The battle for the hearts and minds of mobile users has never been fiercer and much has been spoken of the benefits of Big Data as a panacea for all ills. This article will take a look at the various ways in which mobile operators can harness Big Data to improve customer value management and tackle some of the major challenges facing operators in today’s crowded market place.
Mobile operators across the globe are seeing their ARPU decline with the stagnation of revenues from traditional voice and messaging services. Undoubtedly, they are struggling to get to grips with the multiple challenges of the threat from OTT players, combined with the need to invest in new infrastructure to accommodate the burgeoning growth of data and video content. These multiple pain points highlight the importance of customer satisfaction and a growing fascination with operator’s Net Promoter Scores as a means of measuring their subscriber’s loyalty and likelihood to recommend their services.
Yet measuring customer satisfaction means tapping into the various customer touch points, spanning diverse operations such as network operations, customer service and sales, to gain a full 360degree picture of the customer’s experience. If mobile operators are to emerge victorious in the battle to attract and retain customers they need to be able to collect, analyse and action information from a huge range of different sources.
The Three ‘V’s of Big Data
As more and more data flows within the carriers’ networks and the trend is set to maintain an upward curve with the advent of location-based services and M2M applications collecting ever greater volumes of data: the challenge is not only one of managing the ‘Volume’ which is intrinsically related to the ‘Variety’ of sources of data in different structures. These range from traditional CRM databases to information gleaned from the network itself (such as MSC, probes, IN, Wi-Fi, Femtocell etc...) to customer services, marketing interactions and many more. The full extent of the challenge becomes clear, with the necessity of combining the ‘Velocity’ at which the operator needs to capture and process the information, which is a determining factor for competitive advantage.
Unless the data can be harnessed and become actionable then it has limited use. The question of customer privacy is also of paramount importance when it comes to providing a contextual and customer-centric service that requires the analysis of personal location information: subscriber’s permission is mandatory. On the other hand, when subscriber location data is collected at a mass-scale for the creation of internal and external geomarketing reports for example, the opt-in might be optional.
The creation of tailored advanced services requires moving beyond the traditional one-dimensional information such as gender, age, address, type of mobile device and network information and incorporating contextual information regarding preferences, tastes and interests combined with powerful geolocation data.
This combination offers a myriad of possibilities for personalised offers most likely to appeal to the consumer, that can be delivered consistently regardless of the method of customer engagement; whether it be on the web, through a customer service representative, or via an MMS. However timing is everything: any delay will impact on the success of the interaction and satisfaction can soon change to dissatisfaction if an offer is timed inappropriately, in the same way that annoying PPI telesales calls during the evening generally miss their mark. Such pinpoint accuracy is tangential to a positive outcome for both consumer and operator, but difficult to achieve, requiring near real-time data updates and the ability to process and analyse vast quantities of dissimilar information.
The ability to harvest Big Data to improve customer experience and value can work in a variety of ways; either to propose contextual offers, but equally to enhance the operator’s knowledge and ability to target or anticipate future needs or actions, based on an analysis of past activities. Let’s take the example of the French Tier 1 SFR, a customer of Intersec, who has aggregated location based data from diverse scenarios such as the dispersal pattern of spectators leaving the Stade de France after a European football match, to anticipate future public transport services. Another example is the anonymised data from motorway visitors to a region in France that’s been used by a government tourist organisation to identify the optimal places to promote visitor information.
So how does the operator benefit from investing in systems to gather, analyse and action Big Data? Well, in a number of different ways by improving the targeting of information and services to better match the customer’s individual needs, by reducing costs due to better targeting of information and by increasing ARPU and wallet share by offering relevant, contextual, real-time offers and services at the optimal time and place for each individual subscriber.