From customer service to delivery, personalization to product quality, complaint management to social responsibility, customers expect the first-class experience from service providers where and when they interact with the business. If customers receive anything less, they will resort to a competitor who will meet their expectations.

Through automation and artificial intelligence in telecom can improve its operating quality, while reducing their operating costs.

Telecom operators are in a good position to develop AI through three key purposes: a big data source, exploiting advances in the field of AI, and a wider customer base.

Best service of Artificial intelligence

Currently, one of the major AI in telecom companies is providing better customer experience and reducing the use of Opex by driving automation.

The reality is that consumers spend too much time with customers, but in order to be successful, companies need to consider the service point of service malfunction and figure out what they need to do to eliminate demand. This means that everything must work perfectly and they can be eliminated before problems arise. This could be a problem with billing or networking, buying a new product or connecting with the right agent. All of this can be avoided by implementing different AI models.

But improving the customer experience and speed of service in the telecom industry is only the beginning. The next big step in telecom is the AI-defined network, where the goals and limitations are set by providing the customer with the experience they really need, and the network control software builds the network based on current conditions. The use of AI in such content helps telecoms to minimize human interference and reduce operating costs while configuring, delivering, and maintaining networks while allowing consumers to quickly onboard new products and services to market.

How telecoms can benefit from AI

We have mentioned already different solutions how AI could impact the 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.
• Email Bot — makes Email support easier. Having a bot reply with answers or follow up questions. Routing other emails to the right people the first time.
• CallBot — AI bot will ask customers for their problems, transcribes calls in real-time, then routes the call appropriately.
• Call Analyzer — helps to gauge agent performance and assist with coaching, while measuring customer sentiment.
• Contact prevention — helps to predict when & why customers might contact you. Automation tools help to encourage self-service or provide immediate solutions.
• Omnichannel Analysis — helps to analyze communication and build models across every channel to communicate with customers.

Grow sales

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

Network technologies

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

Business operations

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

How do telecoms start with an AI strategy?

Telecom is one of the largest industries that can collect data and take advantage of big data. However, there is one common question about where to start with artificial intelligence. This question usually follows where and how to focus their efforts, as there is data. Do 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 aspects of the telecom business are positively impacted by AI. But that is not a viable approach. MindTitan has a clearly defined process that we use with telecoms to understand business objectives, audit existing and future processes from both angles, and create an AI roadmap to truly gain a competitive advantage in business.

Start with business objectives

When the telecom executive management team makes strategic decisions, these decisions come to business unit leaders and become time-bound goals and objectives.

For example, expanding market share in a particular area this year is a strategic decision that will become targets for cross-functional business units. Sales and marketing have a goal to increase demand and convert that demand into revenue. Customer service must retain customers, avoid losing revenue, and field inbound inventories on sales. Risk management is also implemented. Telecom must understand whether the products they offer meet the market requirement or whether the core products must be updated or new products and services created.

Audit existing & future processes

Sure, it’s exciting to think about jumping into data and getting started with artificial intelligence planning, but there is a key step that telecoms must take before getting data: auditing existing and future processes.

Why? Well, artificial intelligence and machine learning models do not solve broken business processes. Working with an AI specialist, processes can be re-thought, making them scalable, more efficient, and more effective.

During this audit, it is important to look at the processes from two angles. First, someone who understands the process, usually someone from the business (also known as the “problem owner”). Second, someone with artificial intelligence expertise must see the process because this person offers a fresh, data-driven perspective of the process. This person is usually a data scientist who understands whether there is a business impact use case, or whether the target data is present across business units and interconnected processes.

Also, the AI expert can help you understand the most appropriate starting point. For example, an area telecom can start with machine learning, finding ways to reduce friction on the customer journey to increase revenue, without losing out on opportunities to competitors. Telecom has comprehensive information about customer behavior data, as well as customer personality and product information. It is possible for a human to analyze this data and make decisions, but it takes a lot of time — the customer is already ahead. Using deep neural networks, an AI model can understand the many ways a customer can make decision-making and which path to use based on customer personality, micro-segmentation, and behavior. The AI model automates the process of “spiraling” the customer in the right direction, such as pushing the customer towards the direction of education.