Guavus > Case Studies > Leading North American MSO Uses Guavus-IQ Analytics to Accelerate Operations and Dramatically Reduce Costs

Leading North American MSO Uses Guavus-IQ Analytics to Accelerate Operations and Dramatically Reduce Costs

Company Size

1,000+

Region

America

Country

United States

Product

Ops-IQ
LiveOps

Tech Stack

Big Data Analytics
Machine Learning
Data Lake

Implementation Scale

Enterprise-wide Deployment

Impact Metrics

Cost Savings
Customer Satisfaction
Productivity Improvements

Technology Category

Analytics & Modeling - Big Data Analytics
Analytics & Modeling - Machine Learning

Applicable Industries

Telecommunications

Use Cases

Predictive Maintenance

Services

Data Science Services
System Integration

About The Customer

The customer is a leading Multiple-System Operator in North America, offering content, TV and internet services to millions of subscribers. The corporation has thousands of employees who are focused on providing an excellent experience to customers spread throughout the United States. Managing a network of this size requires the ultimate in organization and rapid access to information.

The Challenge

The corporation, a leading Multiple-System Operator in North America, was facing challenges with delayed problem resolution which was affecting customer satisfaction. The Operations Team was unable to identify the root of the problem and rapidly distinguish between customer premise problems and headend CMTS or video server issues. This led to unnecessary dispatch of technicians to homes, which often turned out to be a headend problem instead of an individual set-top box problem. This resulted in customer frustration and a negative impact on their Net Promoter Score.

The Solution

The corporation implemented Ops-IQ, a product that brought together data from separate portions of the Care organization: technical support calls, subscriber trouble tickets and truck roll data. This allowed the Care Ops teams to quickly discover previously overlooked issues common across micro-populations of subscribers. Using Machine Intelligence, Ops-IQ automatically determined the normal rate of care events specific to that micro-population and recognized anomalies in these rates in real-time. This enabled the Care Ops team to immediately triage and pinpoint the heart of the problem and resolve it more expeditiously, improving customer satisfaction and Net Promoter Score dramatically.

Operational Impact

Dramatic reduction in Mean Time to Detect and Understand from hours to minutes for both network and subscriber problems
Improved customer satisfaction (NPS) through rapid resolution of outages
Faster deployment of new services without the fear of changes negatively affecting the network
Rapid root-cause analysis of problems via correlation of calls, tickets and equipment types
Performance issues revealed by lowering the baseline to discover previously hidden anomalies

Quantitative Benefit

$70M cost savings in first year by distinguishing CPE vs. CMTS/Video Server problems. Reduced truck rolls and customer service calls
Capital Expense savings of $32M, while still maintaining a high level of customer satisfaction throughout the deployment of the new services