AI SOLUTIONS
Audience Intelligence
Predict Customer Churn.
By the time a customer cancels, the revenue is gone, loyalty is broken, and win-back costs are at their highest. The behavioral signals that predict departure were visible weeks earlier. Now you can act on them.
15-30%
Reduction in churn rate after full deployment
3-5X
ROI on AI-powered retention campaigns
25%
Average increase in customer lifetime value
The Problem
Churn isn't random. It's predictable if you know where to look.
Customer churn is the most direct and measurable drain on revenue and lifetime value. By the time a customer cancels, win-back costs are at their peak and loyalty is at its lowest. The window to act is in the weeks before — when behavioral signals are already present in your data, but no one has built the infrastructure to read them.
You're Reacting, Not Predicting
Retention campaigns typically launch after a customer has already missed a purchase cycle or downgraded. At that point, win-back costs are 3-5x higher and success rates drop sharply. The intervention window has already closed.
Your Data Is Siloed and Disconnected
Purchase history lives in your CRM. Engagement data is in your MAP. Support tickets are in your helpdesk. Usage data is in product analytics. Without a unified view, the early warning signs stay invisible.
Retention Campaigns Spray Instead of Target
Sending blanket discount offers trains loyal customers to wait for the next promotion. It erodes margin without solving the real problem: you don't know which customers are at risk, or why they're drifting.
CLV Leaks Quietly Until It Roars
A 5% improvement in retention can grow profits by 25-95%. Even a modest uptick in churn compounds relentlessly. One year of unchecked churn can erase revenue equal to your entire new customer acquisition budget.
No Playbook for At-Risk Segments
Even when teams sense that customers are drifting, there is no systematic process to score, prioritize, and activate a response. Customer success and marketing operate in silos with no shared trigger or unified workflow.
Upsell and Retention Work Against Each Other
Sales pushes expansion while retention tries to stabilize. Without a model that identifies churn risk and upsell propensity together, teams routinely pitch upgrades to customers on the verge of cancellation.
How It Works
Every engagement starts with a Diagnostic Sprint.
Before any build or activation, we define what churn means for your business, assess your data readiness, and recommend the right delivery path. Data readiness is the most common reason AI projects stall — our diagnostic process surfaces that reality before it becomes a problem.
- DISCOVERWks 1-2
Define & Align
Align stakeholders on the definition of churn, identify the segments most at risk, and establish the retention KPIs that will measure success.
- AUDITWks 2-5
Assess Data & Stack
Evaluate the depth and quality of behavioral, transactional, and CRM data needed to train or activate a churn prediction model.
- RECOMMENDWk 6
Deliver the Path Forward
Deliver a clear implementation path with platform requirements, timeline, and projected retention ROI for leadership to commit.
Following the Diagnostic Sprint, the right delivery path is confirmed.
Your data landscape, existing platform AI capabilities, and timeline reality determine which path fits. When the solution requires a proprietary model trained on your specific customer data and business rules, we architect and engineer it from the ground up (Custom Build). When the churn prediction capability already exists within your MarTech stack, we configure, connect, and deploy it (Platform Enablement). Both paths converge on the same commitment: defined timelines, clear deliverables at every phase, and a controlled pilot before full-scale deployment.
- ARCHITECTWks 7-10
Design the Churn Model
Define the feature set, risk-scoring logic, and decision thresholds. Map every score output to a specific retention action before engineering begins.
- ENGINEERWks 10-24
Build & Deploy the Engine
Build data pipelines from all source systems, deploy the scoring environment, and configure platforms to execute automated retention workflows.
- LAUNCHWks 24-32
Pilot, Validate & Scale
Pilot with a defined at-risk cohort and holdout group. Validate churn reduction against baseline. Deploy at full scale once benchmarks are met.
- ARCHITECTWks 7-10
Design the Churn Model
Define the feature set, risk-scoring logic, and decision thresholds. Map every score output to a specific retention action before engineering begins.
- ENGINEERWks 10-24
Build & Deploy the Engine
Build data pipelines from all source systems, deploy the scoring environment, and configure platforms to execute automated retention workflows.
- LAUNCHWks 24-32
Pilot, Validate & Scale
Pilot with a defined at-risk cohort and holdout group. Validate churn reduction against baseline. Deploy at full scale once benchmarks are met.
- DESIGNWks 7-9
Map to Platform Capabilities
Map churn prediction requirements to AI capabilities already available in your existing stack. Identify what must be configured and where gaps exist.
- CONFIGUREWks 9-15
Connect, Activate & Build
Connect data sources to platform AI scoring. Build at-risk segments, retention journey triggers, alert routing, and win-back logic within your existing stack.
- DEPLOYWks 15-22
Pilot, Validate & Enable
Launch with a live at-risk cohort and holdout group. Validate churn reduction against KPI thresholds. Move to full enablement once the configured solution performs as intended.
What You Get
Automated, actionable, and live in your stack.
The churn prediction engine transforms raw customer data into scored intelligence, automated retention actions, and measurable improvements to revenue and lifetime value.
Risk-Scored Customer Segments
Every customer receives a churn risk score updated on a defined cadence. Segments are automatically created in your CRM, ready for automated action across four risk tiers.
Automated Retention Journey Triggers
When a customer score crosses a defined threshold, a tailored retention journey fires automatically. Messaging and offers are calibrated to risk tier and customer segment across all active channels.
AI-Powered Offer and Campaign Intelligence
The model surfaces AI-generated campaign recommendations and cross-sell or upsell opportunities aligned to each customer behavioral profile, giving your marketing team a prioritized action queue.
Customer Success Alerts and Escalation Routing
High-risk and critical-tier customers trigger real-time alerts routed to your customer success team, with account context and recommended retention actions pre-populated directly in your CRM.
The Impact
Measurable outcomes from day one of deployment.
15-30%
Reduction in Churn Rate
3-5X
ROI on Retention Campaigns
25%
Increase in Customer Lifetime Value
Retention shifts from reactive firefighting to proactive, intelligence-driven action weeks in advance.
Marketing and customer success align on shared intelligence with unified triggers and shared workflows.
The model grows more precise with every campaign cycle, compounding its accuracy and impact over time.