Voice is a Concern, Data Brings Promise to CSPs

Voice has turned into a voice of concern for CPS, since the voice & text messaging businesses don’t grow anymore: On the contrary, the revenues are declining.  The telecom industry is undergoing a thorough transformation, and as a result, Data is becoming more important day by day. The word on the street (or in space) is that he’s getting BIG.

Those who are most willing to accept the shifting landscape and try to figure out completely new business and revenue models are most likely to come out strong. Our guy Data really likes to crunch numbers and analyze information to arrive at the right conclusion. In a similar fashion, CSPs need automated predictive analytics to enrich information about the customer to provide attractive and accurate offers quickly, allow personalization, predict/prevent churn and identify fraud, create enhanced customer profiling and superior quality of experience.

It’s no longer news to anyone that customers pay a lot of attention to the price of their plans and quality of service when choosing the CSP, but it’s really important to realize how much the social circle influences a customer’s purchasing decision. A Vanson Bourne study indicated that globally more than 40% choose their CSP based on the experiences and influence from their friends and family. Understanding this playing field and social network sure sounds like a good idea, doesn’t it?

The processing, enriching and analyzing of big data to make it valuable and actionable requires a considerable amount of automisation, otherwise tackling such an immense amount of information becomes a daunting proposition.  An example of such automisation is the realtime decision-making process that defines, when and how to react to poor quality of service by identifying customers who are the most affected by it, to be able to launch a proactive retention or marketing campaign.

However, Data had to learn something else in addition to ‘mathematics’. If you want to connect with people on an emotional level, pure ‘mathematics, statistics and analytics’ aren’t simply going to cut it. You need creative ways to win the hearts and minds of people, and to do that, you have to understand them as individuals. Knowing your customers enables CSP to act proactively with the best possible personalized offering and contextually at the right time. An example of such offering is the proactive identification of those customers who need an upgrade for the data package because their usage pattern has changed. Or the proactive identification of those customers who are using multiple SIM cards from different CSPs. To prevent them from churning and making them to prioritize your offering, it’s relevant to know what their personal preferences are.

In addition to the ‘usual suspects‘ in the telecom ecosystem like customers, CSPs, vendors, OTTs (Internet Service Providers), additionally there are the newcomers from the ‘Internet of Things’ (such as energy, retailers, health, education, automobile, …) who can together with telecoms build unique value propositions where both parties can win. The struggle against the OTTs is transforming into a co-operative approach which allows value-adding joint propositions letting CSPs tap into the OTT’s revenue.

Some have suggested a premium charging model for LTE but many operators are distancing from this approach as it makes LTE generally unaffordable and unattractive for many customers, causing many to stay with their current 3G/HSPA+ plans. The essence of the discussion is to find other ways and means to generate revenue which places the emphasis on developing the co-operation between OTTs and CSPs. Identifying new revenue sources is essential, but we should not forget to keep an eye on the cost base. What’s interesting is that there seems to be a direct relation between subsidized LTE handsets and the CSP’s EBITDA margin: the subsidized handsets have a negative impact on the CSP’s margin which makes it important to know who’s really going to use the CSP’s LTE services (Source: www.tefficient.com ). The solution is to pinpoint those LTE users who really consume LTE services with the help of predictive analytics, instead of choosing the expensive strategy to subsidize LTE handsets for everyone. Please refer to the white paper written by Tefficient: ‘Why mass marketing is inefficient when launching LTE’,

On top of these above mentioned, there’s quite a lot of dynamics around identifying Quad Play opportunities in the CSPs’ business plans at the moment. Bundling broadband, TV, mobile, and fixed creates sticky services and customers, improves the revenue flow and reduces churn significantly, compared to the single or triple play. CSPs are seeking ways to provide these types of offering models by acquiring them or through co-operation. Tackling this kind of complex, multi-service and multi-technology order process requires a common platform with a fully integrated, catalog-driven approach to service order orchestration if you would like to fight the costly order fallouts. And when you add a robust Fulfillment environment enriched with analytics-driven smart order validation that closely monitors the end-to-end process of service-order capture to service delivery, you’re really good to go.

At the same time, shared accounts or multi-device/multi-user accounts are gaining more importance as an offering model, attracting not only users with several gadgets but also families and small business users utilising the same shared account for their data usage. These models are offered with no limit for voice & text usage but with limits on the data plans.  The new era clearly concentrates monetisation on data services. Some CSPs are even bold enough to talk about replicating the same model to their WiFi users, meaning that data usage limits would be imposed on home broadband users as well.

All in all, a lot of interesting topics circling around the market, and many CSPs have sent out ‘trial balloons’ to test the market response.  The known common denominator is that Data will be the future monetisation engine for CSPs, and BIG Data is the way to gain relevant information on customer’s preferences, personalisation and predictions for their ‘next move’. A horizontal and high-performant mediation layer contributes to the collection and processing of BIG data; and enriching the customer and network data with predictive analytics, human expertise and machine learning to automate decision-making. This is a viable way to go forward when combatting churn, generating new revenue and offering bespoke data service packages to customers.