Nokia announces machine learning-powered customer experience solutions; sets new standard for proactive care
(Thomson Reuters ONE) -
* Solutions use Nokia Bell Labs advanced machine learning algorithms to
deliver new levels of proactive care and represent significant first steps
in bringing autonomous capabilities to the customer care process.
* Reduces help desk calls due to outages by 85 percent, shortens average call
handling times by 5 to 15 percent, and eliminates inappropriate truck rolls
by up to 90 percent.
* Updates to Nokia Motive® Customer eXperience Solutions (CXS) portfolio
include: Nokia Motive Service Management Platform (SMP) 7.0 and Motive Care
Analytics (CAL) 2.0.
17 November 2016
Espoo, Finland - Nokia today announced major updates to its Motive Customer
eXperience Solutions (CXS) software portfolio, providing communications service
providers with advanced machine learning capabilities to reduce costs and
improve customer experiences.
Nokia Motive Service Management Platform (SMP) 7.0 and Motive Care Analytics
(CAL) 2.0 use machine-learning algorithms developed by Nokia Bell labs -
advanced capabilities that give computers the ability to learn without being
explicitly programmed. With support for machine learning in its CXS portfolio,
Nokia aims to set a new standard for proactive care in the industry,
dramatically improving the detection, troubleshooting and resolution of
subscriber issues.
Nokia Motive SMP 7.0 features Dynamic Intelligent Workflows, a new self-
optimizing system that determines the ideal sequence of tasks that deliver the
highest probability of resolving billing, subscription and network service
issues in the shortest amount of time. By analyzing data from previous workflow
executions, the network, customer premises equipment, and trouble tickets, this
capability enables service providers to quickly find the optimal remediation to
issues when subscribers contact help desk agents or use self-care.
Nokia Motive CAL 2.0 is the first solution of its kind that automatically
correlates customer help desk calls and self-care actions with network, service
and third-party application topologies to identify call anomalies, such as
unusual patterns in help desk calls that indicate the location of customer-
impacting network and service issues. Once anomalies are identified, Motive CAL
initiates actions through Motive SMP to resolve service disruptions and other
issues before they become widespread problems, triggering call deflection to IVR
systems and OSS alarm correlation for rapid fault localization and
identification.
Together, Motive SMP 7.0 and Motive CAL 2.0 help service providers lower costs
by reducing average help desk handling times 5 to 15 percent and eliminating
inappropriate truck rolls (dispatching a service technician to a customer
location) related to network outages by as much as 90 percent. They also enable
service providers to improve customer satisfaction and reduce churn by
eliminating 85 percent of outage-related help desk calls.
Bhaskar Gorti, president of Applications & Analytics at Nokia, said: "Service
disruptions are often hard to identify because they happen in the access
network, on customer equipment or on customers' devices. Traditional customer
care may only address a small part of a larger problem and the time-consuming,
step-by-step troubleshooting process can lead to customer frustration and the
risk of lost business. By providing the earliest possible detection of network
issues and streamlining help desk and self-care interactions, these new Nokia
solutions reduce IT and care costs, and result in happier, more loyal
customers."
Sheryl Kingstone, research director, Business Applications at 451 Research,
said: "Reducing the complexity around customer care, providing a personalized
experience and introducing self-service are key to maximizing customer
satisfaction and improving Net Promoter Scores. By embedding machine learning
into customer care offerings, operators can provide relevant and engaging
customer journeys across new channels of interactions, which are integral to
success in today's digital world."
Nokia Motive SMP and Motive CAL build upon Nokia's leading customer care
solutions, including Motive Home and Access Analytics, which enables operators
to identify and resolve issues proactively before they affect the customer
experience. Powered by Nokia Bell Labs' machine learning algorithms, the new
Motive SMP and Motive CAL solutions represent the first steps in Nokia's effort
to bring autonomous support into the digital care process.
Nokia Motive SMP 7.0
Nokia Motive SMP is a customer experience platform for home, mobile and
enterprise devices and services, enabling service providers to adopt care
processes that deliver the highest possible customer satisfaction at the lowest
possible cost. Motive SMP provides a foundation for deploying service management
as part of an agile omni-channel customer experience strategy.
Key features of Motive SMP 7.0 include:
* Dynamic Intelligent Workflows that drive actions with highest predicted
resolution for a customer at the time they call or use self-help
* Omni-channel service modeling and management of business logic that render
unified and relevant content to different audiences and a consistent user
interface across channels and devices
* Drag-and-drop interface that makes it easy to build and deploy advanced
workflows
* Robust integration framework that enables: New data source adapters for
underlying systems to gather the information required by models; end-to-end
service management across operations support/business support systems; and
prescriptive workflow actions
Motive SMP delivers the continuous improvement and agile introduction of new
business and technical processes, enabling customers to avoid lengthy IT cycles
in implementing new capabilities. Runtime updating of business logic reduces the
time required to introduce new logic into the network. Dynamic Intelligent
Workflows streamlines this process further, by leveraging machine learning to
automatically update logic at runtime.
Nokia Motive CAL 2.0
The Motive CAL analytics solution lowers call center operating expenses and
improves customer care by optimizing workflow management.
The new Call Anomaly Detection feature automatically monitors and detects issues
otherwise difficult to detect with operations support systems. These include
last-mile issues in the xDSL and fiber networks; mobile service issues; issues
with customer premises equipment; service issues affecting IPTV, high-speed
Internet and broadband service packages; and issues with third-party
applications.
Call Anomaly Detection uses a real-time statistical algorithm to identify
anomalous behavior within normal calls arriving at the call center. When an
anomaly is detected, Motive CAL triggers Motive SMP to execute a proactive
workflow.
Availability
Nokia Motive SMP 7.0 and Motive CAL 2.0 are now available. For more information,
visit the Motive CXS website.
Resources:
* Whitepaper: Using algorithms for advanced customer care
* Data Sheet: Nokia Motive Service Management Platform (SMP) 7.0
* Data Sheet: Nokia Motive Care Analytics (CAL) 2.0
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About Nokia
Nokia is a global leader in creating the technologies at the heart of our
connected world. Powered by the research and innovation of Nokia Bell Labs, we
serve communications service providers, governments, large enterprises and
consumers, with the industry's most complete, end-to-end portfolio of products,
services and licensing.
From the enabling infrastructure for 5G and the Internet of Things, to emerging
applications in virtual reality and digital health, we are shaping the future of
technology to transform the human experience. www.nokia.com
Media Enquiries:
Nokia
Communications
Phone: +358 10 448 4900
Email: press.services(at)nokia.com
This announcement is distributed by Nasdaq Corporate Solutions on behalf of Nasdaq Corporate Solutions clients.
The issuer of this announcement warrants that they are solely responsible for the content, accuracy and originality of the information contained therein.
Source: NOKIA via GlobeNewswire
Unternehmensinformation / Kurzprofil:
Datum: 17.11.2016 - 09:01 Uhr
Sprache: Deutsch
News-ID 507703
Anzahl Zeichen: 9653
contact information:
Town:
Espoo
Kategorie:
Business News
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