Guavus > Case Studies > European MSO Slashes Operational Costs with Guavus-IQ Analytics

European MSO Slashes Operational Costs with Guavus-IQ Analytics

Customer Company Size

Large Corporate

Region

Europe

Country

Germany

Product

Ops-IQ
LiveOps

Tech Stack

Big Data Analytics
Machine Learning

Implementation Scale

Enterprise-wide Deployment

Impact Metrics

Cost Savings
Customer Satisfaction

Technology Category

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

Applicable Industries

Telecommunications

Applicable Functions

Business Operation

Use Cases

Predictive Maintenance

Services

Data Science Services

About The Customer

The customer is a European Multiple-System Operator (MSO) that provides integrated triple-play services to several million subscribers of cable TV, broadband internet, and telephony offerings. The MSO prides itself on delivering world-class customer service and wanted to accelerate network resolution times to continue delivering the strong customer experiences its subscriber base had come to value.

The Challenge

A European Multiple-System Operator (MSO) was struggling to rapidly distinguish between issues caused by customer premise devices and headend equipment. This delayed the MSO’s ability to find the root cause of problems and subsequently resolve the issues. With the cost of a truck roll in Germany running about 60 to 70 euros and the handling of incoming customer service calls running about 5 to 10 euros each, the provider hoped to reduce customer service costs and improve customer satisfaction at the same time.

The Solution

The MSO implemented Ops-IQ after a five-month proof-of-value (POV) test with actual anonymized data. The solution revealed hidden insights and root issues and removed the barriers between data silos. By correlating massive amounts of disparate data and running advanced analytics on it in real-time, the MSO’s NetOps and CareOps teams learned that they could identify and troubleshoot issues faster— sometimes even before they occurred. The result was a domino effect of reduced customer calls, trouble tickets, and truck rolls; enough to save the company “seven digits” annually.

Operational Impact

Unified separate silos of big data to reveal hidden problems and anomalies.
Machine learning and intelligence allowed correlation of disparate data sets (e.g. calls, tickets, and outages) to enable new insights and good decision making.
Closed-loop actions: Automatic actions such as call deflections and trouble ticket generation can be taken when anomalies are detected.
Subscriber-level data is correlated against network-level data to see which items actually impact customers.

Quantitative Benefit