Recently, Telefónica took yet another step into the world of big data analytics – announcing plans to consistently track customers’ service experiences to improve Quality of Service (QoS). This move follows the launch of its Dynamic Insights Unit back in 2012.
To do this, Telefónica will deploy big data analytics tools within all of its service operation centres. The intention is to monitor customers’ network data to anticipate potential network issues, and proactively respond with tailored actions – including network repairs or optimisation. But is what Telefónica’s proposing really enough?
Big data analytics takes things a step beyond the traditional descriptive approach. It allows predictions to be made about future events based on the analysis of historical data, and recommends actions that maximise the probability of a desired outcome. Yet, what Telefónica describes as a “ground-breaking” project, many other big data industries are already doing and in far more advanced ways, using far more advanced tools – in particular, machine learning.
Communication Service Providers (CSPs) are facing fierce competition for customers’ mobile spend from digital services like Netflix and Spotify. To win back their share of the market, CSPs must focus on creating a personalised experience communicating value, increasing loyalty, and recognising the individual. This is something for which some digital services are already held up as a gold standard for by customers – a challenge understood only too well by operators.
Due the sheer size of the task of storing, processing and accessing large amounts of data, one issue many CSPs face is using limited data-sets to action decisions – often done on a “herd” basis, not a personal one.
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