What Is Number Of Truck Rolls For Tv Service Operator For At&t

A provider of integrated triple-play services to several million subscribers of cable Boob tube, broadband internet, and telephony offerings, the European MSO prides itself on delivering world-class client service. Logically, information technology wanted to accelerate network resolution times to continue delivering the potent client experiences its subscriber base had come to value.

The Trouble: Accelerate troubleshooting to meliorate customer satisfaction and lower OPEX

The MSO was looking for a way to identify and resolve network issues faster, particularly problems that could touch client experiences. Its operations team was struggling to rapidly distinguish between issues acquired by customer premise devices and headend equipment. This delayed the MSO's ability to observe root crusade of bug and later resolve the issues.

With the cost of a truck roll in Federal republic of germany running about 60 to 70 euros and the handling of incoming customer service calls running well-nigh 5 to 10 euros each, the provider hoped to reduce customer service costs and amend customer satisfaction at the aforementioned time.

The Solution: Implementing Ops-IQ volition generate OPEX savings of 7 figures each year

Afterwards a 5-calendar month proof-of-value (POV) test of Ops-IQ with bodily anonymized data, hidden insights and root issues were revealed and the barriers between data silos were removed. Past correlating massive amounts of disparate information and running advanced analytics on it in real-fourth dimension, 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 salve the visitor "seven digits" annually.

Engineers looking at a mess of wires

From Faster Mean Time to Repair (MTTR) to a Higher NPS

Earlier deploying Ops-IQ, this MSO set most to validate its real business value.They wanted to ensure that Ops-IQ would in fact help them meet their goals of greater network uptime, better customer experiences, and lower operational costs.

Accomplishing all that would crave reducing the fourth dimension information technology takes to become aware of a network effect, determine its crusade, and resolve it. It's common for repair trucks to be dispatched to client locations, just to find that a trouble's cause lies in head-end equipment, not CPE, for example.With automobile learning and analytics, however, the Ops-IQ solution promised to pinpoint root issues faster and more than accurately, conserve unnecessary truck rolls, ameliorate client satisfaction, and reel in costs.

Existent Simulation

The company validated these claims using a real simulation that involved data from half-dozen months prior, which Guavus analyzed and aggregated into predictions. From the simulation, the MSO determined the time it would take to begin reaping the rewards was five months, says the projection director. The other do good of the simulation was to get a qualitative assessment of the product, "such every bit learning how well it would integrate with our existing systems," he notes.

Unifying Data Silos

Guavus accomplished its on-the-mark predictions and analytics past bringing together data from all over the arrangement, including technical support calls, subscriber trouble tickets, and truck roll data. It correlated the data, identified commonalities, and automatically determined baselines and anomalies.

From there, the software raised alarms with specifics. For example, multiple, seemingly disparate issues all pertained to a certain CPE firmware type. With that information, a problem that would have taken days to troubleshoot, was at present identified and resolved in a thing of minutes.

More Satisfied Customers

The MSO predicts that the acceleration of trouble resolution enabled past Guavus will better its Cyberspace Promoter Score by nearly 1 point once the solution has been in production fashion for five months. This represents a significant boost to the visitor's overall brand.

Operator Blazon

Multi-System Operator (MSO)

Product

Ops-IQ Live Ops

Challenge

A European multiple-systems operator (MSO) needed to advance the discovery and resolution of network issues before they impacted customer service. The provider hoped to use automation to improve customer satisfaction—and thus its Net Promoter Score (NPS)—while also reducing operational expenses.

Results with Ops-IQ

Subsequently a five-calendar month proof-of-value (POV) test with and without Ops-IQ, the MSO installed Ops-IQ Live Ops software in its production environment, having proven that it would derive the post-obit benefits:

Annual OPEX savings in the "seven digits"

4 to seven% OPEX savings attributable to fewer truck rolls

Improved customer experiences

iv to 7% reduction in customer service calls

5 to eight% reduction in trouble tickets, lightening Aid Desk workloads

A meaning increase in overall NPS (almost a full point)

Why Guavus-IQ?

Proven ability to unify separate silos of big information to reveal hidden issues and anomalies.

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

Big Data at scale: Guavus has proven its ability to analyze billions of records per day.

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

Rapid real-time assay of information yields firsthand results.

Subscriber-level information is correlated confronting network-level data to see which items actually touch customers.

Conducts real-time correlations of separate events happening in the network.

Correlates and analyzes twenty one thousand thousand+ time series daily (arrays of numbers indexed past time)

Success stories