How does the telecom industry leverage this big data? Through smart application of big data analytics strategies!
As more data is generated in the telecom sector, big data analytics will emerge as more critical for telecom companies to maintain their competitive advantage, elevate customer experience, and improve efficiencies.
The good news is that data analytics can play a crucial role across various telecom business areas. To that end, let’s discuss some of the telecom use cases where big data can play a role.
How Telecom Companies Are Leveraging Big Data
With access to a wide variety of data sources, telecom companies are sitting on a”goldmine” of data. But how can they maximize their value with actionable intelligence?
Let’s have a look.
1. Customer Insights
Using data analytics, telecom companies are converting both structured and unstructured customer data into customer insights. Many telecom companies are using customer data to develop relevant customer KPIs, personalized or targeted offers, and customer profiles.
For example, using customer insights, sales teams in telecom companies are creating targeted campaigns based on the following customer information:
- Purchase history
- Device
- Geographical location
- Customer preferences or interests
Data analytics is also a useful tool to reduce customer churn. McKinsey has previously reported that the telecom sector can predict and reduce customer churn by as much as 15% by leveraging data analytics. In the same vein, effective data analytics can enable telecom players to predict their customer lifetime value (CLV) and future cash flows.
2. Network Infrastructure Optimization
With the increase in subscriber base, telecom networks are concerned that they will reach maximum capacity and utilization. For network optimization, telecom players are extracting valuable insights from various data sources pertaining to data usage, network
logs, and peak load traffic.
To cater to their growing user base, telecom players are leveraging data analytics to identify the most traffic-congested areas and expand their capacity. Further, telecom companies are using data technology to reduce their network’s energy consumption by 15-20% annually.
Besides real-time analytics, telecom players are also building forecasting models based on predictive analytics. This is useful for predicting and preventing future network outages.
3. Fraud Prevention
The telecom industry loses around $12 billion due to subscription-related fraud. According to industry estimates, telecom players lose around 2.2% of their revenues to various types of fraud and leakage.
As such, telecom companies are now turning to big data and machine learning technologies to detect and prevent cybercrime. According to the Communication Fraud Control Association (CFCA), data analytics and machine learning can detect 350% more fraudulent activities.
How does this work? Data analytics tools can analyze massive volumes of network and customer data to detect any deviations from normal patterns.
4. Operational Analytics
Thanks to growing market competition, telecom companies are under constant pressure to maintain their operational performance. Poor network bandwidth and performance can directly influence customer churn and impact revenues.
Using telecom (or operational) analytics, telecom players can improve their profits by optimizing their network usage and other services. Operational analytics in the telecom sector is the way forward for providing valuable insights into telecom operations. Telecom players can now monitor real-time network traffic (using heat maps) and decide to increase (or reduce) bandwidth during off or peak hours.
Additionally, real-time operational analytics enable telecom operators to plan their equipment maintenance schedule and data updates.
5. Optimized Pricing
In the uber-competitive telecom industry, product pricing is a key factor that has a direct impact on the subscriber base and revenues. To increase their market share, telecom players are adopting a dynamic pricing approach that maps both the customer’s lifetime value and current tariffs.
Using big data analytics, telecom companies can optimize their pricing strategy based on data-driven insights. They can design pricing strategies based on market reaction to changes in pricing, the customer’s purchase history, and competitor prices.
Additionally, telecom companies can determine their pricing strategy based on the expected ROI, product value, and profits & revenues. A data-optimized pricing strategy is important to boost sales activities and revenues and increase the subscriber base.
Next, let’s discuss how Ellicium can help with data analytics services.
How Ellicium Can Help Telecom Companies with Big Data Analytics
As a Big Data engineering company, Ellicium provides its customers with big data solutions that deliver accurate business insights from massive data volumes. Here are some of our big data-related services for the benefit of our customers:
- Designing the right big data implementation roadmap.
- Launching big data initiatives to improve ROI.
- Improving the performance and efficiency of big data systems.
- Providing managed services in big data to streamline business operations.
The Ellicium team is skilled in various big data technologies like Hadoop, Apache Spark, Google Big Query, and Cloudera. Here is a success story of implementing a Hadoop-based data lake for a leading U.S.-based telecom company.
Conclusion
The telecom sector is ripe for business transformation driven by big data analytics. With the growing volume of smartphones and customer data, analytics is the technology that will differentiate successful telecom companies from the rest of the market.
At Ellicium, we deliver customized big data services that can help your company grow to the next level. Let’s partner in your data journey. Contact us today.