How would you assess the progress that operators have made over the past 12 months with regard to understanding the behaviour of their customers through big data initiatives?

Brian Jurutka: Few things regarding big data move quickly within operators. That being said, operators have moved from discussions that define what big data is and proposing generic big data initiatives with no clear use cases, to more targeted initiatives that leverage big data to answer specific questions with business cases to support the investment. As a result, we are seeing more RFPs asking for cohesive solutions instead of the generic capabilities requests we’ve seen in the past.

Do you think the gap between the perceived benefits of big data and the reality is getting bigger?

No – the gap is getting smaller. We [the telecoms industry at large] are smarter now than we were 12 months ago regarding what big data can and cannot do. The industry as a whole is starting to move past the initial hype and the discussion is becoming more concrete around use cases. The real growth in big data analytics is still ahead of us. The opportunities for closing the gap between the investment operators are making to support growing traffic volumes and the potential revenues these data services may provide are being discovered in big data business analytics solutions.

The key to realising these benefits relies on implementing the systems that can accurately and consistently link together big data sets in a way that is actionable by users across the business. With this as a jumping off point, operators are starting to better understand the benefits of big data as it relates to network, marketing, and customer care optimisation, as well as eventual external monetisation opportunities.

Which type of use case do you see driving big data strategies at the present time? 

Research has shown that the value of an operator’s customer data is equal to the internal use cases that are actually implemented; this means that without an analytics solution in place to provide insight for operators to take action, the value of their subscriber data is zero.

When operators deploy data analytics, the benefits realised are typically internally focused. The most successful applications leverage big data to directly affect churn, simply because new subscriber acquisition costs up to three times as much as maintaining a high value subscriber.

We are seeing three core areas in which business analytics are currently being applied: in marketing, to optimise revenue and margins by attracting and growing the value of the subscriber base; in customer care – where analytics can significantly cut the cost of delivering front line subscriber support whilst improving retention through improved customer satisfaction; and in network – where linking subscriber records with network mapping and performance helps to prioritise issue resolution and infrastructure investments based on dimensions previously unavailable to operators.

What are the key questions that operators need to be asking of the big data that run across their networks?The key question that operators’ ask of their big data is generally “how can we improve margins?” To find the answer, operators actually need to start asking questions like: “how can I better segment my subscribers based on their interests and usage behaviour” or “how can I proactively manage my customer’s experience?”

Leveraging big data can provide answers that drive increased margins by identifying opportunities such as lowering churn by improving QoS for the most profitable customers, increasing subscriber acquisition through more focused marketing efforts, and/or decreasing operational costs by lowering average handle time or increasing first call resolution.

What do you regard as the biggest challenge for operators and their big data strategies over the next 12 months?

One of the greatest challenges facing operators right now as it relates to big data is actually getting an analytics solution implemented.  As previously mentioned, the process of drafting an RFP, undergoing proof of concepts, selecting a solutions vendor, and then merging all the data sources and associated teams across an organisation can be timely and labour intensive.

Even if an operator selects the perfect solutions vendor today, it will take at least 6-8 months to implement and deploy the solution to the point where it will be fully operable by end users.

In order to hasten this process, operators need to decide on the use cases they are looking to solve upfront and then vet only those vendors that can answer the desired use cases. However, operators also need to keep in mind how their business may grow, and ensure that they select a vendor who can answer additional questions that may be useful as the business evolves.  

Often, the reporting dashboards and integration with other business systems will need to adapt, once the operator progresses in its use of data insights after initial deployment. The ability for the business users to define their own segmentation, key performance indicators, and reports is increasingly important as the speed with which the business has to adapt to changing consumer behaviours increases.

An understanding of the key business drivers as revealed through analytics can also speed the deployment of predictive analytics based on existing data.

Another huge obstacle that often prevents fruition of big data projects is conflicting inter-departmental goals or lack of communication between IT, network, marketing, and customer care teams. Each business unit traditionally will have their own legacy tools or point vendor solutions that are not holistic or scalable across each business function.

In order for a big data strategy to be successful, IT and network teams need to work closely with marketing and care to share fragmented data sources required as inputs for a complete enterprise analytics solution. In this case, it is best if all business units who stand to benefit from a data analytics solution work together from the beginning to determine goals, budget, and requirements before an RFP is ever released.  

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