Big data holds considerable promise but utilisation of the insights it can reveal has been held back by several factors.
One reason for the delay in utilising data insights is that there is such a large volume of data, identifying what matters can be difficult, says Inna Ott, director of marketing at Polystar. While everyone knows that big data offers huge potential, the issue has been, where to start? With so much data available, it’s hard to see the wood for the trees. It can also seem dauntingly complex, intelligible only to data scientists and other specialists.
This has changed. Today, communications service providers (CSPs) are showing signs that big data strategies and plans have reached maturity by accelerating deployment of solutions that enable them to begin to capitalise on this rich resource. The difference driving this gathering momentum is that there are now clearly identifiable targets that enable a step-by-step approach to the implementation of big data strategies.
Many CSPs, for example, have chosen to focus on subscriber analytics as the first step in their big data strategies. This is because subscriber analytics is believed to offer the greatest potential to deliver rapid returns, enabling them to build better relationships with subscribers, among other benefits.
There’s a simple reason for this: a deep understanding of customer behaviour, while fundamental to business success, has traditionally been an expensive and imprecise discipline. Imprecise, because extrapolation from the few to the many is no guarantee of accuracy; and expensive, because efforts to obtain statistically significant data from which more accurate conclusions can be drawn, can be exceedingly costly.
While isolated data sets can deliver insight, conclusions that can be drawn may not be sufficiently representative. For CSPs, the ability to obtain accurate, timely insight into what customers actually do from a sufficiently broad sample base provides an unparalleled opportunity to not only deliver better service and support, but also to reduce the costs of customer research. Big data provides the constant flood of real-time, objective information about customer behaviour that makes this possible. The difficulty has been to make this information available to the many, not just data scientists.
Happily, there are now solutions that address this issue. A class of advanced processing engines is available that sorts information collected from the network and filters it so that it can be made relevant to different users. This pre-processing removes the pain from big data analysis and delivers the right information in an accessible manner to people within the organisation. Smart solutions provide different views, so that, for example marketing teams can see the information they need, while customer services obtain a different view – and can interrogate the data with different queries and questions.
Putting this into action yields measurable results. The provision of detailed, timely and relevant information makes it possible to implement a transformation in the delivery of customer care, enabling rapid problem resolution and delivering dramatic cost reductions.
For example, when customer care teams are contacted with a problem, it is crucial that the right information is available across multiple channels, such as contact centres, social media and self-service portals. By giving customer care agents detailed insight into the specific services and networks subscribers use and the quality delivered, technical issues will be promptly identified. This, in turn, means that more problems will be solved directly and more quickly by frontline support teams, reducing escalation to more expensive second line agents, saving time and money.
A fully functional Polystar Subscriber Analytics solution delivers insight into voice, messaging, data consumption and performance, among other metrics. Recent deployments have demonstrated that Subscriber Analytics can quantifiably improve overall customer satisfaction and help to reduce churn through improved service in customer care. This ensures a better customer experience while efficiently monetising CSPs’ services and reducing costs.
In one recent deployment, frontline customer care agents have been able to access relevant information from within existing CRM systems, thanks to the integration of data analytics information directly from pre-filtering systems, while second line teams capitalise from a unique analytical user interface, giving access to advanced dashboards, reports and drilldown capabilities. As a result, customer care agents benefit from richer information and more effective tools to address more complex problems.
In this example, the overall cost per call rate has been reduced dramatically. It also facilitated a significant cost reduction by decreasing the time taken to resolve issues in troubleshooting activities and hence the duration of support calls by at least 50%, which corresponds to a saving of US$2-6 per call.
Finally, in the same case, the solution reduced churn rates by 40% due to early identification and resolution of customer issues. Such results indicate the promise of widespread deployment of the solution and the positive benefits that will accrue by selecting customer care as the first part of implementing an enhanced CEM transformation programme.
Polystar’s Subscriber Analytics solution is the brains behind this approach. It is the foundation of a comprehensive programme for implementing effective customer care. The solution enables CSPs to target efforts towards the areas that will generate the most immediate returns, at the same time building a long-term approach to CEM. In practice, this means choosing the most appropriate area in which to focus efforts. For some, that will be customer care, for others, marketing, and so on. Once a platform is in place, that can be used to focus efforts, relevant data can be identified, extracted and delivered to where it is needed most. Moreover, the core platform can be utilised as the foundation for spreading the use of big data throughout the organisation.
This simplification of data analytics is critical. It democratises data and, for the first time, enables its potential to be realised. Data analytics is no longer a specialist discipline. Now, not only can anyone ask the right questions, but we can ensure the right people are able to discover the answers. Finally, we can start to see the wood for the trees and, step by step, begin to unlock the potential insights available from the intelligent utilisation of big data.
The author of this blog is Inna Ott, director of marketing at Polystar.
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