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The number of smartphone users across the world has skyrocketed over the last decade and promises to do so in the future too. Additionally, most business functions can now be executed on mobile devices. However, despite the mobile surge, telecom operators around the world are still not that profitable, with average net profit margins hovering around the 17% mark. The main reasons for the middling profit rates are the high number of market rivals vouching for the same customer base and the high overhead expenses associated with the sector. Communication Service Providers (CSPs) need to become more data-driven to reduce such costs and, automatically, improve their profit margins. Increasing the involvement of AI in telecom operations enables telecom companies to make this switch from rigid, infrastructure-driven operations to a data-driven approach seamlessly.

The inclusion of AI in telecom functional areas positively impacts the bottom line of CSPs in several ways. Businesses can use specific capabilities, avatars or applications of machine learning and AI for this purpose.

Mobile networks are one of the prime components of the ever-expanding internet community. As stated earlier, a large number of internet users and business operations have gone mobile in recent times. Additionally, the emergence of 5G and edge applications, and the impending arrival of the metaverse, will simply increase the need for high-performance telecom networks. It is very likely that the standard automation tech and personnel will be overwhelmed by the relentless pressure of high-speed network connectivity and mobile calls.

The use of AI in telecom operations can transform an underperforming mobile network into a self-optimizing network (SON). Telecom businesses can monitor network equipment and anticipate equipment failure with AI-powered predictive analysis. Additionally, AI-based tools allow CSPs to keep network quality consistently high by monitoring key performance indicators such as traffic on a zone-to-zone basis. Apart from monitoring the performance of equipment, machine learning algorithms can also continually run pattern recognition while scanning network data to detect anomalies. Then, AI-based systems can either perform remedial actions or notify the network administrator and engineers in the region where the anomaly was detected. This enables telecom companies to fix network issues at source before they adversely impact customers.

Network security is another area of focus for telecom operators. Of late, the rising security issues in telecom networks have been a point of concern for CSPs globally. AI-based data security tools allow telecom companies to constantly 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-based network security tools enable telecom businesses to pre-empt future security complications and proactively take preventive measures to deal with them.

Ultimately, AI improves telecom networks in multiple ways. By improving the performance, anomaly detection and security of CSP networks, machine learning algorithms can enhance the user experience for telecom company clients. This will result in a growth of such companies customer base in the long term, and, by extension, an increase in profits.

How Telecom Companies Can Leverage Machine Learning To Boost Their Profits

The Europol classifies the telecom sector to be particularly vulnerable to fraud. Telecom fraud involves the abuse of telecommunications systems such as mobile phones and tablets by criminals to siphon money off CSPs. As per a recent study, telecom fraud accounted for losses of US$40.1 billionapproximately 1.88% of the total revenue of telecom operators. One of the common types of telecom fraud is International Revenue Sharing Fraud (IRSF). IRSF involves criminals linking up with International Premium Rate Number (IPRN) providers to illegally acquire money from telecom companies by using bots to make an absurdly high number of international calls of long duration. Such calls are difficult to trace. Additionally, telecom companies cannot bill clients for such premium calls as the connections are fraudulent. So, telecom operators end up bearing the losses for such calls. The IPRNs and criminals share the spoils between themselves. Apart from IRSF, vishing (a portmanteau for voice calls and phishing attacks) is a way in which malicious entities dupe clients of telecom companies to extract money and data. The involvement of AI in telecom operations enables CSPs to detect and eliminate these kinds of fraud.

Machine learning algorithms assist telecom network engineers with detecting instances of illegal access, fake caller profiles and cloning. To achieve this, the algorithms perform behavioral monitoring of the global telecom networks of CSPs. Accordingly, the network traffic along such networks is closely monitored. The pattern recognition capabilities of AI algorithms come into play again as they enable network administrators to identify contentious scenarios such as several calls being made from a fraudulent number, or blank callsa general indicator of vishingbeing repeatedly made from questionable sources. One of the more prominent examples of telecom companies using data analytics for fraud detection and prevention is Vodafones partnership with Argyle Data. The data science-based firm analyzes the network traffic of the telecom giant for intelligent, data-driven fraud management.

Detecting and eliminating telecom fraud are major steps towards increasing the profit margins of CSPs. As you can see, the role of AI in telecom operations is significant for achieving this objective.

To reliably serve millions of clients, telecom companies need to have a massive workforce that can handle their backend operations on a daily basis efficiently. Dealing with such a large volume of customers creates several opportunities for human error.

Telecom companies can employ cognitive computinga robotics-based field that involves Natural Language Processing (NLP), Robotic Process Automation (RPA) and rule enginesto automate the rule-based processes such as sending marketing emails, autocompleting e-forms, recording data and carrying out certain tasks that can replicate human actions. The use of AI in telecom operations brings greater accuracy in back-office operations. As per a study conducted by Deloitte, several executives in the telecom, media and tech industry felt that the use of cognitive computing for backend operations brought substantial and transformative benefits to their respective businesses.

Customer sentiment analysis involves a set of data classification and analysis tasks carried out to understand the pulse of customers. This allows telecom companies to evaluate whether their clients like or dislike their services based on raw emotions. Marketers can use NLP and AI to sense the "mood" of their customers from their texts, emails or social media posts bearing a telecom companys name. Aspect-based sentiment analytics highlight the exact service areas in which customers have problems. For example, if a customer is upset about the number of calls getting dropped regularly and writes a long and incoherent email to a telcos customer service team about it, the machine learning algorithms employed for sentiment analysis can still autonomously ascertain their mood (angry) and the problem (the call drop rate).

Apart from sentiment analysis, telecom businesses can hugely benefit from the growing emergence of chatbots and virtual assistants. Service requests for network set-ups, installation, troubleshooting and maintenance-based issues can be resolved through such machine learning-based tools and applications. Virtual assistants enable CRM teams in telecom companies to manage a large number of customers with ease. In this way, CSPs can manage customer service and sentiment analysis successfully.

Across the board, users generally cite the quality of their telecom customer service to be below satisfactory. Telecom users are constantly infuriated by long waiting times to get to a service executive, unanswered complaint emails and poor grievance handling by CSPs. Poor CRM does not bode well for telecom companies as it maligns their reputation and diminishes shareholder confidence. By implementing machine learning for CRM, telecom companies can address such issues efficiently.

Like businesses in any other sector, telecom companies need to boost their profits for long-term survival and diversification. As stated at the beginning, there are multiple factors that thwart their chances of profit generation. Going down the data science route is one of the novel ways to overcome such challenges. By involving AI in telecom operations, CSPs can manage their data wisely and channelize their resources towards maximizing revenues.

Despite the positives associated with AI, only a limited percentage of telecom businesses have incorporated the technology for profit maximization. Gradually, one can expect that percentage to rise.

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How Telecom Companies Can Leverage Machine Learning To Boost Their Profits - Forbes