Stop fighting blind. Deploy an AI Customer Analytics Agent to predict churn before it happens, calculate lifetime value, and segment users by real behavior 24/7.
The AI Customer Analytics Agent sits on top of your existing data stack (CRM, Data Warehouse, Product Analytics). It continuously analyzes user behavior patterns to predict churn risks, uncover high-value segments, and trigger proactive retention workflows automatically.
Start predicting churnIdentifies hidden drop-offs in usage patterns and alerts your success team weeks before the customer actually cancels.
Calculates projected lifetime value for new cohorts dynamically based on early engagement signals.
Automatically groups users by behavior (e.g., "Power Users", "Slipping Away") and syncs lists to marketing tools.
Most companies collect terabytes of customer data but lack the analytical resources to turn it into proactive action.
Customers cancel suddenly without warning because support teams missed the subtle, early signals of declining feature usage.
Customer Success only engages accounts when they hit a renewal period or submit a cancellation ticket—when it's too late.
Support data lives in Zendesk, billing in Stripe, and product telemetry in Amplitude, making it impossible to see the full customer picture.
Marketing relies on outdated, static CSV exports to target users, leading to irrelevant messaging and high unsubscribe rates.
Executives review static BI dashboards that report what happened last month, rather than predicting what will happen tomorrow.
Acquisition budgets are wasted on low-value cohorts because the business can't accurately forecast long-term lifetime value.
Create specialized AI agents tailored to specific analytical and retention workflows.
Monitors product telemetry and support sentiment to flag accounts with a high probability of cancelling in the next 30 days.
Calculates predictive customer lifetime value by analyzing early engagement signals, helping marketing optimize acquisition spend.
Automatically groups users into dynamic cohorts (e.g., "At Risk", "Champions", "Needs Onboarding") and syncs to email platforms.
Reads thousands of support tickets, NPS comments, and social mentions to identify emerging frustration points in the product.
Scores every user in real-time on their likelihood to upgrade to a premium tier, sending the hottest leads to sales.
Recommends the specific email, offer, or CSM intervention most statistically likely to prevent a specific user from churning.
Deploying an AI Analytics Agent is straightforward. Connect your data, define your goals, and let the AI build the predictive models.
Start BuildingSecurely link RhinoAgents to your Data Warehouse (Snowflake, BigQuery), CRM (Salesforce), and Product Analytics (Amplitude).
Tell the AI what "Churn" and "Conversion" look like for your specific business model.
The agent analyzes historical data to identify the hidden behavioral patterns that precede churn or upsells.
Create rules: "If Churn Risk > 85%, ping the assigned CSM in Slack and enroll the user in the Win-Back email sequence."
Use the natural language interface to ask complex analytical questions and view real-time ROI dashboards.
Customer Success only realizes an enterprise account is unhappy when they receive a cancellation notice via email.
The agent alerts the CSM 6 weeks in advance because the account's daily active usage dropped below their historical baseline.
Marketing sends the same generic "We Miss You" blast to 10,000 inactive users, achieving a 1% open rate.
The agent dynamically segments users by their specific drop-off reason, allowing marketing to trigger highly relevant, automated interventions.
Answering "What is the LTV of users acquired via LinkedIn vs Google?" requires a data engineer to write a complex SQL query taking 3 days.
A marketer asks the agent the question in plain English and receives an instant answer with an interactive chart.
Real outcomes companies see when they leverage AI for predictive customer analytics.
Average reduction in churn
Increase in LTV forecasting accuracy
Faster access to insights
SQL queries required
Salary and benefits to build, train, and maintain custom ML models and SQL dashboards internally.
Pre-trained models that deploy in minutes, democratizing predictive insights for the entire team.
Immediate capital savings
$120,000+ / year
While preventing hundreds of thousands of dollars in churned MRR.
Every feature is designed to eliminate guesswork and turn raw data into measurable retention actions.
Leverage advanced machine learning out-of-the-box. The AI trains on your historical data to predict future behaviors with extreme accuracy.
Don't wait for monthly reports. Get instantly notified via Slack or email when a high-value account exhibits churn behavior.
The agent synthesizes data from Stripe, Zendesk, Salesforce, and Amplitude to create a unified 360-degree customer profile.
Ask questions in plain English instead of writing SQL. The AI understands context and returns instant answers and charts.
User segments update dynamically in real-time as behaviors change, ensuring your marketing tools always target the right people.
Your data remains in your warehouse. We use secure API connections and adhere strictly to SOC 2, GDPR, and enterprise privacy standards.
Deployed Churn Prediction Agents. CSMs received early alerts when key accounts stopped using core features, allowing proactive interventions that saved millions in MRR.
Churn reduction
Advance warning
CSM adoption
Used LTV Forecasters to determine which Facebook ad campaigns generated high-retention customers vs quick churners, shifting spend to maximize long-term ROI.
LTV increase
Lower CAC ratio
Time modeling
Used Behavioral Segmenters to automatically drop users into specific Braze email flows based on exact feature usage, increasing engagement dramatically.
Email open rates
More daily actives
Manual CSVs
Paste this into RhinoAgents to configure a baseline Churn Prediction workflow.
You are the Lead Data Analyst Agent for [Company Name]. Your task is to identify and prevent churn for our Enterprise tier customers. Operational Rules: 1. Data Sources: Connect to our Snowflake warehouse and Zendesk instance. 2. Trigger: Run a predictive model every Monday morning. 3. Criteria: Identify any Enterprise account where 'Weekly Logins' dropped by >20% compared to their 90-day average, OR where sentiment in support tickets is scored as 'Negative'. 4. Action 1 (Alert): Send a Slack message to the #cs-alerts channel, tagging the Account Owner, detailing the exact reason for the alert. 5. Action 2 (Marketing): Automatically sync these flagged accounts to the "At-Risk Enterprise" dynamic segment in HubSpot for targeted nurturing.
Copied to clipboard!
The agent analyzes historical data to find patterns associated with past cancellations. It then monitors current users for those same signals (e.g., dropping login frequency, ignoring key features, negative support sentiment) and assigns a probability score.
No. Our platform uses natural language processing. You can type "Show me the churn rate of users who onboarded last month" and the AI will write the query and generate the chart for you.
We natively connect to data warehouses (Snowflake, BigQuery), product analytics (Amplitude, Mixpanel), CRMs (Salesforce, HubSpot), and billing platforms (Stripe, Chargebee).
Both. While it provides deep insights, its real power is action. It can ping a CSM in Slack, update a Salesforce record, or drop a user into a specific email sequence via API.
Depending on the volume of historical data available, our predictive models typically achieve 85-95% accuracy for 12-month LTV forecasts after an initial training period.
Yes. We use read-only access to your databases, employ enterprise-grade encryption, and are fully SOC 2 compliant. Your data is never used to train generalized public models.
Don't let your data go to waste. Deploy an AI Customer Analytics Agent to uncover actionable retention insights and protect your revenue today.
14-day free trial · No credit card · Cancel anytime