A 2018 survey reported by Harvard Business Review indicates that 73% of executives in large corporations believe to have received measurable value from AI and data science initiatives.

ai in telecom business

We’re not far from the time where data science isn’t just a way of gaining competitive advantage. It will be a must-have.

Being applied across a wide array of industries, artificial intelligence has also made its way to the telecom business. Innovative telecom operators use AI and machine learning to increase network reliability, improve customer satisfaction & retention, optimize their business processes for higher profit, and much more.

We’re not far from the time where data science isn’t just a way of gaining competitive advantage. Soon, it’s a must-have for any telecom company looking to thrive in the next 20 years.

Currently, we’re seeing telecoms benefitting from data science in three main areas:

AI in telecom customer service and retention

The most visible use case for AI in telecoms is enhanced customer service. Leading telecom companies in the U.S. such as AT&T, Comcast, and Verizon leverage artificial intelligence in a wide array of processes. The long list includes automated chatbots, personalized offers, and efficiently streamlined customer service processes.

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

  • Customer service communication
  • Customer engagement and personalized user experience

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

AI-powered customer service communication

To solve customer’s problems at a scale unfathomable for human agents, the AI algorithms empowering customer communication must process a massive amount of data and interactions. In the telecom business, there’s a lot of data with different nature that can be used to train such algorithms.

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

Here are some examples how AI algorithms are benefitting large US telcos in the area of customer service communication:

  • Acting as a gateway between customer requests and help centre/live chat.
  • Routing customers/customer requests to the proper agent, and routing prospects with buying intent directly to the sales department.
  • Analyzing customer requests together with network data to find the solution to customer’s problem 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.
  • Entertainment chatbots operating on telecom operators’ native platforms or through the Facebook Messenger platform.

AI as a customer service agent

Telecoms often apply machine learning algorithms 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 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.

ai in telecoms

There are several more good examples of AI in customer service in the telecom business.

In 2016, Centrylink implemented their AI-driven assistant named Angie. According to the Harvard Business Review, Angie handles an estimated 30,000 emails each month and analyzes the responses to identify “hot leads” that are then routed to a relevant sales department. The initial pilot showed that Angie could correctly interpret 99% of emails that were processed while 1% were forwarded to human agents. As a result, the company’s sales representatives can save a significant amount of time spent on outreach and follow-ups.

machine learning in telecomsAT&T,  the world’s largest telecommunications company, 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.

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 human conversation 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 their chatbot.

At MindTitan, we also see a potential for utilizing the location layer and network analytics to improve customer service. For example, if a customer from a specific location reaches out with a problem, the algorithms can check network analytics to identify potential issues or shortages in the area. As a result, the customer service rep helping to troubleshoot will have additional knowledge of what the correct solution might be.

Sales and personalized 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 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 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.

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 large quantities of metadata and using machine vision (image recognition) to recommend customers new relevant content.

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 or another kind of problem.

Another popular use for AI in telecom business is matching customers with best-suiting data packages.

Self-learning algorithms accumulate insight of which packages match different customer types, easing the burden on call operators and making sales process far more efficient.

From the customer’s perspective, having an AI-driven agent involved in the process could mean 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.

AI in telecom network analysis & predictive maintenance

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

With the growth of 4G networks and 5G being around the corner, 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.

 

machine learning in telecom business

With the growth of 4G networks and 5G being around the corner, we are leading towards an ever-growing data consumption.

Network maintenance is often considered to be the second generation of AI-powered solutions, focusing on 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 hardware. 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.

Today, algorithms can monitor millions of signals and data points within a network to detect impending problems real-time as they occur. Based on this data, the company can react by load balancing, restart the software involved or send 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 predicts that predictive network maintenance 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:

  • AI-powered system can restart cell 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 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 better maintaining the network. With large and spread-out infrastructures, telecom companies are prone to benefit from scaleable AI-powered solutions.

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. Are you sure that AI is the optimal solution to these problems?
  3. Do you have the required data for the algorithms to learn from or do you need to first set up a data infrastructure?

If you’re interested in additional insights on AI in the telecom business, don’t hesitate to reach out to us at team@mindtitan.com.