The number of smartphone users across the world has exploded over the past decade and promises to do so in the future as well. Additionally, most business functions can now be performed on mobile devices. However, despite the rise of mobile telephony, telecommunications operators around the world are still not this profitable, with average net profit margins hovering around the 17% mark. The main reasons for the average profit rates are the high number of competitors in the market who vouch for the same clientele and the high overhead costs associated with the industry. Communications Service Providers (CSPs) need to become more data-driven to reduce these costs and automatically improve their profit margins. The increased involvement of AI in telecom operations allows telecom companies to seamlessly transition from rigid infrastructure-driven operations to a data-driven approach.
The inclusion of AI in telecommunications functional areas positively impacts CSP results in several ways. Companies can use specific capabilities, avatars, or machine learning and AI applications for this purpose.
AI and predictive analytics: to optimize global telecommunications networks
Mobile networks are one of the main components of the ever-expanding Internet community. As mentioned earlier, a large number of internet users and business operations have gone mobile in recent times. Moreover, the emergence of 5G and advanced applications, and the imminent arrival of the metaverse, will only increase the need for high-performance telecommunications networks. It is very likely that standard automation technology and personnel will be overwhelmed by the relentless pressure of high-speed network connectivity and mobile calls.
Using AI in telecom operations can turn an underperforming mobile network into a self-optimizing (SON) network. Telecom companies can monitor network equipment and anticipate equipment failures with AI-based predictive analytics. Additionally, AI-powered tools enable CSPs to maintain consistently high network quality by monitoring key performance indicators such as traffic on a zone-to-zone basis. Besides monitoring equipment performance, machine learning algorithms can also continuously perform pattern recognition while analyzing network data to detect anomalies. Then, the AI-based systems can either perform corrective actions or notify the network administrator and engineers of the region where the anomaly was detected. This allows telecommunications companies to address network issues at the source before they negatively impact customers.
Network security is another area of interest for telecom operators. Lately, growing security issues in telecommunications networks have been a concern for CSPs around the world. AI-powered data security tools allow telecommunications companies to continuously monitor the cyber health of their networks. Machine learning algorithms perform analysis of global data networks and past security incidents to make key predictions of existing network vulnerabilities. In other words, AI-powered network security tools allow telecom companies to anticipate future security complications and proactively take preventative measures to address them.
Ultimately, AI improves telecommunications networks in several ways. By improving the performance, anomaly detection and security of CSP networks, machine learning algorithms can improve the user experience of telecommunications company customers. This will result in customer growth for these companies in the long run and, by extension, increased profits.
Network behavioral monitoring: to streamline fraud management
Europol classifies the telecommunications sector as particularly vulnerable to fraud. Telecommunications fraud involves the misuse of telecommunications systems such as cell phones and tablets by criminals to embezzle money from CSPs. According to a recent study, telecommunications fraud accounted for losses of 40.1 billion US dollars, or about 1.88% of the total turnover of telecommunications operators. One of the common types of telecommunications fraud is International Revenue Sharing Fraud (IRSF). The IRSF involves criminals teaming up with providers of International Premium Rate Numbers (IPRNs) to illegally acquire money from telecommunications companies by using bots to make an absurdly high number of long-duration international calls . These calls are difficult to trace. Additionally, telecom companies cannot charge customers for these premium calls because the connections are fraudulent. Thus, telecommunications operators end up bearing the losses of these calls. IPRNs and criminals share the spoils. Apart from IRSF, vishing (a portmanteau for voice calls and phishing attacks) is a way in which malicious entities trick customers of telecommunications companies into extracting money and data. The involvement of AI in telecommunications operations allows CSPs to detect and eliminate these types of fraud.
Machine learning algorithms help telecommunications network engineers detect cases of illegal access, fake caller profiles and cloning. To do this, the algorithms perform behavioral monitoring of CSPs’ global telecommunications networks. As a result, network traffic along these networks is closely monitored. The pattern recognition capabilities of artificial intelligence algorithms come into play again as they allow network administrators to identify contentious scenarios such as multiple calls from a fraudulent number or empty calls (a general indicator of vishing) repeatedly passed from dubious sources. One of the most prominent examples of telecommunications companies using data analytics for fraud detection and prevention is Vodafone’s partnership with Argyle Data. The data science-based company analyzes the telecom giant’s network traffic for intelligent data-driven fraud management.
Detecting and eliminating telecommunications fraud are major steps towards increasing CSP profit margins. As you can see, the role of AI in telecom operations is important in achieving this goal.
Robotic Process Automation: to improve back-office processes
To reliably serve millions of customers, telecom companies must have a massive workforce that can effectively manage their day-to-day back-end operations. Dealing with such a volume of customers creates several opportunities for human error.
Telecommunications companies can use cognitive computing – a robotics-based field that involves natural language processing (NLP), robotic process automation (RPA), and rules engines – to automate rule-based processes. rules such as sending marketing e-mails, automatically completing electronic forms, saving data and performing certain tasks that can reproduce human actions. The use of AI in telecom operations brings greater precision in back-office operations. According to research conducted by Deloitte, several telecommunications, media and technology industry executives have found that using cognitive computing for backend operations brings “substantial” and “transformative” benefits to their respective companies.
Chatbots, virtual assistants, sentiment analysis: to improve the quality of customer service
Customer sentiment analysis involves a set of data classification and analysis tasks performed to understand the pulse of customers. This allows telecommunications companies to gauge whether their customers like or dislike their services based on raw emotions. Marketers can use NLP and AI to detect the “mood” of their customers from their texts, emails or social media posts bearing a telecommunications company’s name. Aspect-based sentiment analysis highlights the exact service areas where customers are having trouble. For example, if a customer is upset about the number of regularly dropped calls and writes a long, incoherent email to a telco’s customer service team about it, learning algorithms used for sentiment analysis can still autonomously determine one’s mood (angry) and problem (call abandonment rate).
Besides sentiment analysis, telecom companies can benefit immensely from the growing emergence of chatbots and virtual assistants. Service requests for network configuration, installation, troubleshooting, and maintenance issues can be solved with these machine learning-based tools and applications. Virtual assistants allow telecommunications company CRM teams to easily manage a large number of customers. In this way, CSPs can successfully manage customer service and sentiment analysis.
Overall, users generally cite the quality of their telecom customer service as being below satisfaction. Telecom users are constantly frustrated with long wait times to access a service manager, unanswered complaint emails, and poor handling of complaints by CSPs. A poor CRM does not bode well for telecom companies as it damages their reputation and diminishes shareholder confidence. By implementing machine learning for CRM, telcos can effectively address these issues.
Like companies in any other industry, telecom companies need to increase their profits to survive and diversify for the long term. As stated at the beginning, there are many factors that hinder their chances of generating profits. Going the data science route is one of the new ways to overcome these challenges. By involving AI in telecom operations, CSPs can manage their data wisely and channel their resources towards revenue maximization.
Despite the benefits associated with AI, only a limited percentage of telecom companies have integrated the technology to maximize their profits. Gradually, we can expect this percentage to increase.