The telecom industry deals with myriad data sources ranging from network design and operations to an extremely diverse set of customer data. Businesses need to proactively manage this data in order to be able to set up and optimize the right infrastructure and devices and sell the right products or services at the right time. Machine learning and artificial intelligence offer cost-effective ways to solve this challenge, and AI use cases in telecom are popping up all over the place.
AI in the telecom industry isn’t just hype – it’s already changing how we interact with the world and each other, making it possible to interact with people, services, and devices. A new wave of innovative telecommunication companies are leveraging the power of machine learning (ML) and artificial intelligence (AI) to solve the unique challenges of the networking sector, in particular leveraging the capability to process petabytes of data in real-time at lower costs than ever before – creating unique revenue opportunities within the telecom business.
In this article, we present a long list of top AI use cases in telecom, as well as examples of companies that have implemented these use cases successfully.
Predictive Maintenance
Using a few lines of code, you can train an ML algorithm to detect faults with various equipment on your networks—like switches and routers—and then predict when these faults will occur. When a fault is detected, you can automatically generate an alert. This frees up engineers’ time so they can focus on solving problems instead of logging alerts and filling out service tickets and support requests.
You can also use AI system to trigger a predictive maintenance operation at certain points along your supply chain. For example, as soon as new routers come into your data center, you could train an algorithm to detect when one stops sending traffic or has downtime that exceeds normal operating levels. This way, you know whether it’s time for routine maintenance or something more serious like hardware failure needs to be investigated further. By using predictive analytics to automate some basic maintenance tasks, you can save operational costs by reducing unscheduled downtime and increasing operational efficiency.
Network Optimization
Artificial intelligence is poised to have a big impact on how networks are built and maintained as increasingly intelligent automation systems via more informed decisions about how to route traffic, design bandwidth, and improve security – integrated into network management systems. The Cisco DevNet Artificial Intelligence in Networking hub has a set of best practices for network optimization that promises to use AI to make future networks more flexible and resilient.
This goes beyond typical quality-of-service monitoring; it aims to optimize both bandwidth usage (as is done today) and power consumption. It suggests using deep learning models that get faster at classifying traffic over time without manual updates. It can also accurately predict network anomalies by listening for abnormal activity, such as unexpected spikes or dips in traffic, which can be flagged for further investigation.
Customer Churn Prediction
Churn prediction in the B2C segment is a standard telecom use case. Imagine, you as a B2B business, could predict your company’s rate of customer churn before it happened? Using a machine learning model, telecom companies can forecast when a customer or a subscriber is at risk of churning. They can then take action to reduce their churn rate.
For example, they may proactively reach out to their customers to help them fix an issue that might cause them to leave. The more accurate your predictions are, and therefore, your responses are, you’ll experience fewer actual customer losses. Plus, you’ll create happier customers who stick around longer.
Automatic Service Configuration
One use case for AI is automatic service configuration. To do that, telecoms can deploy machine learning algorithms as a gateway between their network management and other systems. The model would quickly figure out optimal parameter values for a given application or customer and then adjust those parameters automatically whenever necessary. While trained on data from the previous usage, such a network automation system can also learn from new experiences by observing how actual customers, and their customers, behave.
Virtual Assitant
New technologies like virtual assistants are allowing telecom companies to automate business processes such as data entry and customer requests, reducing costs while increasing quality. For example, IPsoft’s Amelia platform is a virtual assistant that can answer basic queries with near-human accuracy. Amelia uses machine learning algorithms to learn how to respond accurately to different questions across all industries, including telecom.
In addition, Alibaba Group has recently made headlines for developing a new artificial intelligence system that it claims can answer 95% of all calls placed to Alibaba’s online chat service within five seconds or less. (Alibaba claims its human assistants answer calls within 30 seconds.) The technology even understands and remembers context from multiple interactions so it can provide more personalized responses over time. Other examples from Vodafone, etc. are also worth checking.
Fraud Prevention and Cybersecurity
Fraud prevention and cybersecurity are top challenges for telecoms today. Luckily, artificial intelligence offers a variety of solutions to help detect fraud across your business. For example, neural networks can be used to analyze changes to mobile user behavior (such as cell phone activity) that might suggest fraudulent activity. These models can also help identify anomalies within call logs and SMS data, helping you determine whether there’s been any fraudulent billing activity. AI can also be leveraged for cybersecurity purposes, such as detecting malicious bots before they spread throughout your network.
Quality of Experience Monitoring and Optimization
In today’s hypercompetitive marketplace, user experience has become one of those critical metrics that can make or break your product. Quality of Experience (QoE) is closely tied to customer satisfaction, so it is a valuable metric for both business and technical leaders.
More specifically, QoE is about maintaining an acceptable level of performance across multiple dimensions throughout an entire customer experience. QoE is often broken down into three dimensions: latency, throughput, and jitter. This is where ML-based predictive analytics come into play. Using advanced algorithms, AI system can determine and predict how users respond to and experience the different aspects of networking mentioned above, without having to explicitly frame these questions. The underlying deep learning model analyzes the multi-dimensional network and customer data to come up with QoE scores.
This allows the telecom companies, in particular communication service providers, to identify problem areas and quickly address them before they turn into complaints. It also allows them to optimize current networks as well as plan future infrastructure investments based on actual user experiences rather than assumptions.
Tracking network Assets
Tracking all your network assets is essential for companies in the telecom sector. You can use artificial intelligence to help you automatically track your network equipment, providing you with a view into your entire hardware and software environment. This can make scaling easier, as you’ll have a more accurate view of your overall software and hardware footprint. And if there’s an outage, you can better diagnose it because of all that data.
To get started using artificial intelligence to track your company’s networks, try a product like Splunk or Kinvey. These products can identify and collect information from a variety of sources within a short period of time. As they scale up, they become smarter with each new piece of data they collect—in other words, they learn and improve with time.
Personalized offerings and recommendations systems (content, equipment, capacity, etc.)
One of the top and most common use cases for AI in telecom is a recommendation system. Recommendation systems are all about understanding individual customer needs and providing personalized offerings and recommendations to customers based on their history, preferences, and what’s best for them (vs. a generic or one size fits all offering).
To stay competitive, telecoms have to deliver offers and promotions that appeal to each customer’s unique needs, enhancing the customer experience. These personal recommendations are based on a customer’s behavioral historical data—for example, how they use their network services and what they search for online. This approach also helps ensure that customers receive ads that are specific to their interests instead of irrelevant pitches based on limited demographic data. While traditionally utilized in B2C segments, these methods have very much become part of B2B businesses as well.
Automated digital advertising campaigns
There’s an incredible amount of competition and time pressure on digital advertising campaigns. An artificial intelligence application can make these campaigns significantly more efficient by automating a variety of processes and improving lead generation capabilities. For example, companies that use AI can automate processes such as data storage and processing, matching leads to targeted ads, and responding to consumer queries. These processes need constant human monitoring; fortunately, humans won’t be needed when tasks are handled by AIs. This will save a considerable amount of resources for businesses that rely heavily on digital marketing campaigns to acquire new clients or retain existing ones.
Customer Insights
AI in telecommunications is widely used to improve customer service processes. For example, chatbots are a smart way for telecom companies to minimize expenses by outsourcing support jobs overseas. Chatbots also allow companies to gather data on their customers so they can target them with more personalized messages and campaigns later. AI’s applications go far beyond chatbots, though. By using AI-based customer segmentation, for instance, a company can learn which users might be interested in a new product. Then it can use that information to target those users with advertisements (or messaging). The goal of these targeted ads is to entice potential customers into purchasing products from those businesses.
Personalized Customer Service
Though it may not seem like a life-changing application of AI, personalizing customer service and implement self-service capabilities is an important component of AI use cases in telecom. Currently, many companies are incorporating a level of human touch into their customer service process by offering 24/7 services to customers and employing people who specialize in communicating with customers. However, as artificial intelligence technology continues to advance and become more integrated into everyday life, your company may be able to outsource much of its customer service needs—and maybe even eliminate some jobs—by using AI-driven bots that can communicate at scale.
For example, USAA created an intelligent assistant called Isabel designed to communicate with customers across multiple channels. She is capable of answering questions about insurance policies, transactions, claims, retirement accounts, and other areas. She’s also capable of interacting with customers on social media sites such as Facebook Messenger or Twitter Direct Messages (DMs). The most impressive part? She’s already assisting over 1 million USAA members! If you’re looking for AI use cases in telecom that will improve your customer experience while simultaneously reducing costs, look no further than personalized customer service.
Sales Lead Generation
One way AI is helping sales teams is by automatically sorting through contacts from a company database and prioritizing those leads based on various factors, such as what telecommunication industry segment they work in or their personal relationship with the seller. In fact, using AI for sales lead generation can save some companies up to four hours a day of manually sifting through data. When that happens, it opens up more time for sellers to talk with prospects—which helps them convert sales faster. Plus, AI-based systems are more accurate than humans at spotting trends that might be useful when generating new leads.
For example, if AI notices that someone you recently contacted has also been in touch with several other buyers at your company recently, it could suggest you contact him again soon so he doesn’t slip away to another vendor.
Where to focus, and how to get started?
Finding use cases to apply AI in telecom sector is relatively easy, but finding use cases that create value is another story. An important factor to consider when selecting an AI use case is how people work today. If you can build a workflow that enables customers, internal or external, to do something faster and easier (e.g. network automation), then chances are good that your business users will find it valuable.
First focus on your business processes, goals and pain points. Does your company need more sales leads? A better way to predict churn? A more efficient means to collect data? Depending on your situation, there are numerous ways that artificial intelligence can be implemented. From there, do some research on platforms that could fit your use case, or consider building a custom solution using APIs or open-source tools.
Whichever approach you take, it’s important to remember that AI is only as good as the business value it aims to generate. If you’re having trouble deciding where to start, don’t be afraid to call us up, and we’ll be happy to support you in this journey.
