Natural Language Processing (NLP) in the Telecommunications Industry

The telecommunications industry is rapidly evolving. Operators are looking for ways to increase revenue and profits, while at the same time reducing costs. In this competitive environment, natural language processing (NLP) can be a valuable tool. NLP can help telecom service providers improve customer service, make better use of data, and reduce expenses. In this blog post, we will discuss the benefits of using NLP in the telecommunications industry and how to approach AI implementations.

Natural Language Processing – What Is It?

Natural language processing is a branch of Artificial Intelligence that allows computers to deal with textual data. Code is a natural language for computers, so naturally, they cannot understand Chinese or Polish unless they’re broken down with the appropriate techniques. Otherwise, it’s all Dutch to them!

NLP developers enable the machines to decipher the structure of language by creating models which can separate and comprehend details from natural language, both in the form of text and speech. Acting as a bridge, NLP enables the user to be understood by the computer without changing their speaking habits and figuring out how the machine “thinks.”

How does it work in practice? In fact, the learning process in NLP quite resembles the way we humans learn a new language. However, the machines may need a little help from the developer’s side. The teaching process divides into morphological, syntactic, semantic, discourse, and pragmatic analysis and involves the following steps:

• segmentation (breaking the textual or recorded data into constituent sentences and explaining them individually to our algorithm)
• tokenizing  (breaking down the sentence into constituent words)
• removing stop words that do not carry a particular meaning, like prepositions or verb forms
• stemming (explaining to the machine the roots of the word and prefixes and suffixes)
• lemmatization (identifying the base words for different word tenses, mood, gender, etc.)
• part-of-speech tagging (explaining the concept of nouns, verbs, prepositions by adding them as tags to the words in our form)

Existing Models for NLP Tasks

NLP is everywhere – take the autocorrect or online translators for different languages used by most daily. But today, we’d like to focus on the business applications of natural language processing and, more specifically, on how the telecom industry uses it to optimize its processes.

Tasks like voice recognition, text classification, and entity extraction are commonly performed by NLP models in the telecom sector.

Meanwhile, if you’d like to dive deep into the topic of NLP in a broader context, here’s our take on this fascinating technology.

Telecom Industry-Specific Language

NLP in the telecom industry refers to the ability of machines to interact with humans using natural language that is very specific to this domain. In the telecommunications industry, there are a lot of jargon and abbreviations. 4G, 5G, UE(User Equipment), eNB, and BTS(Base Station) are just some examples. This can be confusing for customers and laymen, and it can also lead to miscommunication between humans and machines if the NLP model was trained on generic language datasets. NLP models need to be fine-tuned to telecoms-specific language in order to accurately understand particular needs.

The approach used within this industry goes even one level deeper. NLP algorithms are trained on large telecom-specific datasets which cover very specific parts and use cases of telecom-related terms and phrases. These datasets help machine learning algorithms learn the specific vocabulary and jargon used within narrow subsectors of the telecom industry. Thanks to that, AI applications trained on domain-specific data sets gain higher quality and overall better performance.

NLP in Telco - Case Studies for Improved Customer Experience

The telecommunication sector has embraced this branch of AI in its many forms (read more here), forging the path for NLP-based tools that were later swallowed by other sectors. Just like the banks and insurance companies, the telecom enterprises do not have many opportunities to stand out in the market with their offer due to the specifics of the industry. As a result, they have to search for other ways to win the battle for customers.

Polishing the customer experience is their most common way to conquer the clients. And since the nature of telecom services creates demand for loads of professional support and interactions between the customer and the company, it becomes essential to optimize this area. NLP brings it to a whole new level, allowing telecom businesses to solve the typical customer problems in an efficient manner and wherever possible without human intervention.

Before we move on to the NLP use cases, maybe you would like to read more about AI in telecom? If so, we recommend checking our blog series on this topic.

In the meantime, let’s look into NLP applications for customer service.

Virtual Assistants

There are cases in which the support of a real-life consultant is worth its weight in gold. However, for the most part, virtual assistants can handle customer problems impressively well (and fast!). Many companies have appreciated their potential, going all in, like Walmart’s Sam’s Club, where 90% of the questions are answered by an automated bot.

Every extra minute of waiting makes the CSI drop sharply, and with virtual assistants powered by natural language processing, the companies can finally meet the expectations of their clients in that field. Bots can solve their issues fast and 24/7, cutting the customer service costs.

Whether they’re chatbots or voice bots, the rule is the same – they identify the relevant information from the written or spoken inquiries and enter in dialogue with them. In order to be able to solve more complex customer problems and learn with every interaction, it’s worth using deep learning techniques. Neural networks enable the bots to mimic human behavior, making the whole experience feel more natural for the user.

Good training is crucial to avoid the “dumb bot” issue that frustrates the customers so much and stops the companies from reducing costs – because the bot remains just an additional stopover instead of actually solving problems. If the NLP is implemented thoughtfully with the support of machine learning models, the bot will be able to identify intent almost like a human, taking over a great deal of the consultant’s work.

Categorizing Support Requests

Big telecom companies deal with hundreds of thousands of support requests every day. With an omnichannel approach, their processing becomes quite challenging. Every request needs to be assigned to a specialist that can solve a particular problem.

With NLP, this process becomes much simpler and faster. The support agents do not have to look through every request manually to be able to direct it to an authorized person - the algorithm auto-tags and categorizes it (so-called taxonomy). Since the model is trained to recognize the root of the issue by associating particular words with particular problems, it can assign the support request automatically (auto-routing).

Telecom companies can also use NLP for prioritizing requests. This feature helps to organize the tasks in a customer-friendly and efficient way while facilitating the optimization of the processes simultaneously. Having the categorized requests at their disposal, the analysts can find the most frequent issues that sabotage a company’s business growth and affect the customer experience in a negative way. The conclusions can later serve for prioritizing innovations.

Resolving Support Tickets

With NLP, the companies can not only categorize but also resolve tickets. They save on customer service this way, but more importantly, they provide the professionals with time and space to focus on more complicated issues.

After finding the relevant information in the support request, the trained algorithm can categorize it and decide whether it should be assigned to a particular specialist right away or rather solved in an automated manner. For instance, it could send ready-made responses after identifying the root of an issue or redirect the user to already existing resources on the company’s website.

The support agents can also take advantage of NLP through template response suggestions. In this case, the model suggests the answer by drawing from an extensive library of information from chats and emails within the identified category. It’s an excellent way for the enterprise to improve response time.

AI in Call Centers

Even if the customers think otherwise, their problems are predictable and repeatable to a certain extent. That creates cost-cutting opportunities for the telecom companies when managing their omnichannel tickets but also allows them to revolutionize their call centers.

With NLP, and more specifically, voice recognition and voice assistance techniques, a great deal of work that used to be carried out by the call center agent can be automatized. As customers, we’re already used to the systems where we interact with the recordings to get redirected to an appropriate department and reach the agent that will solve our issue. Based on traditional rule-based programming, they help to assign and prioritize the calls but do not solve problems. Machine learning combined the NLP can make these interactions much more sophisticated and less limiting for the client.

So far, just a tiny fraction of the telecom companies with their own call centers can boast of the system that brings this experience close to a real-life conversation. The voice assistants often respond in a non-relevant way. That’s why improving the model’s ability to detect intent is a priority for telecom companies at this point.

If you’re struggling with similar issues, don’t hesitate to contact us. We can help you elevate the response accuracy and solve other issues like the poor understanding of different accents.

NLP Sentiment Analysis

Let’s move to another great feature NLP provides companies with. In a nutshell, sentiment analysis is an automated recognition of positive and negative feedback received from B2B or B2C customers.

While in the case of categorization, the model is taught to associate particular words with the root of the issue the customer comes up with. Here, it links the expressions with positive or negative sentiments, marking the feedback accordingly. Sentiment analysis is a helpful tool for the Customer Satisfaction Specialists or Loyalty Managers to find areas for improvement in customer service/customer support departments.

Competitor Analysis

Aside from sentiment analysis, the NLP offers another analytics tool that can support a company’s growth - not only in the telecom sector. To evaluate the risk of introducing a new service/telecom product to the market, the companies need to know everything about their competition. The AI model can scan the content of the mails or voice recordings and look for the competitor names, tagging context and categorizing the mentions of competitors.

Having this data extracted, the companies can identify good ad bad practices, optimizing their sales and marketing strategies. AI looks for competitor names mentioned in emails or calls, tags context, and categorizes mentions. Aside from that, the analysts also use NLP to streamline the preparation of competitor analysis and validate it. The model can look through vectorized descriptions of companies extracted from freely available resources (like Clutch.co or Crunchbase) and point out the most relevant competitors with the support of NLP.

Case Studies for Streamlined Network Operations for Telecom Service Providers

Just as it does with customer service, the NLP can also serve for streamlining network operations. No wonder the telecommunications sector has embraced it to that extent! Here are some examples of how helpful natural language processing can be in other areas than customer support.

Automated Infrastructure Documentation Generation

Maintaining a clean and organized infrastructure documentation and keeping it updated is essential for telecom operators. In the case of any network failure, it is necessary to look through it in search of an issue. If the language is too complicated or the use of terminology is not consistent, finding relevant information is extremely challenging, particularly in the case of extensive networks.

With NLP, companies can partially automate the generation of the critical infrastructure documentation, standardizing the documents, creating templates, and automatic suggestions that help keep the writing as clear as possible. The NLP engine can verify the usage of terminology and suggest expressions. As a result, maintaining clean and updated documentation becomes much easier, allowing the network support to fix the recurring issues faster and streamlining information flow as well as onboarding and network optimization.

Data Mining for Telecommunications Network Log Analysis

With the environments becoming increasingly distributed, managing logs and resource utilization becomes more challenging than ever. And as with the business growth, the log volume has to increase, making its management as efficient as possible becomes a priority. Troubleshooting the logs is a time-consuming and labor-intensive process, and that, of course, means costs.

NLP can streamline that process, making it much easier and faster to detect issues in real-time and solve them. Since the logs usually contain written text, the developers can use NLP to classify and group them. Such structuring facilitates extracting relevant information concerning the issues.

Using Natural Language to Configure a Network Management System (NMS)

Using NLP, telecom companies can empower their non-technical employees while avoiding costly training at the same time. With the support of the machine trained to interpret the natural language requests, they can configure a network management system without technical skills, just via speech.

The exact course of that process may depend on the developers’ approach, but without going into detail – the NLP engine extracts the information from the command and converts it into a structured network request (SNR) that later gets translated and validated by the deep learning network. The process ends up with the translated SNR being processed by the network management system via API. Result? Fast and human-friendly configuration, less frustration and errors.

Benefits of Applying Natural Language Processing Solutions in the Telecom Industry

The examples above prove that natural language processing creates immense possibilities for telecom companies, helping them streamline customer service as well as network operations.

As a result of implementing natural language processing, the telecom enterprises:

• improve the customer experience and CSI - the customers get their problems solved faster and at any time;
• cut costs on customer service/support - a part of the responsibilities is taken over by the AI, reducing the workforce demand and offloading the consultants
• find the areas for improvement easier - sentiment analysis and competitor analysis with NLP provides them with a holistic overview
• simplify and partially automate a complex ticketing system - the employees can focus on more complex issues while the AI takes care of the typical ones

The Future of Machine Learning and NLP in the Telecommunication Industry

As for today, the developers designing solutions for the telecommunication industry focus on improving the machine’s comprehending capabilities, trying to get as close to the human way of processing language as possible. That requires some advance in detecting sentiments or humor and solving accent-related issues.

The increasing popularity of virtual assistants based on emerging technologies such as Alexa, Siri, or Google Assistant proves how far we’ve got in the development of natural language processing. NLP-based solutions are becoming an essential part of our daily life, and the telecom companies will be embracing that – also for their own good!

Our teams at nexocode apply natural language processing techniques in various projects. If you’re curious about these, check our NLP development services and solutions page and our case studies documenting NLP and robotic process automation implementations for a social platform for idea-exchange, an AI-based mental health support app, or a corporate booking platform.

And if you’d like to check the potential of NLP for your own project, drop us a line!

Join our AI Design Sprint workshops focused on NLP to design your next AI-based solution.