How AI is Transforming the Telecommunications Industry

Harry Liimal | 20. February 2019

From customer service to delivery, from personalisation to product quality, from complaint handling to social responsibility, customers are expecting a first-class experience from service providers, wherever and whenever they interact with the business. If customers receive anything less, they will seek a competitor that meets their expectations.

Through automation and artificial intelligence, telecoms increase their service quality, while reducing their operating costs.

Telecom operators are in a good position to develop AI through three key advantages: big data resource, exploiting breakthroughs in AI field and a broad customer base.

The best service is no service

Currently, one of the main AI driver in Telecom companies is delivering better customer experience and followed by reducing OpEx by driving automation.

The reality is that most of the time with customers is spent on problems, but to be successful, companies need to treat service as a data point of dysfunction and figure what they need to do to eliminate the demand. This means that ideally, everything should work perfectly and problems would be eliminated before they arise. It can be a problem with billing or network, purchasing a new product or connecting with the right representative. This could all be prevented by implementing various AI models.    

But improving customer experience and speed of service can be only the beginning. Telecoms next big step is an AI-defined network, where the goals and limits are set by providing customers the experience they actually need, the network control software would structure the network based on existing conditions. Using AI in such content allows to reduce human intervention to configure, provision and maintain networks and help telecoms to reduce operating costs while allowing to onboard customers faster while making bringing new products and services to market in shorter time.

How telecoms can benefit from AI

We have mentioned already different solutions how AI could impact telecoms industry but let’s have a closer look at how MindTitan has helped telcos overcome their different challenges: 

Optimize customer service

  • Chatbot telecoms – Chatbots enable telecommunications companies to solve customer problems quickly by integrating with the website and receiving several relevant data inputs about the customer. Read more…
  • EmailBot – makes email support easier. Having a bot reply with answers or follow up questions. Routing other emails to the right people the first time. Read more…
  • CallBot – AI bot will ask customers for their problems, transcribes calls in real time, then routes the call appropriately. Read more…
  • Call Analyzer – helps to gauge agent performance and assist with coaching, while measuring customer sentiment. Read more…
  • Contact prevention – helps to predict when & why customers might contact you. Automation tools help to encourage self-service or provide immediate solutions. Read more…
  • Omnichannel Analysis – helps to analyze communication and build models across every channel to communicate with customers. Read more…

Grow sales

  • Intelligent sales – helps to provide salespeople with optimal upsells & cross-sells. Read more…
  • Customer lifetime value – helps to model B2B & B2C customers to understand spending and growth patterns. Read more…
  • Customer rescue – helps to get customers back with beneficial lifetime value by using client-specific price points with an extended sales NBO model. Read more…

Network technologies

  • Customer experience index – helps to understand how customers experience your wireless network. Whether or not they tell you. Read more…
  • Network fault prediction – helps to predict network errors and proactively take corrective action. Read more…
  • Fault Grouping – helps to reduce alert noise. AI model will understand vast alert & alarm systems to provide more meaningful alerts. Read more…

Business operations

  • Revenue assurance – helps to guard your business against systemic failures where hard-coded and hand-set rules do not help. Read more…
  • Infrastructure planner – helps to find the optimal balance between improved customer experience and return on investment when building or improving infrastructure. Read more…

So how do telecoms start with an AI strategy?

Telecoms are one of the biggest industries who collect data and have the possibility to take advantage of big data. However, there is a common question about where to start with artificial intelligence. As the data is there then this question is usually followed where and how to concentrate their efforts. Should they optimize customer service, optimize revenue, manage risk or use machine learning to come up with better products?

The answer to these questions is that almost all aspect of Telecoms business can be positively impacted by AI. But that’s not a feasible approach. MindTitan has a clearly defined process which we use with Telecoms to understand the business objectives, audit existing and future processes from two perspectives, and create an AI roadmap to truly gain competitive advantages across the business.

Start with business objectives

As a telecom executive management team makes strategic decisions, these decisions roll down to business unit leaders and become time-restricted objectives and goals. 

To illustrate, a strategic decision could be to expand market share in a particular region this year, which become objectives for cross-functional business units. Sales and marketing have an objective to increase demand and convert that demand into revenue. Customer service must retain customers, as not to lose revenue, and field inbound inquiries to sales. Risk management comes into play as well. And the telecom needs to understand if the products they offer meet the market need, or if core products must be updated, or new products and services should be created. 

Clearly, each business unit has its own interests, but the objectives are clear and aligned to increasing market share. This leads to the next logical question: How do we accomplish our objectives and how do we measure success?

Audit existing & future processes

Sure, it’s exciting to think about jumping into the data and getting started with an artificial intelligence plan, but there’s a crucial step telecom must take before even getting to the data: Auditing existing and future processes. 

Why? Well, no amount of artificial intelligence and machine learning models will fix broken businesses processes. Granted, working with an AI expert, processes will be re-thought, making them scalable, more efficient, and more effective. 

During this audit, it is utterly important that the processes are looked at from two perspectives. First, the processes must be looked at from someone that understands the process, typically someone from the business (also known as a “problem owner.”) Second, someone with artificial intelligence expertise must look at the process, as this person provides a fresh, data-led perspective of the process. This person is typically a data scientist, who understands if there is even a use case with business impact, or if the data to achieve the objective exists throughout interconnected processes within and between business units. 

Also, the AI expert can help understand the most optimal starting point. For instance, one area telecoms can start with machine learning is finding ways to reduce friction in the customer journey to increase revenue, as to not lose opportunities to competitors. Telecoms have a plethora of customer behaviour data, as well as detailed information about customer personas and product information. Having a human analyze this data and make decisions is possible, but takes a very long time – the customer has already moved on. Using deep neural networks, an AI model can understand the several paths a customer could take to make a decision, and which path is most probably based on customer personas, micro-segmentation, and behavior. The AI model will automate processes to “nudge” the customer in the right direction, like pushing educational information to the customer.

Create an AI roadmap

One AI model can accomplish quite a lot for a particular business unit within a telecom, but several machine learning models, working together can do wonders, which is why a roadmap for an artificial intelligence initiative is important. 

Simply put, the output of one artificial intelligence model can become an input for another model, making them more powerful together – think 1+1 = 3. 

For example, the AI sales flow assistant model described in the previous section becomes a lot more powerful when coupled with AI-powered propensity modeling. Propensity modeling helps the AI model that assist the product managers in understanding the fit between the services the telecom offers and the market. 

And so on. 

Granted, budgets and ROI are important, which is why picking an appropriate starting point are necessary, as mentioned above. Think big, but start small by working with the AI expert to deploy a model with a good return on investment, but doesn’t take much too long to get results, doesn’t have an astronomical price tag, or has an unnecessary strain on company resources overall. 

Lastly, the roadmap creates a clear plan for the next machine learning models to deploy and work on, while the other ones are learning and improving. 

So, start with what you know – company objectives and current & future processes. 

Then enlist an artificial intelligence expert to help with the telecoms AI & machine learning journey.

 

 

Harry Liimal

Business Development Associate

Harry has years of experience working in business development, strategy and digitalization for some of the world’s largest companies. He is a peoples person and enjoys fast-paced environments and multiple responsibilities. He is especially curious about transformation and disruptive technology.

Harry holds a Masters degree in Law at Tartu University and is acquiring an MBA at Estonian Business School. He has also studied innovation management in Japan at Nagoya University of Commerce and Business.

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