Why is Big Data Analytics important in telecommunications?
The telecommunications industry holds a wealth of customer information, which is why it is an ideal sector to benefit from a powerful data analysis tool that can help parse complex data into actionable intelligence.
Compared to other industries, however, the telecommunications industry does prioritize data analytics to increase data visibility and use data insights to improve operational efficiency across the entire telecom value chain, from network operations to service offerings to product development, marketing, and sales.
Surprisingly, there are still many telecom companies far from capitalizing on their data, putting themselves at risk by delaying big data analytics adoption. Many companies are also slow to adopt due to the lack of expertise or constraints around the scalability of legacy systems.
Given that data in the telecommunication sector is both structured and unstructured, unifying data from various sources—such as call detail report, network data, social media, and equipment sensors—is a challenge to consider.
3 ways telcos can prepare to meet future customer demands
1. Prepare network and infrastructure
Big data analytic tools can help companies become robust, optimized, and scalable. They provide companies with infrastructure, which is now a basic requirement for companies to differentiate themselves. Data is undoubtedly growing rapidly for the telecommunication sector. Just think about how much your data can grow if you are capturing customer call data, or maybe even the real-time data gathered through connected devices.
Deprioritizing the adoption of big data analytics may fail in the future as many organizations will be ill-prepared to play catch up. In other words, it might be too late.
2. Understand your customer for better service
Analyze network traffic in real time—social media data, call logs, and other sources—to serve customers better with differentiated offerings. Drill down into issues that impact customer satisfaction, plan ahead to delight customers with more control and precision, and apply predictive analytics to forecast customer churn.
Remember that your existing customers are a valuable source of ongoing business. If a customer exits a service network, the costs of acquiring a new one is significantly higher.
3. Customer-centric product development
Analyze customer behavior and usage patterns to help design products and services that are catered to current and future demands. Innovate by developing entirely new products, identify opportunities to introduce new product features and new product extensions, and improve existing product lines.
Adopt a proactive, rather than a reactive approach. Don’t devalue your product or your brand by planning your next big move based on your competitor’s actions. With big data analytics, stay in control and make intelligent decisions based on your company performance data which will exert more influence to your market.