Reducing Customer Churn by 20% using Agentforce and Predictive AI
Industry: Real Estate
20%
reduction in customer churn through early risk detection
~75%
accuracy in predicting at-risk accounts before disengagement
Background
A leading real estate service provider operates a high-volume, service-driven business where customer engagement is reflected through work orders, service frequency and ongoing account activity.
However, identifying churn risk was challenging. Teams often realized an account was at risk only after engagement had already declined significantly. Early warning signals like cancellations, reduced service scope, product changes and stalled proposals were spread across multiple systems, making it difficult to act in time.
With limited historical CRM data and inconsistent usage, the organization needed a more reliable way to detect, understand and prevent customer attrition.
Industry Challenge
Service-driven organizations often struggle to identify churn risk early because key engagement signals are fragmented across systems.
As a result:
- Risk is identified too late, after revenue impact begins
- Early warning signals go unnoticed
- Retention efforts remain reactive and inconsistent
Goals
- Identify churn risk early using real engagement signals
- Bring service, revenue and customer activity into a single view
- Give Account Managers clear, easy-to-understand insights
- Enable timely and targeted retention actions
- Continuously improve how risk is identified over time
Implementation
Infoglen worked closely with the client to design a practical solution that helps teams spot risk early, understand what’s driving it and take the right action at the right time.
The first step was to understand how work orders, services, products and opportunities truly reflect account health. Based on this, a clean and reliable dataset was created to capture key engagement patterns such as service frequency, cancellations, revenue changes and activity trends.
This foundation enabled the following:
Unified Service Activity View
All service and sales activity was brought together into a single timeline, making it easier to track changes in engagement and spot early signs of decline.
Risk Scoring Model
A structured model was built to evaluate multiple factors such as engagement trends, cancellations, seasonal patterns, delays in activation and revenue changes.
Each account was assigned a risk score (0–100), grouped into Green, Yellow and Red categories, along with clear reasons behind the score.
Work Order & Revenue Behavior Analysis
Early signs of disengagement were identified, including declining work order volumes, missed service windows, consistent revenue drops, repeated cancellations and reduced service coverage.
Clear Explanations & Suggested Actions
Instead of raw data, Account Managers received simple explanations (e.g., “service activity has declined over the past 90 days”) along with recommended next steps and expected impact.
Their feedback (accepting or rejecting suggestions) helped improve the system over time.
Dashboards, Alerts & Workflow Support
High-risk accounts were surfaced through dashboards and alerts, with tasks automatically created and categorized as Immediate, Strategic, or Preventive actions.
Continuous Improvement
Feedback from Account Managers was used to refine how risk is identified, improving accuracy and reducing noise as more data became available.
Governance & Data Control
All data was handled within approved environments, with strict access controls and validation processes to ensure reliability and compliance.
All of this was built directly into Salesforce, so teams could review risks, understand the reasons and take action without changing how they work.
Impact
Measurable Outcomes
- 20% reduction in customer churn
- 70–75% accuracy in predicting churn risk
Qualitative Outcomes
- Shift from reactive to proactive account management
- Clear visibility into why accounts are at risk
- More confident, data-driven decisions by Account Managers
- Improved customer satisfaction through timely intervention
- Stronger foundation for future AI-driven initiatives
Technology Stack
- Salesforce Data Cloud
- Salesforce (Core CRM)
- Agentforce
- Data Transform
- Custom Predictive Models
- Workflow Automation & Dashboards
What the Customer Says
Working with Infoglen has been a great experience. The results and collaboration so far have been very positive and we’re excited to continue this as a long-term partnership.
Goals
• Detect churn risk early
• Unify fragmented engagement data
• Enable proactive retention decisions
Implementation
• Unified service and sales data into a single timeline
• Built a predictive attrition model with risk scoring and drivers
• Embedded insights, alerts and recommendations into Salesforce
• Enabled continuous learning through Account Manager feedback
Results
• 20% reduction in churn
• 70–75% prediction accuracy for at-risk accounts
• Earlier and more effective retention actions
