Know who is about to leave before they do.
Ebenezer's digital organism monitors behavioral and engagement signals across your customer base to surface at-risk accounts and trigger retention workflows before churn happens.
TL;DR
Churn prediction automation continuously aggregates product usage, support, and engagement signals into a risk score for each account and triggers retention workflows when scores cross configurable thresholds.
Last updated: 2026-03-12
Definition
Churn prediction automation is a monitoring process in which a digital organism collects and aggregates behavioral signals from product usage logs, support systems, and CRM activity into a composite churn risk score for each account on a rolling basis. When a score crosses a defined threshold, the system triggers a configurable retention workflow and notifies the responsible account owner.
Industry context
Why this matters
Acquiring a new customer costs 5 to 7 times more than retaining an existing one (Harvard Business Review, cited broadly since 2014)
A 5% increase in customer retention can increase profits by 25 to 95% (Bain and Company, widely cited)
72% of customers who churned gave no explicit warning signal in the 30 days before cancellation (Totango, 2022)
The median early churn indicator appears 47 days before a cancellation event in B2B SaaS (Gainsight, 2023)
Only 42% of CS teams have a documented early-warning process for churn risk (TSIA, 2023)
The problem
What teams deal with today
CSMs have too many accounts to manually monitor for early warning signs of churn
Churn prediction models built by data teams go stale and are rarely integrated into operational workflows
Retention actions start too late because risk signals are only reviewed during QBRs or renewal prep
How it works
The Churn Prediction Automation workflow
Connects to your product analytics, CRM, and support platform to collect behavioral signals
Computes a composite churn risk score for each account on a daily or real-time basis
Surfaces the highest-risk accounts in a prioritized dashboard for your CS team
Triggers a configurable retention workflow when a score crosses a threshold
Logs risk score history over time so trends and inflection points are visible to account owners
Integrations
Works with your existing stack
The AI organism connects to the tools you already use, building context from every interaction.
Common questions about Churn Prediction Automation
What signals does Ebenezer use for churn prediction?
The digital organism is designed to ingest any signal your systems produce. Common inputs include daily active user counts and trend, feature adoption breadth, support ticket frequency and sentiment, response rate to CSM outreach, executive engagement with product updates, and NPS trajectory. The weighting of each signal is configurable and can be tuned based on which signals have proven most predictive for your specific product and customer segment.
Does Ebenezer build its own prediction model or use rule-based scoring?
The default configuration uses a weighted rule-based scoring model that you control and can audit without data science involvement. Signal weights, threshold values, and decay rates are all configurable through the settings interface. For teams with existing churn models built in Gainsight, Totango, or a custom data warehouse, Ebenezer can consume the output score from those systems and use it as the trigger for its retention workflows.
How does the system avoid overwhelming the CS team with false positives?
Threshold sensitivity is configurable. You can set the score threshold at a level that balances precision against recall for your team's capacity. The system also allows suppression rules, for example excluding accounts that just completed onboarding or are in an active upsell conversation, to reduce noise. Over time, as your team marks risk alerts as false positives, those patterns can inform threshold adjustments.
What does the retention workflow look like once a risk threshold is crossed?
Ebenezer creates a task for the responsible CSM with a summary of the triggering signals, routes an internal alert to the CSM manager if the account exceeds a revenue threshold, and optionally sends a proactive check-in message to the account contact. The specific workflow steps, message templates, and escalation paths are all configurable per customer segment and risk severity level.
Ready to automate churn prediction automation?
Your AI organism learns your workflows, runs them autonomously, and gets permanently better every week.
Get started free