Machine Learning and Its Impact on The Telecom Industry

Mar 27, 2020

Machine learning (ML) is poised to reshape every aspect of the telecommunications industry – neither the facts nor the hype should be neglected. Real-time insights, powered by machine learning and AI, is a game-changer for reducing costs, creating new revenue streams, enhancing customer experience and achieving the scale demanded by the IoT and 5G technologies. This post aims to present a succinct overview of machine learning’s potential and how it can drive significant innovation across the telecom value chain.

Machine Learning and Its Impact on The Telecom Industry

(Pixabay / kRYoS42)

Machine Learning Use Cases in Telecom

Machine learning applications are infiltrating the core of the digital transformation age and several telecoms are living it. Verizon, for instance, now relies on machine learning algorithms to track data streaming from a plethora of network interfaces. This data comes from an array of sensors collecting temperature data from customers’ routers and more. It allows them to predict many customer-impacting events before they occur and take measures to mitigate them. Other tier-one telecoms like AT&T and Vodafone are also using machine learning to enhance the quality of resources.

It’s, therefore, no surprise that a number of business models that rely on machine learning are already starting to emerge within the industry. Here are a few key areas where new and existing companies can unleash the power of machine learning.

  • Enhancing Cell Tower Efficiency

Cell tower maintenance presents a big challenge for telecom companies since it demands regular on-site inspections to ensure that all of the infrastructure is functioning properly. To address this challenge, organizations could use ML-backed visual analysis coupled with security cameras at the towers. Machine learning can be used to automatically detect irregular events like smoke, fire, and intrusion.

Sensors could also be strategically placed at towers and algorithms could analyze the information and mix it with camera data for continuous monitoring. The data could also be linked with material ledgers and equipment dispatching systems to determine when part replacement is needed. Proper implementation of these devices can help improve overall coverage.

  • Identifying Churners

With customer churn becoming a frequent occurrence for network operators, many of them are investing in pattern-matching solutions to identify relevant churners. However, these solutions are far from efficient and demand frequent maintenance. The good news is that machine-learning algorithms are being introduced to gain insights from new data to understand the reasons behind customer churn and adapt as fresh patterns emerge.

ML can also help companies understand why certain subscribers have turned to their competitors and the steps they can take to improve customer retention (by offering favorable data usage policies, for instance).

  • Detecting Anomaly and Fraud

Because of the fact that telecom companies host a large number of users on a daily basis, there’s a high chance of anomaly and fraud. Illicit activities, such as theft, fake authorization, and account cloning, damage the business-customer relationship. That’s where machine learning algorithms can come in handy. Unsupervised algorithms can help telecoms detect abnormal activity or characteristics of active customers.

Analyzing previous data of customers also enables ML algorithms to visualize and report on anomalies in real-time. This is especially useful as it empowers telecoms to notify their users (and law enforcement agencies) of fraudulent activity immediately, protecting the business-customer relationship.

  • Segmenting Customers for Better Campaigns

When it comes to marketing campaigns, success heavily relies on efficient segmenting and delivering content according to audiences’ traits and preferences. This rule also applies to the telecom industry. Machine learning can help companies create accurate audience segments based on relevant customer traits. This will enable them to better target customers with relevant marketing materials, increasing their chances of making a sale.

What’s unique about machine learning is that it can segment customers in real-time. As certain individuals change behaviors and preferences, they’ll be re-segmented based on whatever changes are most prominent. The result? The chances of targeting people with irrelevant campaigns become low, and the staff that once had to segment and re-segment these groups manually gets more time to work on other tasks.

  • Increasing Marginal Income

With the adoption of machine learning, telecoms can also utilize historical and current data points like buying patterns, social links, and customer usage. These data points can then be combined with data from ERP systems to develop granular and multi-dimensional insights that translate into improved margins. ML algorithms can be used to set up action plans for every customer, suggesting steps that ultimately optimize marginal income while enhancing the customer experience.

The new plans can also be incorporated into CRMs so sales reps can share them with potential customers when they contact a call center or visit a retail outlet. Service uptake can also be improved as machine learning algorithms that are designed on behavioral data analysis are fed across the network.

  • Improving Customer Service

One way to leverage machine learning in this area is to create an always-on chatbot. Chatbots can get instant responses and offer resolutions for different issues through ML-enabled scripts in relevant systems and service ticketing. Algorithms can also recognize anomalies based on historical data, including service ticketing and network log information. The bots can then identify and suggest solutions for connectivity hiccups, addressing customer complaints quickly and easily.

Chatbots can also help predict when someone will contact the customer service team (and why). Additionally, they can be set up to encourage self-service. With integrated OSS systems and self-service, automatic resolutions could help free up support personnel, as well as reduce unnecessary helpline calls and technician dispatches. All of this should translate into significant savings.

Conclusion

With their wide reaching and massive infrastructures, telecom organizations stand to benefit greatly from these machine learning technologies. The most successful companies will be those who are committed to staying up to date with the latest ML practices when it comes to analyzing the data points flowing through their networks.

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