The telecom sector has continuously grown despite the pandemic to meet the constant demands for high-speed networks, increased network capacity, and wireless deployments. Smartphones, broadband, 5G networks have massive amounts of data available to them from their large subscriber base with great potential. At the same time, researchers have predicted that the telecom analytics industry will grow at a compound rate from 2020 to 2027.

In recent years, data analytics, along with AI and ML, has been one of the most critical aspects of business transformation, while data is modern-day gold for industry business. The interconnected network of devices in the universe produces large volumes of data. Telecom companies gather that data, analyse it, and derive practical insights. Industries have realized that not taking advantage of business intelligence through data stand are missing out on various opportunities. Very soon, data science is becoming the need of the hour for telecom companies to survive and thrive for the next 15-20 years.

Let’s look at some of the use cases that analytics is solving in the telecom sector:

Need for improved customer experience

The humongous data gathered from call centres, CRM systems, and other data sources can help telcos understand customer pain points and challenges. Tens and millions of CDRs can help to analyse the patterns that could derive problem statements, get insights through data visualizations at scale, and use predictive analytics to reduce factors related to call quality drivers. 

Many telcos are using speech analytics to analyse customer calls and understand the sentiment of the customer and how they can improve the customer experience of their call centres.

Telecom industries can use edge computing analytics to use bandwidth more accurately, improve network visibility and deduct operational costs. Edge computing analytics combines advanced processing powers, AI, and state-of-art connectivity and provides business intelligence for better connectivity and greater automation.

Optimize Field Services

With ready-to-deploy analytics solutions in place, businesses can now get customized dashboards basis their defined scenarios and ML algorithms that could reduce the unprecedented service calls and in-person visits. Telcos can further improve their self-service capabilities, deploy virtual assistants that can guide the customer in self-service, and do smart scheduling of field service based on predictions. Based on the history of encountered problem statements by the customer, AI could deploy the right technicians, prioritize the high-risk calls, and resolve low-risk calls through remote engineers. 

Reduce Customer Churn

With a nearly 20-40 percent churn rate per year, companies are facing very high customer churn as a major key challenge. Predictive Churn analytics and customer profiling can help businesses identify customers who are likely to churn and who might still be potential loyal. Businesses can create a Churn prediction framework by using data science techniques such as decision trees and logistic regression models to classify their subscribers.

Big data and Network Optimization

There are algorithms today that can identify the root cause of the issue, detect it in real-time and restart or optimize the network performance through software or by human intervention before it comes to the customer’s attention. With AI systems, businesses can understand their tower behaviour and optimize the behaviour based on real-time usage data. 

Telecommunications analytics of big data allows companies to grip a more enhanced view of their business operations. The data scientists and engineers can help with this by providing intelligent business insights and last-minute solutions through end-user analytics, avoiding network failures, and improving the security and growth of telecom networks. 

Conversational AI

Businesses have already understood that call abandonment and poor customer experience can affect reputational costs. That’s where conversational AI’s play key role with 24/7 self-serve and also guide the agent by assisting them in understanding customer behaviour in real-time, providing suggestions on the question to be asked and what to be answered. This also helps in avoiding manual human errors and automating or identifying the task that can be automated.

Personalized Sales

With usage data, companies can create recommendation engines to suggest to customers relevant upsell and cross-sell their products and services. They can assess which package will best suit the customers and increase their sales success rate. The self-learning algorithms can be made best use to accumulate insights into which packages could match the different customer needs, thereby reducing the burden on sales teams.

Author’s Bio

Saurabh Rai is an industry-recognized thought leader, an experienced advisor on GIS, Geospatial Technology, Process Initiatives, Data Science, Data Architecture, Data Engineering, Artificial Intelligence, Machine Learning, Technology, Analytics, User Experience, Data-driven businesses, Customer Success, and Consumer Insights.