Category: Telecom

The telecommunications industry is the backbone of technological growth and digital transformation. While revenues in the telecom sector declined in the first half of 2020, owing to economic impact as a result of the pandemic, GSMA reported that recovery took place in the second half of the year with increased consumers and households acquiring integrated bundle services and high-speed connectivity becoming sought after across all rungs of society.

If anything, telcos have learned in the past year that they need to support communities and enterprises to become more digital, using flexible and scalable approaches to cope with volatilities. As countries set sights on becoming digital economies, the telecommunications industry needs to jumpstart transformation from within, using artificial intelligence (AI) and tools like machine learning, deep learning and natural language processing (NLP) to capitalize on its connectivity capabilities and the vast amount of collected data to drive new business opportunities and seek growth.

Projected to reach US$13.45 billion by 2026, the global AI market value in the telecommunications industry is expected to grow at a CAGR of 49.8 percent from 2021. Within the sector, AI can be harnessed to bring about multi-pronged benefits to telcos.

Any transformation must first begin from within. To ensure robust networks to support growing demand in IoT and connectivity, telcos can only stand to benefit from developing their network infrastructure. One such way is building self-optimizing networks (SONs) to automatically improve network quality.

GSMA Intelligence estimates 8.6 billion mobile connections by 2025, up from 7.9 billion in 2020. Of which, two-thirds of the 600 million new connections will arise from APAC and Sub-Saharan Africa. When pressured to provide higher quality and faster networks, telcos can turn to AI applications and algorithms to manage their networks. Using AI-powered solutions, telcos can predict network congestions and address issues using historical data to prevent outages. For telcos, this means leveraging data to perform predictive analytics and anticipate failures based on past patterns. Using AI-powered cameras, IoT sensors and machine learning, real-time monitoring and maintenance of mobile towers can also be carried out effectively and efficiently.

Telcos are constantly challenged to improve customer services. In competitive telco landscapes where services are priced not too differently, a rewarding customer journey is what stands out. While humans have limited ability to make sense of the vast amount of data, AI and machine learning can make use of the same set of data to identify consumer behaviors and patterns to better predict and influence outcomes from every point of contact with a consumer. For instance, these tools can also be used to identify and divert customer service calls that can be easily resolved to eventually reduce service calls and operator costs.

Using intelligent virtual assistants or chatbots on AI-powered customer platforms can automate and conduct one-on-one conversations to provide maximum support at reduced business costs.

With prevalent low loyalty rates and high churn rates in the industry, telcos can leverage AI and automation to conduct churn analysis and predictions to map out strategies to achieve customer loyalty. Using algorithms, telcos can work on customers’ profiles to provide meaningful insights into consumers’ behaviors. This allows telcos to personalize customer journeys and deliver seamless customer experiences, providing custom products and services that lead to maximum uptake, and eventually revenue.

Conversational service automation (CSA) platforms tap on conversational AI, robotic process automation (RPA) and NLP to understand sentiments and manipulate human language to better handle communications and encourage upselling.

According to the 2019 Cyber Telecom Crime Report by Europol and Trend Micro, global telecoms fraud costs the industry US$33 billion each year. Of which, International Revenue Share Fraud (IRSF) is the most common fraud scheme encountered. With accelerated digital adoption worldwide and the growing sophistication of cyber-attacks, this figure is expected to be on the rise. One way to counter such errant activities is using fraud management systems that perform real-time fraud detection via machine learning algorithms to mine historical fraudulent activities and detect anomalies and threats in the telecom network.

Essentially, data is king. In today’s data-driven landscape, telcos can seek growth opportunities by tapping on the power of data to drive insights across industries and their digitalization trajectories. To stay abreast, telcos must develop future-proof solutions to spearhead transformation and generate growth, using analytics to make sense of future paths. For the telecommunications industry, accelerated AI adoption has become a necessity.