Company Profile
Company Type
Mid-size healthcare provider
Team Size
30-150 employees
Industry
Healthcare
Key Challenge
Struggling with inefficient manual customer retention processes that were slowing growth and increasing operational costs. Their primary concern was HIPAA compliance.
Tools Connected
The Challenge
This mid-size healthcare provider had reached a breaking point with their manual customer retention process. With 30-150 employees managing daily healthcare operations, the team was spending an average of 25+ hours per week on repetitive customer retention tasks that added no strategic value. The workload was unsustainable, and errors were becoming more frequent as volume grew.
The consequences extended beyond wasted time. In their healthcare business, delayed customer retention created a cascade of downstream problems — missed deadlines, frustrated stakeholders, and data quality issues that undermined decision-making. The team had tried hiring additional staff, but the cost was prohibitive and training new employees on their complex healthcare processes took months. They needed a solution that could handle their current volume and scale with their growth, without requiring a proportional increase in headcount.
The Solution
The team selected Arahi AI to automate their healthcare customer retention workflow end-to-end. Implementation began with connecting their core tools — Epic, Kareo, and Slack — to the Arahi AI platform. Using the no-code builder, they configured AI agents that replicate their best-performing team member's decision-making process, but at machine speed and consistency.
The AI agents handle every step of the customer retention process: receiving incoming requests or triggers, analyzing the context using healthcare-specific rules, making intelligent routing decisions, executing the core actions, and notifying the right stakeholders. What previously required 45+ minutes of manual work per instance now completes automatically in under 2 minutes. The agents also learn from corrections, continuously improving their accuracy. The team connected Google Forms for tracking and reporting, giving leadership real-time visibility into customer retention performance metrics for the first time.
The Results
Measurable improvements across key healthcare customer retention metrics.
Monthly Churn Rate
60% reduction
Before
5.2%
After
2.1%
At-Risk Detection Lead Time
Proactive vs. reactive
Before
After cancellation
After
14+ days before churn
Retention Intervention Success
189% improvement
Before
18%
After
52%
Annual Revenue Saved
$340K impact
Before
$0 (no proactive program)
After
$340K recovered
NPS Score
142% improvement
Before
24
After
58
“We went from spending half our day on customer retention to having it just happen automatically. The AI agents handle the routine work perfectly, and our healthcare team can focus on the strategic decisions that actually move the needle. I wish we had done this a year ago.”
VP of Operations
Mid-size healthcare provider
Key Takeaways
The most important lessons from this healthcare customer retention automation project.
Automating customer retention in healthcare delivered immediate, measurable results: faster processing, higher accuracy, and lower costs.
The key to success was connecting existing healthcare tools to AI agents rather than replacing the entire tech stack.
24/7 automated processing eliminated backlogs and ensured consistent service quality regardless of volume fluctuations.
Starting with a pre-built template and customizing for healthcare-specific requirements dramatically reduced time-to-value.
Implementation Timeline
From zero to production in 2 hours — here's how they did it.
Step 1: Connected healthcare tools to Arahi AI
Integrated Epic, Cerner, and Athenahealth with Arahi AI using pre-built connectors — no API keys or custom code required. The team verified data flow between systems in under 15 minutes.
Step 2: Configured AI agent business rules
Defined the healthcare-specific rules for customer retention: scoring criteria, routing logic, escalation thresholds, and exception handling. The team used Arahi AI's visual rule builder to translate their existing process into automated workflows.
Step 3: Tested with live healthcare data
Ran the AI agents on a week's worth of historical customer retention data to validate accuracy and identify edge cases. Made minor adjustments to scoring weights and routing rules based on the results.
Step 4: Launched and monitored
Deployed the AI agents to production with the entire team notified via Google Forms. Monitored the first 48 hours closely, confirming 99%+ accuracy before reducing oversight to weekly reviews.
Setup Time
2 hours
AI Agents
3 AI agents
Tools Connected
5 integrations
Frequently Asked Questions
Common questions about automating customer retention in healthcare.
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