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

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	European MSO Slashes Operational Costs with Guavus-IQ Analytics Guavus

European MSO Slashes Operational Costs with Guavus-IQ Analytics

Guavus

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
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. Read More
About 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. Read More
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. Read More Log in to view content
Contents

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

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.

About 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.

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.

Impact #1	Unified separate silos of big data to reveal hidden problems and anomalies.
Impact #2	Machine learning and intelligence allowed correlation of disparate data sets (e.g. calls, tickets, and outages) to enable new insights and good decision making.
Impact #3	Closed-loop actions: Automatic actions such as call deflections and trouble ticket generation can be taken when anomalies are detected.

Impact #1

Unified separate silos of big data to reveal hidden problems and anomalies.

Impact #2

Machine learning and intelligence allowed correlation of disparate data sets (e.g. calls, tickets, and outages) to enable new insights and good decision making.

Impact #3

Closed-loop actions: Automatic actions such as call deflections and trouble ticket generation can be taken when anomalies are detected.

Benefit #1	Annual OPEX savings in the “seven digits”
Benefit #2	4 to 7% OPEX savings attributable to fewer truck rolls
Benefit #3	4 to 7% reduction in customer service calls

Benefit #1

Annual OPEX savings in the “seven digits”

Benefit #2

4 to 7% OPEX savings attributable to fewer truck rolls

Benefit #3

4 to 7% reduction in customer service calls

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Overview

European MSO Slashes Operational Costs with Guavus-IQ Analytics

Operational Impact

Quantitative Benefit