Deep Learning in Telecom: Parting the Sea of Data

deep learning in telecom

91.53 percent of the world population own a phone, including smart and feature phones. As a result, the amount of generated data is mindboggling, close to 2.5 quintillion bytes per day. And this number is set to skyrocket further in 2023 with the rise of IoT. As such, telcos will need a helping hand to go through it and make the best out of it. That’s where deep learning in telecoms comes in. If you are not familiar with it, it’s machine learning’s more complex son.

Before We Begin…

While both are filed under Artificial intelligence (AI), deep learning (DL) is far more complex than machine learning (ML). The former designates computer systems that learn and adapt automatically from experience sans explicit programming. Take a music streaming service as an example. It learns your preferences through likes, library additions, or logging which songs you listen to fully and offers new suggestions. Conversely, the latter layers algorithms and computing units (akin to human neurons) into what is known as an artificial neural network. Such a network allows data to pass in a non-linear fashion (just like we, humans, can jump to conclusions). For instance, Alexa asks follow-up questions when encountering a knowledge gap. So, these space parsers allow Alexa to understand gaps and extract new concepts.

Now, Deep Learning in Telecoms

DL is imperative in telecoms as it constitutes a central pillar of predictive analytics with so much data generated daily (from texts, SMS, video calls, etc.). These types of analytics result in better decisions regarding resources, network management, marketing strategies, and client support.

  • Network Optimization: AI-based systems can better keep up with complex and often unstructured datasets. It finds and alerts about issues leading to network crashes or downtimes in the sea of data.
  • Predictive Maintenance: Big data solutions can analyze historical and real-time data from the telecom equipment collected via IoT, preventing issues before they occur.
  • Predictive Issue Identification and Increased Network Security: Detecting issues and anomalies early, deep learning in telecoms helps companies avoid downtime expenses and a stain on their reputation.
  • Real-Time Analytics: The technology is tasked with monitoring things like data throughput, packet loss, location, traffic, and real-time analysis. Doing so helps telecom operators tailor services and solutions for other operators in real time.
  • Fraud Detection:  Predictive analytics allow the prevention of these telecom frauds. The technology studies the dataset containing legitimate and fraudulent actions. It is then capable of finding patterns and potential flagging fraud.
  • Price Optimization: The model can make intelligent pricing decisions when fed structured and unstructured data identified as relevant for sales metrics. This solution is typically combined with a dynamic pricing strategy in which prices change even hourly.
  • Customer Segmentation: Deep learning segmentation enables businesses to fully understand customer preferences, uncovering connections that would otherwise go undetected.
  • Customer Churn Prediction: The model can predict which clients will likely cancel their service subscription. Once identified, the telecom service provider takes actions (e.g., sending promotional offers and personalized messages), increasing retention among specific target groups.
  • Marketing Intelligence: The company can estimate market opportunity more accurately, pick the most promising strategies, and better understand its competition and customers. The program extracts these accurate data insights from the datasets.

So, What’s in It for Me?

Simply put, despite it looking like the telcos are implementing this for their bottom line’s benefit, it ultimately benefits you as a business owner. Implementing deep learning in their companies means a couple of things for you and your business.

  • By forecasting the traffic, they can pick the best time for technical work to minimize their impact on your satisfaction and choose their area of optimization focus.
  • Relying on state-of-the-art prevention software results in less downtime and, by extension, less inconvenience for you. Meaning business as usual with a slight hiccup.
  • As a business person, your biggest worry is the security of your business data, primarily if you work in a field that handles sensitive information. The predictive analysis leads to preventative measures that ultimately support your cyber security.
  • The real-time analysis contributes to your security by lessening the chances of not catching a bug or a fault in time in the central server.
  • The reduction in expenses is directly linked to a decrease in prices, so the cost of the desired bundle decrease. This change in pricing triggers a reduction in your own expense.
  • You are safe from telecom fraud, but in reality, a whole category targets you, the customer. We’re talking SMS phishing frauds, account seizures, international revenue-sharing frauds (IRSF), and much more. So you see, a secure telecom provider means an at-peace mentality to you.

Final Thoughts

Deep learning is the artificial equivalent of a primitive human brain. And its uses are vast and complex, especially in the telecoms industry. Business account owners are sure to benefit from its integration in this domain. Here’s some advice, but feel free to take it with a grain of salt, and stay up to date with your operator, as you never know when they will make a move that will benefit you and feed YOUR bottom line.

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