How Nokia is Injecting Innovation Deep into CEM Space

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An Interview with Nokia at the TM Forum | TelecomDrive.com

With ‘experience’ becoming the most important entity for telecom operators to build long lasting relations with their customers – Nokia is positioning its innovative CEM offerings that can create untapped opportunities for its global customers.

Rich Crowe, Head of OSS Marketing at Nokia and Shelley Schlueter, Head of Analytics Marketing at Nokia interact with Zia Askari from TelecomDrive.com on the sidelines of TM Forum Live.

How is Nokia injecting innovation into CEM offerings today?

Nokia is announcing two new additions to its software portfolio: Nokia Autonomous Care and Nokia Cognitive Analytics for Crowd Insight. Both bring new innovation to our Customer Experience Management (CEM) portfolio by including machine learning developed by Nokia Bell Labs and other key technologies.

They enable service providers to improve customer care and services consumption, open new revenue streams and create a more innovative customer experience, while differentiating their brands and creating unparalleled cost-leverage in the digital era.

Nokia Autonomous Care with a combination of AI (with natural language processing) and machine learning, applies powerful bots to customer care. With the introduction of these intelligent bots – computer programs designed to hold conversations between service providers and their customers via auditory or textual methods are able to initiate care actions – Nokia Autonomous Care goes beyond delivering strong multichannel engagement.

It provides automation and analysis of customer interactions, delivering proactive and fully automated customer care as well as efficient self- and agent-assisted care in real time.

The bots predict and proactively address customer questions or concerns – eliminating the time subscribers spend fumbling with a complicated IVR menu or navigating an unwieldy website.

They also provide recommendations for problem solving, both by presenting the next best action for each individual subscriber, but also by providing recommendations to the customer and support staff on how to address the problem, improving support effectiveness and making self-care quicker and more personal.

And, through continuous learning and the ability to process huge amounts of information the software will eventually acquire more knowledge than any one human expert could ever possess – approaching human-like intelligence and solving increasingly complex problems.

Cognitive analytics for Crowd Insight also uses Bell Labs machine learning 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. The software optimizes itself over time, continually building a more accurate and complete profile of subscriber crowd activity.

How can these offerings help operators increase their profitability?

Nokia Autonomous Care improves the subscriber’s experience, but also equips service provider employees with the necessary tools to better assist customers which, should improve the company’s bottom line.

It dramatically reduces the daily inbound calls handled by human agents, and decreases the CSR workloads by automating a wider range of activities through bots. It can free agent time by handling more than 80 percent of transactions best suited for self-service. It can also predict and resolve up to 70 percent of residential issues before the subscriber is ever aware of a problem.

At the same time, Nokia Autonomous Care software reduces customer effort having to deal with long hold times, outdated Interactive Voice Response (IVR) systems, calling more than once for the same reason and repeat information to more than one agent. Reducing customer effort is fundamental to increasing levels of satisfaction and reducing churn, which can have a major impact on the bottom line.

Nokia Cognitive Analytics for Crowd Insight opens additional revenue streams by allowing service providers to operationalize their data. For example, it can help retailers identify new store locations based on traffic flows of their target demographic customers; allow municipalities to identify the optimal location for a new bus stop; or help advertisers determine the appropriate content for digital billboards.

What kind of challenges are being faced by operators where machine learning-driven CEM can help them?

Operators face major challenges in cost-effectively satisfying the growing demand for personalized always-on, 24-7 responsive service and keeping up with changing communication preferences of subscribers. They also must deal with frustrated subscribers who face long hold times and outdated Interactive Voice Response (IVR) systems, and have call more than once for the same reason and repeat information to more than one agent due to inefficient service channels.

96 percent of customers don’t complain after a poor customer experience, but 91 percent of those will switch providers. These “silent churners” are a major challenge for service providers. If a customer doesn’t call to complain, how do you know they even have a problem? What is required is another way to identify and diagnose issues, without the customer having to contact the help desk. The predictive capabilities of Nokia Autonomous Care with the ability to take proactive remediation actions address these customers’ needs even when they don’t call to complain.

Please provide details on some of the big innovations driven by Nokia in this space.

Nokia is bringing one of the first practical applications of AI and machine learning to the customer care process. At the core is our deep expertise in the telecom domain and networks providing out-of-the box value to our customers. Nokia also has an extensive library of use cases (A.K.A., a “knowledge system”) that can be used to deliver personalized customer service, power proactive care solutions and empower CSRs. As the knowledge system continues to improve with each subsequent transaction, it will eventually acquire more knowledge than any one human expert could ever possess, approaching human-like intelligence and solving increasingly complex problems.

Leveraging this knowledge and Bell Labs machine learning, Nokia Autonomous Care enables “zero-touch” care, automating tasks and taking proactive action rather than waiting for subscribers to call for support. Customer issues will be resolved automatically, without the need for any interaction between the customer and the help desk.

Nokia brings machine learning to more than just customer care, also using it to continually build a more accurate and complete profile of subscriber crowd activity. Then, there is our innovation of approach – enriching telco data with demographics to create robust segments for verticals to use when deciding who their target customers are and seeing where they go at different times of the day. Combine these with customized reporting capabilities and real-time movement data and you have a truly innovative view that operators can sell to many vertical players.

What growth opportunities does Nokia see being opened up with these solutions?

Nokia Autonomous 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 2020, according to Analysys Mason. The software is designed for innovators looking, not just for automated attendants which is predicted to be mainstream in the next three years, but for deeper machine learning capabilities that use bots to predict and resolve service-impacting issues before they happen.

Cognitive Analytics for Crowd Insight addresses the analytics market, which is forecast to reach 3.096 billion USD by 2020, according to Analysys Mason. While current use cases include retail, transportation, digital billboards and travel/tourism, there are many more vertical markets that could benefit from movement and location data associated with their customers and demographics.

On the technology side, SDN and NFV are becoming even more important for operators today, driven by greater cloud adoption. How do you see the future of CEM in a NFV world?

Technologies such as SDN and NFV are important for service providers to modernize their networks in order to keep pace with the increased speed and scale driven by the cloud.  In this environment it is even more important to automate customer experience management through intelligence and machine learning.