August 12, 2021

Top 5 Uses of Ai in Telecommunications

Companies in the telecommunications (telecom) industry are benefiting greatly from artificial intelligence (Ai) and the practical use of Ai through machine learning. For example, Gartner research indicates that product leaders must understand the realistic adoption and potential impact of strong Ai technologies across the telecom industry.

What are some of the best ways Ai can help the telecom industry? Let’s take a look at the top 5 uses and how they can help your company succeed.

Network Optimization

Network optimization is used to improve the network’s performance. Global load balancing, minimizing latency, packet loss monitoring, and bandwidth management are all a part of network optimization to ensure the highest levels of service for users.

How can machine learning methods help with network optimization? Through a basic workflow model. Taking the problem formulation to define the key parameters of the problem and determine the data needed to run the model, a channel is then established to collect the data (e.g., traffic patterns, performance logs, etc.) and process it through feature extraction and data filtering.

After the data points are analyzed, the model is trained and explicitly programmed on the data to provide ready-to-use outputs that are used to begin optimizing the network. Along the way, the model is fine-tuned by comparing the network performance with the model prediction of previous stages. The training data is then optimized to provide the most accurate pattern recognition.

Preventative Maintenance

Preventative maintenance is the regular and routine maintenance of networks and equipment to keep them running effectively and to prevent costly unplanned downtime from unexpected failures.

Telecom companies can alleviate crisis management problems by leveraging machine learning techniques to extract actionable insights. These insights include pattern shifts in service location usage and degradations, network infrastructure inspections through drone footage and computer vision, optical switch maintenance and node failure predictions, and much more.

Ai can also help you find breakpoints and repair the breaks without human intervention. During an Emerj podcast, AT&T’s Mazin Gilbert identified predictive maintenance as a major Ai initiative within the company to help with locating and repairing network breakpoints.

Preventative maintenance is a great way to prevent customer churn as unexpected failures are mitigated. If a failure does occur, machine learning can quickly resolve the problem or direct repairmen to the exact failure spot.

Robotic Process Automation (RPA)

Robotic process automation (RPA) uses a combination of integrations, advanced technologies, and cognitive processes to automate specific tasks. Telecom companies can benefit from RPA because the manual, repetitive, and rules-based processes the industry currently suffers from can be turned into quick and actionable processes.

RPA can help Telecom companies reduce error rates, improve their data quality, increase customer satisfaction, make operations more efficient, and reduce costs. RPA lacks intelligent qualities that help it to learn and improve itself over time. Therefore, RPA in conjunction with machine learning is a great investment with a high ROI.

Machine learning models can be inserted into RPA workflows to perform machine perception tasks, such as computer vision’s image recognition. This is sometimes referred to as hyperautomation, an infrastructure of advanced technologies used to scale automation capabilities in an organization.

Customer Service and Virtual Assistants

Excellent customer service is crucial to retaining customers. Part of the trend for improving the customer experience is the inclusion of virtual assistants. These virtual assistants can either be placed on the company’s website or over the phone to help answer questions.

Marketing efforts can be improved through machine learning with personalized campaigns. This increased flow of traffic to the website can affect customer service by not quickly addressing questions. Virtual assistants can help drive customer acquisition by managing that flow of increased traffic. This leads to conversion-rate improvement that helps to increase the company’s revenue.

Virtual assistants can also help reduce churn by maintaining a high level of customer service with 24/7 first-line support to cover the initial troubleshooting phase. Customers are quickly directed to the answers they seek or to the right department to address any remaining concerns.

Personalized Customer Experience

Personalizing the customer experience is another way to provide excellent customer service. Personalization is the process by which profile and behavioral data are leveraged to create highly relevant and customizable experiences for prospects and customers across multiple channels.

By creating a personalized customer experience, you can build and strengthen customer relationships that drive engagement, more sales, and thus more revenue. Anticipating and delivering products or services to prospective customers without them having to put in the effort, you’ll have a higher success rate because the customer then has a frictionless experience.

Machine learning is an intelligent system that can help companies provide a personalized journey with specific offers based on what the customer is most likely to be interested in. Ai can present real-world offers based on the customer journey and behavior, thus improving conversion and order value.

How ElectrifAi Can Help

ElectrifAi’s domain knowledge and vast library of pre-built machine learning models can help telecom companies with their digital transformation. Cutting out the overhead costs associated with building your own machine learning models and the time it would take to build (with no guarantee of success), ElectrifAi offers business-ready solutions.

Are you ready to join the world of Ai and machine learning? Contact us today for a custom demo!

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