How Machine Learning Is Creating New Opportunities In The Telecommunications Industry

Julie Stoughton

Artificial intelligence (AI) and machine learning (ML) are boosting innovation in every industry, providing opportunities for new ideas that are accelerating digital transformation.

In the telecommunications industry, AI and ML may be the core technologies that help organizations build new revenues and stronger, more loyal customer relationships.

To learn more about how telcos are impacted by these technologies, we surveyed them at Mobile World Congress and asked how AI and MI would affect their businesses in the near future. For 93% of those surveyed, these technologies will be a game-changer, and within three years or less, 76% will have incorporated AI and ML into their businesses. There are forerunners that have already adopted AI (12%) and many that are proactively in the exploratory and discussion stages (62%) of investigating how to apply the technologies.

Why are telcos adopting AI and ML so rapidly?

Here is a quick look at what these technologies can do for telcos:

  • In the past, while the growing volume of data is an asset for telcos, slow information processing still presented a challenge. Now, with AI and ML, telcos can analyze bigger, more complex data and deliver faster, more accurate results – even on a massive scale. This provides the telcos with insights on how to identify profitable opportunities and avoid unknown risks.
  • AI and ML are considered core technologies in digital transformation, something that many telcos are experiencing today. They can be embedded in a variety of business areas, such as customer experience, network automation, business process, and new digital services to help telcos build new business models and improve operations.
  • These technologies are also critical for the success of the evolving 5G, both from both a network and a connectivity perspective.
  • They can reduce operational costs and optimize the customer experience by reducing customer friction, personalizing experiences, and enabling businesses to achieve higher margins with greater efficiency.

Some Tier-1 telcos — like Vodafone, NTT, Telefonica, Orange, SK telecom, and AT&T – have already recognized the power of these technologies and have either launched or planned to launch their own AI platform.

How can telcos unleash the power of AI and ML?

To see how these technologies can help telcos and communication service providers (CSP), I’ll share a few potential applications:

Use case #1: Improving margins

CSPs are switching their focus from revenue growth to margin protection and improvement, and as this shift occurs, they must look not only at corporate margins, but also at individual customer ones. This has been a challenge for many because customer data is spread amongst a variety of heterogeneous data sources.

But with AI and ML, telcos can leverage current and historic Big Data such as customer usage, purchasing patterns, and social links. And they can combine it with data from BSS, OSS, and ERP systems to create multi-dimensional and high-granular insights into potential customer margins. They can then apply ML algorithms to create action plans for each individual, recommending ways that ultimately optimize and increase margins while improving the customer experience. These plans can then be integrated with CRM systems so sales and service staff can offer them to customers when they visit stores or contact calls centers.

Use case #2: Optimizing mobile tower operations

Maintenance at mobile towers can be challenging for CSPs, as they require frequent time-consuming on-site inspections to make sure that all the infrastructures, including passive equipment such as power generators and air conditioners, are running properly. In addition, CSPs are concerned about intruders that want to steal the valuable equipment at a tower.

To overcome these challenges, CSPs could use AI-empowered video and image analysis with surveillance cameras at towers. With the use of AI, irregular events such as intrusion, fire, and smoke, could automatically be detected and personnel can be alerted in real time. IoT sensors could be installed at towers and ML algorithms could analyze the data and combine it with surveillance camera data for 360-degree continuous monitoring and alerts. All this data could also be integrated with workforce dispatching systems and material ledgers to identify when on-site maintenance or spare parts replacements are required, reducing the number of inspections and unneeded downtime. And active network configuration information can be cross-checked with asset management systems to maximize network utilization and improve coverage.

Use case #3: Delivering better customer service

Finally, let’s look at how AI and ML can provide better 24/7 interaction and resolution through customer service automation. Telcos often get complaints about the connectivity of customer premise equipment (CPE), such as broadband modems and internet protocol television (IPTV) boxes. Dispatching technicians to resolve these issues can generate a large number of on-site visits that are often unnecessary because problems could be solved with a simple reset.

By deploying a 24/7 customer service chatbot, customers can receive instant responses and resolution to many issues through ML-enabled automation in service ticketing and relevant systems like fulfillment and assurance. ML algorithms can recognize fault models based on historic information, including network log and service ticket data. The chatbot can then detect and identify common solutions for connectivity issues, resolving customer issues quickly and easily. The chatbots can also identify which failure patterns need technician dispatches and which don’t. When integrated with customer service and OSS systems, automatic resolutions and instructions such as a CSP or telco reset could help avoid on-site maintenance, reducing the number of unnecessary technician dispatches and truck rolls, and saving significantly.

These scenarios are not futuristic strategies – they are practical tactics already in use today. To learn more about them and about how telcos and CSPs are taking advantage of AI and ML, read this free e-book: Turn Thinking into Doing, SAP Machine Learning for Telco.

Julie Stoughton

About Julie Stoughton

Julie Stoughton is the Head of Telecommunications Marketing & Communications at SAP. She is a seasoned professional with 16 years of marketing and product marketing experience in software and media technologies. Julie's specialties include strategic market development, positioning and messaging, customer segmentation, product launches, ROI analysis, and go-to-market execution.