The growth of mobile networks has not only made it possible for NGOs, Governments and service organizations capable of delivering public services, it has also made telcos (telecom companies) the focus of data based decisions.
But, the economic value of data does not lie externally for telecom firms. It lies in the effective use of analytics on the collected mobile data to segment the userbase and maximizes revenue.
Revenue Assurance & Fraud Management
Telecom firms have been facing huge revenue pressure due to regulatory changes and increased competition. As market penetration increases so does the competition and price wars. Moreover, the threat of a disruptive technological interference is more than real in the telecom market. With price sensitive customer ready to turn to the cheapest available option, a player with deep pocket can easily capture a market share.
Latest case: “Reliance Jio captured 100 million subscribers within six months of its launch.”
There are many reasons for revenue loss or leakages for telcos (see figure 2). A great part of revenues loss can be attributed to numerous frauds as well.
The good part is latest big data analytics can help telecom firms in identifying and plugging these loopholes. All you need is a big data analytics solution which is capable of tracking and predicting user behavior and bring actionable insights for the operator. These insights can be used to engage the consumer in a way that ensures revenue maximization.
The same system can identify the fraudulent users and check such activities on the network.
Figure 1: Revenue Leakages for Telecom Companies
Source: Telecom Business Review | SITM Journal Volume 6 Sep 2013 | Next Generation Revenue Assurance | Sunny Gajbhiye
Customer Experience Management (CEM) System
Predictive big data analytics can help telcos to analyse consumer behavior and predict:
- Attrition possibility
- Purchase propensity
- Next most-likely purchase
With such information, operators can optimally engage the customer to enhance customer life and higher revenue generation possibility. Profiling customers as per their usage and offering them multiple service points can create a higher engagement level for the customer.
Big data analytics also helps in measuring campaign performance, and marketers can improve their strategies to enhance campaign ROIs across domains.
Network Analytics
Data analytics requirements have changed significantly. Large big data analytics solutions could be useless if unable to generate actionable insights. To provide a seamless user experience on the network the data analytics system should be able to:
- Capture real and synthetic data from various data points
- Plot and correlate the data points over time for millions of customers
- Automatically offer insights or answer complex questions related to user and the usage on the network
- Receive feedback and improve
Fortunately, modern big data analytics suites can perform this complex activity.
Trends in Big Data Implementation
In April this year, International Data Corporation (IDC) published an update to the Worldwide Semiannual Big Data and Analytics Spending Guide. As per the update, the worldwide revenues for big data and business analytics (BDA) is expected to reach $150.8 billion in 2017, an increase of 12.4% from the last year figures.
At the same time, it also predicted the growth of 11.9% (CAGR) for Commercial purchases of BDA-related hardware, software, and services till 2020. The expected total revenues by 2020 were quoted at $210 billion.