The conversation around big data has taken many forms, but in the telecoms sector, it’s largely been seen as a challenge. For communications service providers (CSPs), more data has been equated with operational inefficiencies, such as issues with collecting and storing the information. But there’s a flipside to this—CSPs that can better tap into all of the data at their fingertips will eke out critical insight into their subscribers’ transaction patterns, social networks, circumstances and even general moods, and in turn, realize more business opportunities.
To make truly meaningful data a reality, we must first abandon the traditional ways we’ve dealt with large amounts of information. Conventionally, CSPs have stored customer data across complex and disparate silos, without proper standardization or sharing of information. What I’m advocating, however, is a move towards advanced analytics, which calls for creating an architecture that pulls and integrates data from across CSPs’ organizations, no matter the department and system. Plus, a growing area of advanced analytics is social network analytics (SNA), which dives into the billions of daily data transactions on the network to identify influencers and patterns among social circles. All of this data can then be converted into real and valuable knowledge. After all, only when data is put into context does it provide a panoramic view into the network and subscriber base, and allow for pattern matching, predictive modeling and other forms of data analysis that can be acted upon. Altogether, this is what I call contextual intelligence.
Because its potential is astounding, it’s worth taking a step back and considering the practical implications for contextual intelligence. Ultimately, the motivation for taking the leap into the world of predictive and contextual analytics is to increase customers’ lifetime values. There are several ways to do this, but for starters, analytics enables CSPs to better retain the customers they have. For example, with predictive modeling, CSPs can identify not only which customers have a higher propensity to churn, but also those within their subscribers’ social networks that are likely to follow suit. Armed with this unique customer insight, CSPs can automate customized offers for customers deemed ‘high risk’ for churn, which is triggered by—or even before—a service quality problem occurs. The beauty of this approach is its shift from using data retroactively to using it to look into the future and ask: What will trigger churn, and how can we prevent it? Of course, the answer to this varies with each subscriber, but again, advanced analytics leaves room for this and enables targeted outreach based on an integrated view of the users.
Then, there are the opportunities for upselling, cross-selling and new customer acquisitions. For instance, identify the ideal users for a new 4G service launch and pique their interest via an appealing offer, or target those customers who travel frequently with data roaming bundles, if they don’t already have this. Plus, taking into account SNA, CSPs can also determine the users whose friend circles are largely off-network and offer them a “friends and family” deal that, perhaps, might nudge new customers into their networks. Ultimately, the entire process of using analytics for new sales opportunities is underpinned by the concept of advanced offer management, which just means the ability for CSPs to know which promotions work and to manage this via data like traffic stats or loyalty points.
Beyond churn prevention, contextual intelligence breaks new ground when it comes to customer profiling. By analyzing incoming data from varied sources across their organizations, CSPs can drill deep into their subscriber bases, segmenting customers by usage, interests, location, socioeconomic class or influence, to name just a few. This, in turn, allows CSPs to be strategic and savvy about their customer outreach. For instance, by knowing which subscribers are heads of households and by identifying and predicting their needs, CSPs can offer more relevant and attractive services and, in turn, enhance their satisfaction and loyalty. In addition, being able to execute targeted marketing campaigns in a timely manner has the potential to increase the revenue from the entire household.
Advanced analytics plays a pivotal role in helping CSPs employ their assets efficiently. Real-time feeds from network and back-office systems, combined with the prediction of customers’ lifetime values, spending patterns and service use, among other things, can help CSPs make intelligent operational and business decisions. For example, it can support capacity planning and network optimization, and drive advanced policies and charging models, working in unison with network inventory and service fulfilment processes, to deliver the level of service quality customers are paying for and willing to spend more on.
The reality is too many CSPs are stuck making operational decisions manually or offline, which tends to be subjective and, at best, suboptimal—or they are hardcoded inside a BSS/OSS app that stifles change. It’s true that traditional business intelligence and data warehouses have taken some measures to move the industry forward by consolidating data sources into a centralized location. But this data, again, is limited in its functionality to simply reporting. We’re now moving into an era in which big data can—and must—be converted into real-time actionable insight.
The path forward requires the conscious use of advanced analytics and policy control, as there is a very thin line between providing personalized services and intruding on privacy. Multiple consumer studies actually show that the majority of subscribers don’t mind opting in to share their personal information in order to get targeted offers and discounts. To mark this transition toward closer customer interaction, the term CIQ4T, which is shorthand for “Contextual Intelligence for Telecoms,” embodies these ideas, and offers a way to truly engage subscribers versus just simply managing their experience and capitalize on the big opportunities big data presents.