The use of big data technology has become a necessary means for contemporary operators to increase business income and conduct business transformation. More and more telecom operators are gradually using their own data while going on the digital transformation trip.
McKinsey Quarterly conducted a statistical analysis from 80 telecom industry companies (exhibit) and found that big data had a sizable impact on Telco's profits, exceeding 10 percent. Many of them had incremental profits of 0 to 5 percent, and a few experienced negative returns. It can be seen that not all telecom operators can quickly and successfully benefit from the use of big data technologies. But what can we learn from those who are ‘doing it right’?
Improve customer experience
The application of big data technology has made significant achievements in improving the customer experience, especially from the quality optimization of products and services.
a) Call Drop Analysis: A disruption or outage in the network can lead to call drops and poor sound quality, which harms the reputation of the telecom provider, and can increase the attrition among its customers. Call drop analysis collects call detail data (CDR) at a rate of millions of records per second, correlates them with network devices logs and conducts relevant analysis of reasons for call drop. It’s also recommended to drill down dashboard to discover region-wise analysis, enhance data visualizations, and consider revenue loss through subscriber calling patterns.
b) Network Analytics: There are two aspects of network quality assurance that telecoms should take into consideration. One is the failure of a transmission tower can cause service degradation and replacement of equipment is usually more expensive than repair; the other is the trending of telecommunication networks’ migration from traditional hardware and appliance-centric deployments to cloud based deployments, with software as the critical component of all network functionality – NFV or SDN.
Using the network analytics, telecom companies can monitor real-time data from network, compute, and storage applications, which examine data use to react more quickly to potential risks or failures. Big data analytics tools can virtualize their functions and process data on-demand, enabling a-per-unit-of-information processed models.
Reduce Customer Churn
Prediction of customers who are at risk of leaving a company is called as churn prediction in telecommunication. An undeniable fact is that, acquiring a new customer is always more expensive than retaining the old one. Industry trends show that annually there's an over 20-40% churn, especially in the telecommunication industry.
With the help of predictive models and machine learning algorithms, it is possible to identify customers who are likely to lapse accurately. Bringing together data collected on customer usage, complaints, transactions and social media, they can create factors that can identify customers at risk of moving out. Techniques such as decision trees, which enable long-term forecasting and early detection of customer’s value loss and profiling, allow marketers to use variables to easily identify potential churners.
a) Customer Value Segmentation: Identify loyal customers with potential lifetime value for targeted marketing and retention activities to reduce churn.
b) Create tailored products for customers: Broader customer segmentation allows high availability products for each market segment based on customer needs, thereby increasing customer satisfaction.
c) Identifying high-value and long-term customers: Using the integration of big data and other attributes can help to reorder the predictive model, which can help identify customers who are more likely to repeat purchases/buying patterns.
d) Identify potential customers: Big data will help identify new customer segments with high potential in the near future. This helps Telcos identify target customers and cut costs for unaffiliated customer groups.
Broadening business areas:
Telecom operators can use the big data technology to expand their business areas, for example, the Location-based service:
Geographical location data related to mobile devices provide great insights into real-time information. These data sets can be utilized for various analytical services and representations. For instance, telecom operators can reach cost savings through consolidation, monitoring, and optimization. Besides, location data form telecom operators can also contribute to a broader range of industry from intelligent transportation environment to any other public projects.
Through years of in-depth cooperation experience with China Mobile, ARCH kept producing innovations of big data applications in the telecom industry. Some of these technologies are already in use and provide sizable profit growth for China Mobile. For the detailed China Mobile practical case in the international roaming business, you can visit our previous article .
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