AI Use Cases in Telecom Relevant for 2022 with 8 examples

Konstantin Sadekov
December 12th, 2021

telecommunications antenna

What are the AI use cases in telecom companies?

According to Harvard Business Review, the pandemic has accelerated the adoption of data analytics and artificial intelligence among companies. About 74% of executives believe that AI is going to make businesses more efficient moving forward. We’re not far from the time where data science and data collection aren’t just a way of gaining a competitive advantage. They will be a must-have.

Being applied across many industries, telecommunications companies now also implement AI projects or solutions in their business models.

Innovative telecom providers use AI and machine learning techniques to optimize network performance, improve customer satisfaction and retention, streamline their business processes for higher profit, and much more.

Data science isn’t just a way of gaining market intelligence. Soon, it’s a necessity for any company in the telecom sector looking to thrive in the next 20 years.

Currently, we’re seeing leading telecommunications companies benefitting from data science in three main areas:

At the end of the day, the main question is: is your company ready for AI? But first, let’s explore how AI adoption can optimize the telecom industry.

Using machine learning to improve customer satisfaction

The most visible AI use case in the telecommunications industry is enhanced customer service. Leading telecom companies in the U.S. such as AT&T, Comcast, and Verizon are implementing AI in a wide array of key processes. The long list includes automated chatbots, personalized offers, and streamlined customer service processes.

With some exceptions, AI-powered customer service solutions can be divided into three categories:

  1. Customer service communication
  2. Sales and personalized user experience
  3. Call center automation

Solving (or improving, at least) each of these problems presents potential savings and increased efficiency for companies.

AI-powered customer service communication 

To solve customers’ problems at a scale unfathomable for human agents, the AI algorithms empowering customer communication must process vast amounts of historical data and real-time interactions. In the telecom sector, big data with different variables plays a key role in training these algorithms through machine learning.

AI-powered customer service solutions are often represented by virtual assistants or a chatbot interface. But that is not always the case. Sometimes, these algorithms also work in the background, helping to make customer service departments’ work more cost-efficient. For example, analysing extensive background data to help a customer service agent to identify the root cause of a customer’s problem and find the appropriate solution more quickly.

Here are some examples of how AI algorithms are benefitting large U.S. telecom organizations in the area of customer service communication:

  • Acting as virtual assistants or gateways between customer requests and contact center/live chat
  • Routing customer requests to the proper agent, and routing prospects with buying intent directly to the sales department.
  • Analyzing customer support requests together with network data to find the solution to customer’s problems more efficiently
  • Identifying “hot leads” from thousands of emails and routing them to the salespeople
  • Letting customers explore or purchase media content by spoken word rather than remote control
  • Having entertainment chatbots on telecom operators’ native platforms or through the Facebook Messenger platform

This became possible because of the natural language processing technology that helps the AI to understand written text. NLP use cases help to understand their benefits and how exactly they are used in your industry.

AI as a customer service agent

Telecoms often apply machine learning algorithms derived through big data to make the customer service process more cost-efficient. This kind of AI use case is present in AT&T, Spectrum, CenturyLink, and many other well-known telcos.

The AI-powered Ask Spectrum virtual assistant helps customers with troubleshooting, account information, or general questions about Spectrum services. The customer inquiries managed by the assistant range from identifying service outages to ordering paid content services. The assistant can either provide users with helpful tips and links to the Help Centre or in the case of more complex requests, refer them to Live Chat representatives. As a result, some of the work is loaded off the CS team’s shoulders and they’re left to deal with more demanding cases.

Below are some more examples of successful AI adoption in the area of telecom customer service.

case study example from elisa

A chatbot case study from Elisa demonstrated a chatbot’s ability to fully automate 45% of the inbound contacts, with 42% FCR level. The users are very happy with the solution as the transactional NPS now increased from 30 to 50, which is above the average level of human customer service.

This helps product owners ensure that the information really gets to the clients and reaches the sales goals (as some of the automated customer conversations are about purchases). The number of unnecessary contacts in the future is also reduced by efficiently updating the manuals, because now the product owners actually understand what end users are asking.

tobi chatbot

AT&T, the world’s largest telco, leverages AI to process all “online chat interactions.” In December 2016, AT&T rolled out Atticus, the entertainment chatbot that communicated with users via the Facebook Messenger platform.t

In April 2017, Vodafone released its new chatbot TOBi that can assist customers via live chat on the Vodafone UK website. Using a combination of AI and predefined rules, TOBi simulates humanlike, one on one conversations and responds to customer inquiries ranging from troubleshooting, order tracking, and usage.

Recently, TOBi also acquired the capacity to assist users with the purchase of SIM-only plans. The company is constantly looking for new add-ons to its chatbot that can deliver more value to customers.

 

According to a 2021 report released by research firm GlobalData and AT&T, the evolution of AI chatbots especially during the pandemic has resulted in a more emphatic customer experience, where engagement and sentiments are accurately tracked for data analysis. Furthermore, as the technology progresses, chatbots are increasingly becoming skilled in handling more complex tasks such as data recording, receiving reports, and handling bookings. It won’t be long before there’s a universal adoption of chatbots in all major telco players.

Sales and personalised user experience

In addition to customer service chatbots and inquiry routing systems, AI can help telecoms improve customer retention and receive higher profit per user. Potential AI use cases for machine learning algorithms in this area include:

  • Making personalized recommendations based on a user’s behavioral patterns and content preferences
  • Making relevant upsell and cross-sell offers to the right users at the right time
  • Assessing which call & data package best suits different types of users, increasing the sales success rate
  • Detecting and fixing potential issues for customers even before they’re apparent to the end-user

Analyzing social media, brand coverage, and customer sentiment to learn what drives customers to the service provider and what drives them to leave is important. Comcast, the largest broadcasting and cable television company in the world by revenue, has launched a voice remote that enables users to interact with their Comcast system through natural speech. The telecom company is also using AI to process massive amounts of metadata and using computer vision machine learning (specifically image recognition) to recommend new relevant content.

Accurate product recommendations

In more technical language, many recommender engines are based on NBO (next best offers) optimization and NBA (next best actions) optimization. The NBA methodology can also be applied to debugging some customer issues. Algorithms can recommend the best potential solutions to a connectivity-related problem and other similar concerns.

Another popular AI use case in the telecom industry is matching customers with best-suiting data packages. Self-learning algorithms accumulate insight into which packages match different customer types, easing the burden on call operators and making the sales process far more efficient.

From the customer’s perspective, having an AI-driven agent involved in the process could mean a significantly better service experience. Instead of waiting for 20 minutes to talk to the customer service rep, a customer’s problem could be solved by an algorithm within seconds, depending on the nature and complexity of the issue. This will lead to higher satisfaction and, eventually, to higher retention.

Call Center Automation

Streamlining the different systems in a call center is such an important part of lowering operational costs. This includes automating repetitive tasks and eliminating manual work, which can be prone to human error. Most companies still use manual methods in key processes such as ticketing, and data entry.

Using AI solutions can eliminate pain points such as high call abandonment rate and bad customer experience which can have disastrous reputational costs as well. Below are just some examples of new technologies or processes that companies can explore to automate their call centers.

  • Self-serve, 24/7 systems
  • Conversational IVR that understands customers by just listening to the voice during the phone call
  • Agent assist tool that guides an agent during the phone calls and suggests what questions to ask and answers to give

AI in telecom network analysis & predictive maintenance

As mentioned at the beginning of this case study, telecoms use AI in two key areas: customer service and network maintenance.

With the continued rollout of 5G around the world, we are leading towards an ever-growing data consumption. Optimizing the networks to withstand this kind of heightened data usage is becoming one of the key strategic decisions in the telecom business. This includes improving network quality and Internet connectivity.

Network maintenance is often considered to be the second generation of AI-powered solutions, focusing on a software-centric approach toward self-healing, self-optimizing, and self-learning networks.

A few years ago, network providers used to send field workers to sites to periodically check up on network equipment such as hardware and even cell sites. This resulted in frequent delays and errors, having a negative impact on customers’ experience. While this method is still relevant and widely used today, many urgent and unplanned check-ups could be avoided thanks to data science.

Big data and network optimization

Today, algorithms can monitor millions of signals and data points within a network to conduct root cause analysis and detect impending problems in real-time as they occur. Based on this data, the company can react by load balancing, restarting the software involved, or sending a human agent to fix the issue and thereby avoid many outages before they’re noticed by customers.

Mazin Gilbert, VP of Advanced Technology at AT&T Labs thinks that predictive network maintenance and network optimization will continue to drive favorable expense trends over the next several years.

“We are implementing AI to help us to identify where these breakpoints are, and help to repair those in an automated way without human intervention. This goes for hardware failure, software failures.”

Mazin Gilbert, VP of Advanced Technology at AT&T Labs

Recently, AT&T announced the testing of a drone to expand LTE network coverage in the form of a Flying COW (Cell on Wings). The company is exploring ways to incorporate AI and machine learning for the analysis of video data captured by drones for tech support and infrastructure maintenance of cell towers.

Verizon offers similar services called “condition-based maintenance” to other carriers. Here’s a great explanatory video on predictive maintenance for heavy industry.

AI-enabled networks are capable of self-analysis and self-optimization, resulting in greater agility and precision.

Here are some examples of AI-powered network analytics in action:

  • The AI-powered system can restart cell sites or towers based on their behavior, e.g. if they’re not connecting to the network
  • Algorithms can point out parts of the network which need investments and would produce the highest ROI
  • Similarly, network operators can use AI to identify parts of networks with a large number of users who would benefit from network improvements, leading to higher profit
  • Optimize the behavior of the network based on weather data, daily movements and real-time usage data
  • Enhance network utilization and customer satisfaction through dynamic resource allocation

Is your telecom business ready for AI?

When working with telcos, we usually see a lot of low-hanging fruits for streamlining customer service and enhancing capacity planning and network optimization. With large and spread-out infrastructures, telecom companies are prone to benefit from scalable machine learning or AI-powered solutions, while transitioning legacy systems to more modern infrastructures.

While AI can help optimize a company’s operations, it’s not always an easy solution to implement. It takes a lot of analysis and management support to ensure that an AI project will succeed. You would need to study your existing data infrastructures and stay informed on telecom AI trends to see if they fit your business objectives.

Key questions to ask

Before you take the first step to bring artificial intelligence into your company, we recommend that you consider the following questions:

  1. What are the key areas where you’d like to see improvement? Is it your customer service, sales, network operations department?
  2. What are the pain points or unrelenting pressure in these areas of growth? What are the future results you want to see?
  3. Are you sure that AI is the optimal solution to these problems?
  4. Do you have the required data for the algorithms to learn from or do you need to first set up a data infrastructure?
  5. Do you have data-driven teams such as data scientists, engineers, etc?

ai use cases formulation

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