How Skan AI Saved 3 Fortune 500 Enterprises Millions Through Contact Center Optimization

In the complex maze of contact center operations,  most leaders can only see a fraction of what's really happening. Like submarine captains navigating with limited sonar, they miss the vast ocean of activity beneath the surface. Skan AI works like advanced underwater imaging, revealing the complete picture of agent activities across every application and process.

Let's look at how Skan AI has helped 3 leading enterprises in their contact center optimization journey.

Case Study #1: The $28M Health Insurance Breakthrough

The Challenge

One of America's largest health insurers operated a massive contact center with over 20,000 representatives. Their costs kept rising despite multiple improvement initiatives. Traditional process mapping and time studies weren't working.

They couldn't understand why agents took so long to handle calls. Their systems showed good talk times, but overall handle times remained high.

The Discovery Process

The insurer implemented Skan AI to observe how agents actually worked across all applications. Installation was quick and required minimal IT involvement. Within weeks, they had their first insights.

What they discovered shocked leadership. The data revealed patterns nobody had spotted before.

Key Findings

Skan AI's process intelligence platform uncovered three major issues that traditional methods had missed:

1. Application Switching: Agents switched between applications an average of 37 times per call. This constant toggling added minutes to each interaction. Agents were using 11 different applications, including 3 systems management didn't even know about.
2. After-Call Work: Representatives spent 42% of their time on documentation after calls ended. This work was riddled with duplicate data entry across systems. Simple updates to customer accounts took 3x longer than necessary.
3. Process Variations: Skan AI revealed 14 different ways agents handled the same type of call. Top performers used completely different workflows than average agents. The best agents skipped unnecessary steps that others followed religiously.

The Results

The health insurer identified $28 million in potential savings from operational efficiency improvements. They implemented changes that cut down non-value-added activities by 40%.

Within six months, they had reduced application switching by 52%. Average handle times dropped by nearly a minute per call. "The depth and specificity of the data-driven insights delivered by Skan AI in 3 months would have taken a team of 8 six sigma blackbelts multiple years to put together," said their Director of Process Excellence.

Case Study #2: Improving First Call Resolution at a Leading Healthcare Payer

The Challenge

A leading healthcare payer struggled with low First Call Resolution (FCR) rates in one of their contact centers. Customers frequently had to call back about the same issues. This created frustration and increased costs.

Leadership couldn't pinpoint why some agents resolved issues on the first call while others didn't. Their existing systems showed what happened but not WHY it happened.

The Approach

The company partnered with Skan AI to discover insights that would help improve their FCR rate. The implementation focused on tracking end-to-end customer journeys across all applications and channels.

Skan AI observed how work actually happened, not just how it was supposed to happen. This approach revealed the exact differences between high-performing and average agents.

Key Discoveries

The analysis uncovered several critical insights:

1. Process Standardization Gaps: Skan AI found significant process variations between teams and individuals. Top performers followed specific pathways that others missed.
2. Root Cause Patterns: The system identified the exact reasons customers needed to call back. Many stemmed from incomplete information gathering during initial calls.
3. Application Usage Patterns: High-FCR agents used applications differently. They accessed knowledge bases more efficiently and navigated systems in a specific order.

The Results

The healthcare payer achieved remarkable improvements through these insights:

• $4 million in savings from a 40% reduction in process variability
• $7 million in annual savings via a 20% improvement in workforce capacity utilization
• $4 million in savings by cutting customer account update cycle times through automation

The project identified $15 million in cost savings. More importantly, customer experience improved substantially as more issues were resolved on the first call.

Case Study #3: Financial Services Company's Process Transformation

The Challenge

A major financial services company struggled with their IRA and 401k consultation process. Agents juggled multiple platforms including their CRM, ERP, file storage and document signatures.

This complexity created high average cycle times of 2.6 days. Only 10% of consultations resulted in sales.

The Solution

Skan AI observed the entire consultation workflow across all applications. It tracked exactly how agents navigated between tools. The system identified where time was being wasted.

The analysis revealed significant inefficiencies. Agents were spending excessive time sending documents manually. They were entering the same data in two different applications.

The Results

The insights from Skan AI helped the company identify nearly $13M in estimated savings.  They reduced average call processing time by 45%. Sales conversion rates improved as agents spent less time on paperwork. Employee satisfaction increased as frustrating duplicate work was eliminated.

The company implemented automation for routine document tasks. This reduced manual effort and eliminated errors from incorrect documents being sent.

Common Threads Across All Three Case Studies

Every organization was shocked by what they discovered. Leaders had no idea how much application switching was happening.

All three companies found excessive after-call work consumed agent time. Process variations caused significant waste across all organizations.

The companies discovered applications being used that management wasn't aware of. This shadow IT created major inefficiencies and security risks.

How to Find Hidden Costs in Your Own Contact Center

Look for these warning signs of application switching problems:

• Long silences during customer calls
• Agents complaining about "too many systems"
• New agents taking months to reach proficiency

After-call work might be excessive if:

• Agents spend more time on wrap-up than talking to customers
• Documentation requirements keep growing
• Similar information is entered in multiple systems

Process variations are likely costing you if:

• Different teams handle the same calls differently
• Top performers follow different processes than others
• You lack clear standard procedures

The Future: Harnessing Process Understanding to Save Millions

These three case studies reveal the power of seeing what's really happening in your contact center. The combined impact totaled over $50 million in savings. The competitive advantage of process visibility can't be overstated. Organizations that can see and optimize their complete workflows will outperform those still operating in the dark.

Skan AI observes every application agents use without requiring integration. Other approaches only see part of the picture.

The platform creates a digital twin of operations that shows exactly how work flows. Traditional methods cannot achieve this complete visibility.

Continuous monitoring captures all process variations over time. This is far more accurate than point-in-time studies or manual observations.

You likely have hidden costs waiting to be discovered as part of your contact center optimization. The question is whether you'll find them before your competitors do.

Want to see what's really happening in your contact center?