The value of analytics in telecoms is well established. But the breadth of advantages it can deliver as well the challenges to be overcome in getting deployments “right” may be less clearly understood.
These days, there’s no shortage of conversation around the topic of analytics in the telecom community. That’s not surprising when you consider the numbers. According to consultancy McKinsey, telcos can generate incremental revenues of between five and 15%, and cost reductions of 15 to 35%, through the effective use of B2B analytics alone. So, the motivation to sharpen analytics skills (and deploy analytics tools) is clear.
And not just that. Considering today’s heterogeneous networks, increasingly distributed and complex in character, and requiring constant upgrade and evolution cycles to meet increasing demand for capacity and diversified services, the value of having the access to the detailed information that analytics provides is ramped up. Without analytics, the telco’s ability to:
…is diminished.
That telcos are grasping this reality is to some degree reflected in how approaches to analytics are changing, taking into account new requirements driven by the way their businesses are evolving. A few trends stand out to highlight the challenges they are facing.
Location information is becoming a central element in analytics. Where something happened is becoming as valuable as what happened and when it happened, which should come as no surprise given the millions of always-on devices now in circulation. They create a treasure trove of data than can be leveraged in much of which location plays a central role in creating value.
Another wrinkle on location: 5G infrastructures drive a much broader geographical distribution of assets which, of course, must be managed. And not just managed. Given that user expectations are expanding to expect proactive responses to issues, access to analytics plays a growing role in meeting new service levels.
Given its nature – industrial equipment that generates large streams of data, it doesn’t require a huge leap of faith to anticipate a growing requirement for predictive maintenance, which means information so that repairs can be made quickly must be made available. Analytics changes the telco from being simply a responder to network issues to an intelligent business that can anticipate them.
With the diversification of data sources suggested above (for instance, think about a use case that required bringing together and analysing data from the Packet Core Gateway, the Media Resource Function, and the eNodeB in a 5G network), telcos will be challenged to merge and analyse data the sources of which are disparate and have different owners. But to accrue insights, they’ll have to overcome this challenge.
A “cousin” of data fragmentation, telcos are currently mainly organised around separate groups pursuing their own remits in their own ways. But analytics, and subsequent optimisation and data aggregation, increasingly demands a holistic picture that drives a holistic response. Silos are antithetical to effective use of analytics. Investment in analytics tools will not be effective unless they consider data sets across the breadth of the organisation, not just in one silo.
We all know the bywords – personalisation, performance, innovation, quality of service. They all require a marriage of customer and network data if the telco wants to get a handle on QoS right down an individual level. Without analytics, customer retention becomes a major challenge and churn soon follows.
If we accept that it’s critical to both start leveraging advanced analytics (encompassing and meeting some of the challenges identified above along the way) and re-configure organisations in the way required to do so, what’s the payoff? McKinsey identifies three “classes” of use case where the benefits of analytics can be accrued: Market Share, Customer Profitability, and Cost Efficiency. The use cases within them are worth looking at in more detail because they, too, point to a roadmap forward. They include:
By using predictive analytics based on usage history, billing patterns, pathways of acquisition, and service history, telcos can better predict which customers are at risk of churning.
Analytics can be used to develop and deploy models likely to increase both leads generated and conversions. Given competition for market share is unlikely to become less intense, this may be significant.
Algorithms driven by data and using predictive analytics are already appearing to great effect sourcing data related to buying patterns and communications history.
In a similar manner to DNBO, targeted customers can be encouraged to shift from legacy products to services that are strategically prioritised.
Machine generated data is an obvious means of improving asset utilization which in turn should reduce capital expenditure
Predictive analytics can be used to pinpoint network inefficiencies by combining usage pattern data, traffic analysis, and network parameters.
By identifying service issue and proactively informing customers, streamlined intervention requirements should drive lower costs.
Today’s operator must be able to effectively measure the services used and their delivery to customers. That means structuring and deploying an approach to analytics that, likely, surpasses legacy analytics boundaries (where they exist). Holistic, fully-fledged analytics are rapidly becoming a table-stakes function within the telco business.
For operators with already complex networks and considering likely plans for further evolution, Utel can help ensure that these challenges (both today and in future) are met successfully via a single, comprehensive and proven solution that leverages analytics information and can be deployed across any network. Let’s explore how we can help you benefit from a holistic approach to analytics – so you can secure the rewards.
