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Data analytics and automation lead to greater customer experience management

Closeup of businessman and woman with jigsaw puzzle pieces in office

By Neven Stipčević, CTO Bulb Technologies

When it comes to customer experience management, replying to an incident in real-time is imperative as to reduce frustrations from customers. Predicting and fixing issues before they even become visible to a customer is even more favourable, but in order to do so, a company must have a level of analytics and intelligence that’s able to learn from every past incident and optimise the succeeding experiences to learn and provide a more personal experience.

A more demanding customer

Customers crave a digital omni-channel strategy for a real and personalised experience by connecting in real-time to all their wants and needs. In fact, according to a survey by Aspect Software, businesses that adopt omni-channel strategies achieve 91% greater customer retention rates year-over-year compared to businesses that don’t.

Customers today also expect to have a flowing and personalised customer experience, constantly. This means, the customer journey has to be optimised in real time to provide a personalised user experience in every single digital touch point. The view for a data-driven digital transformation combines advances in human science to design and optimize omni-channel customer experiences in real-time to provide the optimal next-best-action for customer interaction.

However, while customers may be positive and accept different service levels from different channels, they expect the communication and service to remain consistent, and that creates a real challenge.

What’s holding you back?

Developing customer experience management is the primary objective for most businesses today and communications service providers (CSPs), being forced to transform to digital service providers (DSPs) are no exception. However, traditional approaches to ensuring this, such as overbuilding the network or supporting expansive engineering, aren’t economically sustainable.

Endless new bundled services over converged infrastructure cause exponential growth in complexity of customer support processes. CSPs constantly have to juggle through several different networks to find the problem and then respond to the customer to advise them on the problem and find a solution, which can be time consuming and expensive.

However, more and more companies are seeing the value in focussing on enhanced customer experience. This is backed up by Walker’s study which suggests that by 2020, customer experience will overtake price and product as the key brand differentiator. A good example of this is Amazon, where the delivery and convenience often acts as a driver for customers to buy the products themselves.

Companies have begun to shift in attention to the customer experience and start looking at ways to enhance it at dramatically lower cost. Companies are seeing the value in placing customers at the forefront and in order to do this, many CSPs believe that the best solution is automation.

How analytics and automation come into play

Operators must automate quality of experience (QoE) assurance to ensure they raise the bar for the broadest range of customers without increasing costs by relying on manual, people-dependent processes. The delivery of software solutions must turn to automation for complex telco service management processes as they cover various aspects of the service lifecycle.

Organisations must utilise solutions that correlate customer experience design and internal journeys, and gather the information collectively with network analytics onto a big data analytics platform. Via the platform, deep learning, machine learning and AI algorithms can be applied together with human insight to generate the right resolution in as close to real-time as possible, meaning journeys are no longer static.

A data analytics service that provides network analytics, showing the customer experience from a network-centric point of view – e.g. dropped calls, bad calls, availability, etc. can be a good solution. For example, solutions that use a digital overlay for fast and efficient provisioning of new digital services (including IoT services for industry verticals) will reap the benefits, but there’s a lot to do before this becomes the norm.

Gone are the days of old school data centres with one network environment causing companies to stifle through millions of data sets that are impossible to manually make sense of. Due to companies establishing new network nodes in multivendor network environments, companies now see the need of automating their processes, or they’ll fall behind the pecking order. Unique selling points in today’s digital economy is being able to process and analyse big data in real-time – and the answer is, automation.

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