Operators typically spend astronomical amounts on getting customers, even though the cost of retaining an existing customer could be as much as 50 times lower than acquiring a new client. This is a vast, yet relatively unexplored and potentially profitable opportunity for mobile operators.

Systematic churn prevention requires predicting and behavioural modelling based on a vast amount of data living in several locations on the network, and gets continuously updated.

Could artificial intelligence and big data be applied in an economical way to help operators turn around their churning customers? This needs dedicated resources and investments in big data analytics and machine learning, which would be able to detect low signals and see correlations that normal rule engines or humans cannot see.

Service assurance systems are promising everything from real-time service health information, inferred from low-level resource event data, to digital customer service assistants. This white paper assesses the realism and viability of such approaches.

We start by defining service assurance itself and by describing the types of service assurance systems available on the market today, in terms of what questions they can answer and what kinds of data they provide. Next, taking a strictly fact-based and neutral, engineering view of the topic, we look into what may be possible with Big Data and AI and what is not realistic – in terms of cost as well as resources.

We therefore discuss what questions should be answered by service assurance systems and what kinds of data we can pull from the network as input to provide these answers. We also summarize the roles of Big Data and AI in the context of service assurance.

Finally, we introduce the missing piece in service assurance today: active testing and monitoring. Active testing and monitoring addresses the question: “Are we meeting the level of service quality we promised?” We describe how this activity complements the data landscape derived from the service assurance stack with precisely the kind of pertinent and high-quality data that is missing.

We close the white paper with a brief look at some of the relevant results from a recent NFV Service Assurance and Analytics research study and survey completed by Heavy Reading in October 2017. The findings of the survey and the industry’s views on what is needed to satisfy the fundamental goals of service assurance are discussed in relation to the research and findings of this white paper.

In an interview with analyst Mark Mortensen, Digital Experience Research Director with Analysys Mason, Jonathan Kaftzan of software giant Amdocs, discusses the current landscape, identifying the key threats and opportunities that have become apparent.

As they move increasingly into the digital space, with a range of increasingly complex new digital services bundles for both consumer and enterprise, there is unquestionably an overwhelming need for Telcos to simplify their business processes.

That said, they also have a number of inherent strengths, some currently untapped, that can be galvanized and built on, as they transform, for immediate financial and subscriber value benefits.

Take a look at the very real market opportunities already open to CSPs, how some are already realising that value and the key learning points, outlined in this recent discussion.