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3 min

How to Use AI for IT Operations in the Telecom Industry

Optimizing resources, streamlining operations, and improving network management

George Petersen

Head of Telco-enabled Projects

Eva Petritski

Content Manager

November 13, 2024

Intro Managing data for optimized costs and predictive maintenance Transforming network operations with automation and analytics Case Study: implementing AI for sales forecasting

Managing data for optimized costs and predictive maintenance

The amounts of data that Telcos today have access to are exploding: customer data, interactions, network events, etc. To make use of this information to improve customer experiences, telecom companies need to oragnize and manage this data in real-time:

Here’s where telecom companies can use AI for IT operations: identify and correct duplicates, errors, and inconsistencies so that only high-quality data is used for data analysis and decision-making. Or continuously monitor data inputs to detect anomalies or deviations from expected patterns.

AI and machine learning algorithms can also analyze historical data to predict future trends, such as customer behavior or network performance issues. When applied to 5G and internal data network management, AI can have predictive maintenance to reduce downtime. And by evaluating patterns in data, it can help to identify potential risks, such as customer churn, which can enable companies to implement targeted retention strategies.

Transforming network operations with automation and analytics

Data-driven operations have become a priority for telecoms globally. Leading operators aim not only to improve operations through automation, analytics, and AI, but to largely make every operation within their ecosystem more data-driven.

One thing that usually hinders this transition is the lack of synergy between core underlying processes and the data. And analytics is crucial to bridge this gap:

This three-step approach can help achieve that:

Automate routine processes. This will help to improve efficiency and accuracy:
— Automate the provisioning of services (e.g., setting up new accounts or activating services)
— Automatically detect and respond to network issues, reducing downtime and improving customer service quality
— Streamline billing and payment processing to minimize errors and enhance customer satisfaction.
Analyze customer behavior, network performance, and operational efficiency. It will help you get meaningful insights to inform business decisions:
— Mark customer usage patterns, preferences, and potential churn risks
— Continuously analyze network data to identify trends, bottlenecks, and areas for improvement
— Measure key performance indicators (KPIs) to evaluate the effectiveness of different processes.
Implement AI. This will allow to enhance operational efficiency and customer interactions:
— Identify at-risk customers based on their usage patterns and trigger proactive retention strategies
— Automate customer support with AI-driven chatbots or virtual assistants to handle customer inquiries, reducing the load on human agents and improving response times
— Detect anomalies in network data that may indicate problems, enabling faster resolution and minimizing service disruptions.

Case Study: implementing AI for sales forecasting

Furukawa Electric is a Japanese telecommunications company that provides solutions for broadband and smart cities. They decided to implement artificial intelligence into their Sales and Operations Planning transformation in LatAm to enhance their forecasting accuracy and receive more revenue.

Here are the key steps they followed:

Tailored machine learning models
‍Their product offerings spanned multiple markets with diverse requirements, so the team decided to not use a one-size-fits-all model. Instead, they identified key variables impacting sales for different product families, and prioritized the most representative items.
Prepared and analyzed data
They reviewed and prepared data for the AI models, then analyzed over 12,000 items to determine which data points were most critical for forecasting accuracy.
Developed predictive models
‍Using machine learning techniques, they created predictive models tailored to their key product families. These models utilized historical sales data and other relevant inputs to improve forecasting.

With these changes, Furukawa Electric was able to decrease the average waiver from 28% to around 3%. Besides that, the company reduced inventory costs and decreased product families by around 50% and 20%.

This is just one example of how to leverage AI and data science in telecom industry. Some telecom companies use it to entice customers with better experience, and others shift their focus to digital ecosystems to maximize revenue from one subscriber. If you want to learn more about digital solutions for telecom, visit this page or book a call with our team.

last update

November 13, 2024