• Evaluation with a major service provider shows new Nokia Autonomous Customer Care, equipped with machine learning-powered interactive care bots, predicts and resolves up to 70 percent of residential service issues, like poor DSL or Wi-Fi performance.
• New Nokia Cognitive Analytics for Crowd Insight taps machine learning to track, analyze and understand crowd behavior, enabling service providers to monetize their data and allowing retailers and other market verticals to maximize customer opportunities.
• New Nokia Analytics Office Services suite provides deep domain expertise to help service providers improve customer experience, data monetization, service operation centers, data science and network automation.
Nokia today unveiled new machine learning capabilities and expertise to help service providers strengthen the digital customer experience.
Designed to help service providers improve business processes and deliver greater value to subscribers, Nokia’s new solutions include Nokia Autonomous Customer Care and Nokia Cognitive Analytics for Crowd Insight software, and the Nokia Analytics Office Services suite, each targeting high-growth segments of the software and services market.
Bhaskar Gorti, president of Applications & Analytics at Nokia, said: “As the next step in our strategy to build a standalone software organization at scale, we are making major investments in our software and service capabilities that will help customers build strong digital businesses. We want to eliminate the need for customer service calls by avoiding issues in the first place. We also want to give service providers the ability to better understand and contextualize consumer needs.”
“Zero Touch” Nokia Autonomous Customer Care
Nokia Autonomous Customer Care software targets the customer interaction market, the fastest-growing sub-segment of customer care, forecast to increase by nine percent a year to reach 1.486 billion USD in sales by 20201, according to Analysys Mason. Available in Q3, Nokia Autonomous Customer Care software provides customers deep machine learning capabilities that help them resolve service-impacting issues, before they ever happen.
Building upon Nokia’s deep digital network and services expertise, Nokia Autonomous Customer Care software offers interactive care bots with natural language processing (NLP) capabilities and Nokia Bell Labs machine learning algorithms. It is the first autonomous care solution developed based on extensive telecommunications industry experience and know-how.
Nokia Autonomous Customer Care has the capability to predict and resolve service-impacting issues so that corrective actions happen before they impact customers. It also interfaces with consumer intelligent assistants, such as Apple Siri, Amazon Alexa, Microsoft Cortana, and Facebook Messenger* and other popular social platforms, allowing subscribers to use natural language and the channel of their choice to troubleshoot and request services without being put on hold.
Nokia Bell Labs reviewed the performance of Nokia Autonomous Customer Care in a tier-one service provider network. It found that the machine learning capabilities could predict and resolve up to 70 percent of residential issues that would lead to service disruptions before the subscriber is ever aware of a problem. Other Nokia data found the software could handle up to 80 percent of care issues without customer support agent intervention when subscribers use interactive bots, significantly reducing the number of help desk calls, customer support requests and contact center workloads.
Mark Mortensen, Research Director & BSS Practice Leader at Analysys Mason, said: “As CSPs move forward in their journey to become DSPs, they need to provide an enhanced, digitalized user experience to consumers and businesses. These users are demanding intelligent, personalized self-service from CSPs. Solutions such as Nokia’s Autonomous Customer Care have the potential to handle a substantial volume of incoming customer calls as well as increase self-service access to a wide range of options. Key to this self-service revolution is machine learning coupled with natural language user interfaces.”
Nokia Cognitive Analytics for Crowd Insight
Part of Nokia’s comprehensive Customer Experience Management (CEM) portfolio, Nokia Cognitive Analytics for Crowd Insight is a new software application using Nokia Bell Labs’ machine learning algorithms to track and analyze the aggregate movement of subscribers using real-time network data instead of GPS or application data. This allows for more frequent updates and larger sample sizes to give precise, timely movement information, and can leverage Nokia network analytics installed base in more than 200 customers. The software optimizes itself over time, continually building a more accurate and complete profile of subscriber crowd activity.
Nokia Cognitive Analytics for Crowd Insight opens additional revenue streams by allowing service providers to operationalize their data, such as helping retailers identify the best high-traffic areas for new stores; allowing municipalities to identify the optimal location for a bus stop; or helping advertisers determine the appropriate content for digital billboards based on subscriber travel patterns at any given period in time. In a recent evaluation, one customer achieved a 40 percent increase in shopping mall traffic from nearby neighborhoods due to tailored campaigns based on Crowd Insight data. Nokia Cognitive Analytics for Crowd Insight will be available in Q3.
Nokia Analytics Office Services
Available today, the Nokia Analytics Office Services suite addresses the growing need for analytics experts, including data scientists that are in short supply. The analytics services market is growing at seven percent a year and expected to reach 1.613 billion USD by 20202, according to Analysys Mason. The comprehensive suite is designed to help service providers understand how to use analytics to improve customer experience, monetize services, run better Service Operations Centers, tap into the expertise of world class data scientists and automate their networks.