There are these two sets of companies: the traditional telcos who are facility based and provide landline/ mobile/ Internet services and who control the “last mile” access to network services; and the Internet companies who are often non-facility based and provide platform based services that typically ride on the infrastructure of telcos. Of late, the innovation by the likes of Amazon, facebook, Netflix, and Google, who belong to the latter category are breaking the “walled garden” approach of the telcos and emerging as significant threats to the business models of the telcos.
For example, the search algorithm of Google, the recommendation engine of Amazon, the movie ranking heuristics of Netflix, the co-visitation indexing by YouTube crunch enormous amount of usage data to provide personalised user experience. Simple voice services in India generate about 6-10 billion Call Detail Records (CDRs) per day per operator. Given the vast volume of data gathered, one would assume that telcos to be leading in generating insights using this data. But, it is the Internet companies who have taken a huge lead over the telcos in this area.
The problem
The Telecom Industry has been going through rapid transition. Some of the trends that have emerged threaten to reduce operator relevance for customer. These trends include: change in customer preference from using traditional voice communication to data communication, competition from niche ISPs and Cable companies who offer Internet service and strong emergence of Over The Top (OTT) players like Skype and WhatsApp that provide substitutable services such as Internet Telephony and Instant Messaging. Increased competition, falling margins and limited opportunity for growth due to market saturation are additional challenges that are impacting the telcos. To counter these impacts telcos need to offer differentiated and personalised experience to retain customers and to remain relevant in their mind.
For example, the billions of CDR data can give telcos answers to some of the following questions: What time of day do the subscribers call each other? How frequently do they call each other? How long are the calls? How many messages do they exchange? Metrics such as these give an idea about the strength and intensity of their interactions and create a map of their social network. Evidence is abound as to how this personalisation can reduce the churn rate and improve loyalty of subscribers, especially in hyper competitive markets such as the U.S., and India.
The way out
Operators such as Verizon in the U.S. have used subscriber and related data to address specific needs of a particular segment – say, Micro, Small and Medium Enterprises (MSMEs). Information such as the customer’s location, the corporate structure, number of employees, quantity of and type of services across voice, data, IP and managed services , help the operators get a 360 degree view of the customer and hence better target their services and provide a personalised offerings, thus improving customer retention. What are the criticalities of the applications used – help telcos offer appropriate pricing plans, provide flexibility to users to shift their demands economically, and better manage network resources.
Subscriber data analysis can also provide information regarding adoptions and profitability of existing products and services. Armed with these insights, telcos can respond faster to market by developing new products and offering wide range of new value-added services cost efficiently. Telcos need to use data analytics to gain insights from the vast amount of data residing in their Billing Support Systems such as identifying most profitable segment of customers; their needs and buying patterns so that these can be used to improve overall quality of customer experience.
Though telcos have mastered the art of Erlang calculations of the voice era, network capacity planning for diverse data services is often much trickier than for plain vanilla voice traffic. This can also help in devising appropriate pricing plans suitable for different segments of customers based on the usage and its effect on network capacity and performance.
Indian operators though marked the revolution of “sachet pricing” in voice and recently in data services, need to go beyond myriad of pricing plans and invest in big data analytics to differentiate their service and quality of offerings to survive in the hyper competitive market place.
(The writers are employed at Sasken Communication Technologies. Views are personal.)