The telecom industry has consistently increased despite the pandemic to meet the growing demand for fast networks, higher capacity for networks, as well as wireless deployments. Smartphones, broadband and 5G networks are awash with information available to them through their huge subscriber base, which has huge potential. In addition, research has predicted that the telecom analytics market will grow at a rapid rate between 2020 and 2027.
In recent times the field of data analytics, alongside artificial intelligence (AI) and machine learning (ML0029 has become among the top crucial aspects of business change, and data is the modern-day gold for business in the industry. The interconnected network of devices across the universe generates huge volumes of data. Telecom companies accumulate this data, study it, and draw practical information. Industry leaders have realized that companies that aren't using data-driven business intelligence being left out of numerous opportunities. Soon data science will be an essential aspect to ensure that telecom companies be successful and survive in the coming 15 years.
Need for Improved Customer Experience
The vast amount of data collected from call centers, CRM systems and other sources of data can assist telcos in understanding the customer's issues and problems. The millions and tens of millions of CDRs could help identify patterns that could be used to identify problems, gain insights from data visualizations on a large scale, and apply predictive analytics to identify the elements that affect driver factors that affect call quality.
Many telecom companies use speech analytics to analyze calls from customers and to understand the mood of their customers and ways to improve the customer experience in their call centers.
Telecom companies can benefit from edge computing to make use of bandwidth more efficiently, increase the visibility of networks and reduce operating costs. Edge computing analytics blends powerful processing capabilities, AI, and state-of-art connectivity, and offers businesses with greater connectivity as well as greater automation.
Optimize Field Services
With ready-to-use analytics installed, companies can now create custom dashboards based on their specific scenarios, and ML algorithms that can reduce unimaginable number of customer service calls as well as in-person visits. Telcos could further enhance their self-service capabilities by deploying virtual assistants to assist customers in self-service and make smart schedule of their field services based on predictions. Based on the previous history of problems reported by customers, AI could deploy the most appropriate technicians, prioritize most risky calls, and solve calls with low risk using remote engineers.
Reduce Customer Churn
With a 20-40% annual rate of churn businesses are confronting high levels of customer churn as a major issue. Predictive churn analytics and customer profiling could help businesses find customers most likely to be churning and could still be loyal. Businesses can build and churn prediction framework applying data science methods such as decision trees or logistic regression models to categorize their customers.
Big Data and Network Optimization
There are algorithms available today that can identify the primary reason for the problem and identify it in real time and then restart or improve the network's performance using software or through human intervention prior to it coming to the attention of the customer. With AI technology, companies can learn about their tower's behavior and improve their behavior by analyzing real-time usage information.
Big data analytics in telecoms gives companies an improved view of their operations. Data engineers and scientists can assist with this by providing savvy business insight and solutions using end-user analytics. to avoid network failures and increasing the security and performance telecom network security.
Conversational AI
Companies have realized that call abandonment and a poor customer service can impact reputational cost. This is where conversational AI plays an important role in providing 24/7 self-service and assist agents by aiding with understanding the behavior of customers in real time and offering suggestions for the type of question that needs to be addressed and how is required to be addressed. This helps to avoid mistakes made by humans and making the task easier or finding one that could be automated.
Personalized Sales
Utilizing data on usage, businesses can build recommendation engines to offer customers upsells that are relevant to them and cross-sell their goods and services. They can determine which product is most suitable for their customers and boost their sales rate. The self-learning algorithms could be used to gain insights into which products will best meet diverse needs of the customers, thereby decreasing the load of sales staff.
A Path to Improved Business Profits by Leveraging the Power of Telecom Analytics
By identifying problems and offering the best course of action before they have an impact on customers, telecom analytics may assist service providers increase their profitability, boost customer happiness, and increase efficiency. In fact, the ideal telecom analytics platform should be able to resolve even the most challenging customer experience problems while delivering insights that may influence a variety of crucial choices and actions, such as creating more aggressive packages and offers.
About the Author
Prabhjinder Bedi
Chief Growth Officer - Tech Mahindra Business Process Services
Bedi has over two decades of experience involving launching start-up ecosystems, scaling-up businesses, successfully taking product and services to market across industry verticals, spanning telecom and media, hi-tech/new economy, financial services, retail and consumer goods, manufacturing and life sciences. Having spent over 16 years at Tech Mahindra, Bedi is currently responsible for taking our existing and new age service offering to global markets and adding meaning to our shareholders, partners, and customers. Bedi is bachelor’s in engineering from IIT, Benares and a Master of Business Administration (MBA) from IIM Calcutta.
