Predictive Analytics in Telecom: How Deep Learning Is Bringing a New Competitive Edge

The telecom industry is one of the most data-intensive industries in the world. With so many calls and texts being transmitted every day, it’s no wonder that predictive analytics is becoming such an important part of telco operations. By using predictive analytics, telcos can make better decisions on allocating resources and managing their networks, planning marketing strategies, and supporting their clients. With the advent of deep learning, predictive analytics has taken on a whole new level of sophistication. Deep learning plays a key role in making predictive analytics more accurate and efficient. In this article, we will explore various ways in which predictive analytics is used in telecom and see how deep learning is changing the game.

Complex Predictive Analytics Based on Deep Learning – Where Is the Edge?

For a long time, predictive analytics would rely heavily on statistical modeling. Whereas the techniques that fall under this category can solve simple problems, their accuracy is poor when it comes to more complex ones. The inference of the statistical models relies on small samples. Deep learning, a machine learning technique based on neural network architecture, on the other hand, finds predictive patterns in larger datasets. AI and deep learning are changing the landscape of today’s telecom industry. The logic established in such a manner is applicable to the different cases without the risk of biased results.

Due to its capability to deal with big data, deep learning works perfectly for predictive purposes. With their many hidden layers, the neural networks bring the prediction process to the next level, being able to process numerous input variables and find associations among them. No human involvement is necessary, and that’s the clue. With such amounts of data, traditional non-automated analytics would be counterproductive.

Many types of neural networks can serve for predictive analytics, including supervised learning models (Recurrent Neural Networks, Multilayer Perceptron, or Deep Boltzman Machine), unsupervised learning models (Autoencoders), and both (Convolutional Neural Networks, Deep Belief Network). Even though deep learning models take more time to train and require more extensive datasets to provide accurate results, it is not the issue with telcos that usually have tons of data at their disposal.

Predictive Analytics in Telecom Industry – Use Cases

Among all the industries, telecom can definitely boast of the broadest use of predictive analytics. That’s because of the complexity of their services and the tight bonds between them. A small software or hardware failure can cause a domino effect, quickly escalating into something much bigger and harder to fix. Plus, since the telecommunication companies usually operate on a big scale, any issue is generally more felt by the customers, affecting the customer experience much more than in other industries.

We’ve drawn up the most common use cases of predictive analytics in telecom in order to give you a hint of the role it plays in this sector.

Network Optimization

With customers getting used to fast, uninterrupted connection very easily, the mobile broadband service providers have to face increasing pressure on constant optimization of their services. Clients set the bar high in terms of the speed and performance of the network. Until everything works smoothly, they do not really think about the quality of the service, taking it for granted. But when some issue occurs, it impacts their experience heavily and right away. Thus, continuous optimization is the telecom service providers’ core focus. And there is no other path towards it than via advanced data analytics.

Since their networks are constantly expanding and gaining complexity, telecom companies need efficient AI-based systems that can cope with complex and often unstructured datasets. Predictive modeling enables them to find information about issues that could lead to network crashes or downtimes in the sea of big data.

Thoughtfully-built predictive models trained with quality datasets find patterns in the past data, helping the service providers to prevent issues instead of constantly putting down fires. By forecasting the traffic, they can pick the best time for technical works to minimize their impact on customer satisfaction and choose their area of optimization focus.

Predictive Maintenance

Since the poor maintenance of the equipment is the most common cause of network downtimes, predictive models can really make a difference in terms of customer experience for telecom companies. Naturally, the more complex the network becomes, the more difficult it is to control it. Reactive maintenance, regardless of how fast and effective it is, is always a worse option than predictive maintenance in this case.

Using artificial intelligence, telecom firms can constantly monitor the equipment that may be prone to failure. It allows them to exchange the network infrastructure elements before their work-life comes to an end and detect the processes that may speed up their damage instead of acting ad hoc. With big data solutions, they can analyze historical and real-time data from the telecom equipment collected via IoT, preventing issues before they actually occur.

Predictive Issue Identification and Increased Network Security

Every downtime is costly – also because bringing the network to the pre-failure state takes much more time than the actual fixing works. Even if their equipment is well backed-up, every minute of network outage costs the telecom companies a lot of money and affects their reputation.

Predictive modeling supports downtime prevention by detecting issues and anomalies at the very early stage. Appropriately trained models can also identify security issues that could lead to data leaks and infrastructure damage. Now, with the extensive use of IoT, the telcos are particularly vulnerable to these.

Real-Time Analytics

With the ever-growing amounts of transferred data and the evolution of the next G technologies, it is also important to take a closer look into monitoring networks. 5G network performance and dependability are important issues in 5G use cases, including autonomous cars and their remote management. CSPs can use predictive analytics to assist their partners in making 5G proactive and predictive when it comes to network capacity planning. Predictive analytics improves the quality of the experience, which is required by use cases such as autonomous vehicles, warehouse and plant robots, and 5G drones.

Monitoring things like data throughput, packet loss, location, traffic, and real-time analysis with predictive analytics can show you what is going to happen. Real-time analytics coupled with predictive analytics can help telecom operators tailor services and solutions for other operators in real-time. Predictions can help in traffic rerouting

By harnessing the power of deep learning, they can bring a new level of competitive edge to predictive analytics. This technology is still in its early days but it has already shown a lot of promise and is something that all telecom companies should be watching closely. You can read a detailed use case from Nokia here: AVA NWDAF - Network Data Analytics Function.

Fraud Detection

When it comes to fraud danger, the first industries that usually come to mind are banking and insurance. Let’s not forget, however, that the telecom industry is equally susceptible to fraud. The spectrum of possible telco frauds is broad. SIM swapping, deposit fraud, interconnection bypass fraud, telecom arbitrage fraud – all these can cost the telecom company millions every year, affecting its financial safety. Some affect the service provider while leaving the clients untouched, and others harm both parties which makes their prevention even more significant.

Among the most common frauds that fall into the second category, there are SMS phishing frauds, account seizures, and international revenue-sharing frauds (IRSF). The takeovers are particularly problematic since, in most countries, the telecom industry still lacks regulations regarding chargeback procedures in the case of unauthorized access. Whereas introducing them would definitely be beneficial, the telecom companies should also put a strong emphasis on prevention.

Predictive analytics allows them to do so. Based on the dataset containing both legitimate and fraudulent actions, the model can find patterns and red flags that could imply potential fraud. Since it’s often a compilation of potentially harmless factors that would not raise suspicion during human verification, engaging a complex neural network seems the best solution.

Just as in the case of insurance fraud detection, the main challenge may be to get the dataset right. Fraud attempts are just a drop in the ocean of calls, messages, and other network inquiries, which doesn’t make the deep learning algorithm training part easy. There are solutions to that - if you’d like to see how we’ve dealt with it when developing a fraud detection system, read our case study.

Price Optimization

Traditional pricing methods often stop companies from maximizing profit since they force them to play safely to avoid losing potential and actual customers. In the telecom industry, where the services are mainly long-term-oriented and subscription-based, going for a safe option is even more tempting. However, it always generates a loss in the long run.

With predictive modeling, telecom companies can estimate the maximum price they can charge for their service without losing customers in order to create optimal pricing strategies. The model fed with the structured and unstructured data that were identified as relevant for the sales metrics can make intelligent pricing decisions. Such a solution is usually paired with a dynamic pricing strategy in which prices change even hour by hour.

Customer Segmentation

Considering their business model, the telecom companies value every customer since the acquiring process can be challenging due to long-term commitments. Customer segmentation helps them both to attract clients and retain them. The more personalized the communication is, the more chances there are for a better deal– it applies to practically any industry, but telecom in particular.

Using a clustering technique, telecom service providers can group big, often unstructured customer data in an unsupervised way, saving time and maintaining maximum accuracy. While manual segmentation is often based on assumptions, the segmentation via deep learning enables companies to fully understand the customer preferences, finding connections that would be otherwise undetectable.

Customer Churn Prediction

Since the telecommunication companies work mainly with a subscription-based model, they have to adopt a slightly distinct approach to sales and customer service. Their focus shifts on strengthening retention rather than acquiring new clients all the time. Plus, customer acquisition is much more costly than retention-oriented actions, so it’s important for telecom companies from a financial point of view.

With predictive analytics, they can detect the clients likely to cancel the subscription to the service. Once they are identified, the telecom service provider takes actions (like sending promotional offers and personalized messages, for example) proven to enhance retention among a particular target group.

In this case, the model gets fed with data from customers who have left and those who extended the service in order to identify correlations between each decision and different factors. It could be, for example, a high rate of cancellations between clients who have reported more than two issues in total or have been spending more than X monthly. After the model makes sense of all this, the telecom companies can use this newly established logic to flag potentially “risky” customers.

Marketing Intelligence

Predictive analytics also serves as a tool for dealing with marketing intelligence, understood as external data about the market the company has just entered or plans to enter. The purpose of processing this data is to make better-informed market-related decisions and gain a competitive advantage. With marketing intelligence, the company can estimate market opportunity more accurately and pick the most promising strategies, as well as understand their competition and customers better. These accurate data insights can be extracted from the datasets with predictive analytics.

Customer Lifetime Value Prediction

As we have already mentioned, the nature of telecom services enforces a more deliberate approach to sales strategy. Not only do the companies from this sector invest much more in retention-oriented actions – but they also heavily depend on the customer lifetime value calculation. This indicator is basically the sum of all the revenue a particular customer will generate.

Relying on the CLV, the telecom company can plan and prioritize its actions in order to maximize revenue. With this parameter, they can see which clients are likely to be unprofitable and which are likely to stay with them for long. How? The predictive algorithm can identify the correlations between the customer lifespan and different data based on the previous company records. Having found the patterns, it can estimate the customer lifetime value of the newly acquired customers or those about to be acquired.

Why Telecom Companies Should Invest in Deep Learning-Based Solutions for Predictive Analytics?

As you can see, based on the examples above, predictive analytics is an immensely versatile tool. Combined with other AI-based solutions such as IoT, it can optimize the company’s external and internal processes, increase margins, improve the customer experience, reduce risks, and prevent financial loss. Check other industries where predictive analytics can be used.

It is unlikely that the businesses will step away from data science instead of continuing to invest in it. Almost 500 exabytes of data are created every day ( World Economic Forum), and this number will only continue increasing. We are undoubtedly heading towards a data-driven future; without embracing deep learning, it will be impossible to make the most of that opportunity. So, why not do it sooner than later?

If you’re considering applying predictive analytics for any of the purposes described above or have another idea on how to use it, reach out to us! We have implemented it for various industries, including pharmaceutical manufacturing, fintech, or last-mile logistics, and cannot wait for new challenges in this field.