal Risk Agent monitors every signal across every account, triggering playbooks 90–120 days before risk becomes confirmed churn. Book a renewal risk assessment.
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A static health score is a weekly composite on a handful of signals. The AI Renewal Risk Agent monitors 50+ signals continuously, weights them using ML trained on your actual churn outcomes, and acts autonomously — triggering playbooks and escalating to account leadership without waiting for a CS manager to notice.
50+ signals across five domains — Product usage (DAU/MAU, feature adoption, session frequency), CRM engagement (CS touchpoints, QBR recency), Support (ticket volume, SLA compliance, sentiment), Financial (payment timing, billing vs. commitment), and Relationship (champion stability, LinkedIn activity, multi-threading score).
Champion departure raises churn probability 40–60%. The agent simultaneously monitors CRM inactivity, LinkedIn job changes, email delivery failures, and conversation intelligence drop-off. When signals cross a departure threshold, the account is flagged immediately so CS can build relationships with alternative stakeholders before renewal.
Configured in a no-code builder — no engineering required. Standard tracks: Usage Recovery, Champion Departure Response, Competitive Displacement Defence, Executive Re-engagement, and Renewal Acceleration. Every action is a CSM-reviewed draft; the agent never sends without human approval.
CRM: Salesforce, HubSpot. CS platforms: Gainsight, Totango, ChurnZero. Product analytics: Mixpanel, Amplitude, Segment, Pendo, Heap. Support: Zendesk, Intercom, Freshdesk. Conversation intelligence: Gong, Chorus, Clari. Billing: Stripe, Chargebee, Recurly, Zuora. Custom sources connect via REST API and webhooks.
CRM stage-based forecasting ("Commit", "Best Case") ignores the usage and support signals that actually predict intent. The agent replaces static stages with signal-weighted renewal probability across 30/60/90-day cohorts — continuously backtested and recalibrated each renewal cycle.
6–8 weeks: API integration and historical data pull (Weeks 1–2), model training and validation (Weeks 3–4), playbook configuration (Weeks 5–6), phased go-live and CS training (Weeks 7–8). Prerequisites: API access to CRM, product analytics, and billing; 18 months of renewal outcome data; a CS or RevOps project sponsor.
Bonami deployments consistently achieve 30–50% reductions in preventable churn within 12 months — recovering $840K–$1.2M ARR annually for a $30M ARR company at 10% churn. CS monitoring time drops 40–60%, forecast accuracy improves 15–25 points, and first-year ROI runs 300–500% with investment recovered in 3–5 months.
