Case Study: 85% Faster Issue Resolution

When a fast-growing IoT company came to us, they were struggling with a common problem: their support costs were scaling linearly with device sales. Every new customer meant more support tickets, more agents, and more complexity. They needed a fundamentally different approach.

The Challenge

The company manufactures connected industrial sensors deployed across thousands of facilities. Their devices monitor critical infrastructure—temperature, pressure, vibration, and more. When devices fail or report inaccurate data, the consequences can be severe.

Their support team faced several challenges:

• High ticket volume: 5,000+ tickets per month
• Long resolution times: Average MTTR of 47 minutes
• Expertise bottleneck: Only 3 senior engineers could handle complex issues
• Blind troubleshooting: No visibility into device state during support calls
• Costly truck rolls: 30% of issues required on-site visits

The Solution

We implemented a comprehensive device support platform with three core components:

1. Real-Time Device Dashboard

Every support agent gained instant access to:

• Live telemetry from any device
• Historical trend data
• Automated anomaly detection
• Comparison with fleet-wide baselines

When a customer called, agents could immediately see if the device was online, its recent readings, any error conditions, and how it compared to similar devices.

2. AI-Powered Diagnostics

We trained diagnostic models on their historical support data to:

• Automatically classify incoming issues
• Suggest likely root causes based on telemetry patterns
• Recommend resolution steps ranked by probability
• Predict which issues would require escalation

3. Remote Remediation

For many common issues, we enabled one-click fixes:

• Firmware updates
• Configuration resets
• Calibration adjustments
• Diagnostic mode activation

Agents could resolve issues without walking customers through complex procedures.

Implementation Timeline

Week	Milestone
1-2	Telemetry integration and dashboard deployment
3-4	Historical data import and model training
5-6	Agent training and pilot program
7-8	Full rollout and optimization

The Results

After 90 days of full deployment, the metrics told a compelling story:

Resolution Time

• Before: 47 minutes average MTTR
• After: 7 minutes average MTTR
• Improvement: 85% reduction

First Contact Resolution

• Before: 42% of issues resolved on first contact
• After: 81% of issues resolved on first contact
• Improvement: 93% increase

Escalation Rate

• Before: 28% of tickets escalated to senior engineers
• After: 8% of tickets escalated
• Improvement: 71% reduction

Field Service Dispatch

• Before: 30% of issues required on-site visits
• After: 9% of issues required on-site visits
• Improvement: 70% reduction

Customer Satisfaction

• Before: NPS of 32
• After: NPS of 67
• Improvement: 109% increase

Financial Impact

The ROI was substantial:

• Support cost per ticket: Reduced from $45 to $12
• Annual support savings: $1.8M
• Avoided truck rolls: $420K in field service costs
• Reduced RMA returns: $280K in unnecessary replacements

Total first-year savings exceeded $2.5M against an implementation cost of under $300K.

Key Learnings

Several factors contributed to the success:

1. Executive sponsorship: The CTO championed the project and removed organizational barriers
2. Agent involvement: Frontline agents helped design the dashboard interface
3. Iterative rollout: Starting with one product line allowed rapid learning
4. Continuous training: Weekly model updates incorporated new issue patterns

What's Next

The company is now exploring: - Proactive support that reaches out before customers notice issues - Predictive maintenance scheduling based on telemetry trends - Self-service portals where customers can view their own device health

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Ready to transform your hardware support operations? Contact us to discuss how Telemetron can help you achieve similar results.