Transforming Telecom Sector Through Data Analytics
The telecom sector is undergoing a transformation. This is primarily owing to evolving customer needs, an upsurge in technological innovation and the changing landscape of the industry itself. In this context and to keep pace with competition, service providers are undertaking innovative measures to reduce customer churn and increase the average revenue per user.
Today, the challenges before any operator are multi-fold and broadly include-an overcrowded industry, technological parity between players and low switching costs from one operator to another. These challenges have a direct impact on the operator’s already wafer-thin margins. The situation is worsened by the emergence of over-the-top (OTT) players and the substantial investments in 3G and 4G networks.
In this scenario, predicting and analyzing a customer’s usage patterns in a timely manner is imperative for reducing churn and increasing profitability. To understand the multiple nuances of a customer’s usage patterns, a well-architected predictive analytics model that goes beyond basic manual monitoring is required. A predictive analytics model analyses a customer’s past and current usage patterns to predict future outcomes.The basic idea supporting the concept of “predictive analytics” is that the more information available to a telecom company today, the better their ability to determine a future scenario for their operations and processes. Additionally, predictive churn models also provide a churn propensity score against each customer. This enables an organization’s marketing team to tailor the offers while highlighting the most compelling offers to high risk-high value customers, thus optimizing their costs. Another advantage of predictive models is that they can be customized to respond to certain customer segments. Thus, one can even deploy region-specific churn models.
To illustrate-for an operator with close to 1 million customers, a 10 per cent reduction in customer churn will be equal to a 1 per cent increase in annual revenue. With the help of campaigns designed for customer retention, the rate of uptake can be as high 20-25 per cent, which in turn translates to significant revenue.
Globally, the uptake of data services is increasing substantially. According to a CLSA report, the number of mobile data subscribers in the country is expected to grow two-fold to reach 501 million by 2017-18.This will be fuelled by increasing consumer engagement with mobile phones, availability of low-cost smartphones and expansion of data networks. With such avast repertoire of data, “personalization” can encompass historic behavior, social interactions, preferences and the demographic parameters of each subscriber. These parameters can be used to group subscribers in very narrow buckets for personalized targeting with campaigns, to respond to inbound calls withup-sell /cross-sell campaigns, to predict take rates based on past behavior of subscribers with similar attributes etc.
Need of the hour is a high precision real-time analytics driven marketing solution that drives revenue growth through contextual marketing with a focus on customer engagement and retention. Service providers need to be equipped with tools to develop and optimize strategies to capture and enhance customer value. Imagine a tool that would take subscriber usage data from multiple sources, including transactional call data records, recharge records, billing systems and VAS service usage records, to identify a wide range of service usage and interaction patterns. More interestingly, imagine if the same tool designs finely targeted campaigns to ensure greater customer receptiveness and uptake - whilst avoiding revenue cannibalization.
The application of predictive analytics for customer value management is wide and pervasive. Once fully utilized, I believe the Indian telecom sector will have the potential to make a mark globally and overcome the hurdles it faces currently.
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