How can telcos use AI/ML to improve customer experience?
By 2025, Artificial Intelligence in telecom will be used in 95% of all customer interactions, including live phone and internet conversations, making it impossible for customers to know if they’re talking to a human or bot. Only a few years ago, service providers would mass market to consumers a single offer at a time (or a small number of offers). What we’re seeing now with AI is the potential to sell to much narrower client segments, providing a far better experience for customers. There will be no more wide strokes. As AI takes over the burden, micro-segmentation, custom-made products, and tailored experiences are becoming more prevalent. Here’s how AI and Machine Learning algorithms are changing the telecom’s customer experience.
• Offer Services That Customers Want
With AI/ML in the telecom industry, telecoms can look at their customers’ data, usage patterns, and purchases and identify micro-segments that may not be traditionally apparent. The next step is aligning product offers to these micro-segments, as opposed to having a broad stroke approach. By predicting and marketing new offers to these micro-segments, we increase the chance the consumer will be interested in that offer. There’s a couple of things happening here. Consumers get a better customer experience, getting more of what they want, tailored to them. And the flip side of this is more revenue per user, with the added ability to upsell components consumers might not have known.
• Create Products Faster
Data science in telecom can generate product proposals on its own. By examining the existing product portfolio, customer usage patterns, and customer complaints, data science, AI, and machine learning can assess this data and forecast that, for example, adding another 100 minutes of free voice to this bundle will have a high likelihood of success. It is capable of producing that output on its own. Before launching the freshly generated product, the product management person still validates it and double-checks that everything is in order and can deploy it soon. Furthermore, AI in chatbots can reduce mundane, manual activities to a bare minimum, allowing agents to focus on more complicated jobs and spend more time with those who need it the most. Customers may use AI to make it easier to complain, and it can even engage proactively to prevent complaints.
• Self-Diagnostic Fraud Detection
Fraudulent activities such as theft or phony profiles, unauthorized access, and more can be detected using machine learning algorithms. These algorithms learn what “normal” activity looks like, allowing them to discover anomalies in massive datasets considerably faster than human analysts, allowing them to respond to suspicious activity in near-real time thanks to data science, AI, and machine learning.
• Predictive Maintenance and Improve Network Optimization
Companies can use data-driven insights to monitor equipment, learn from prior data, predict equipment failure, and correct it before it happens. Network optimization is another crucial area where AI can help. Artificial intelligence-powered Self-Organizing Networks (SONs) can help networks adapt and rearrange based on current demands. It’s also useful for creating new networks. AI-enabled networks are more efficient at providing consistent service since they can self-analyze and optimize.
• Real-time Data-supported Decision-making
Telecoms possess enormous amounts of data from customers. With AI and machine learning, telecoms can extract meaningful business insights from this data to make faster and better business decisions. This crunching of the data by AI helps with customer segmentation, customer churn prevention, predict the lifetime value of the customer, product development, improving margins, price optimization, and more. By using AI and real-time decisions, it helps to recognize and understand a customer’s intent through the data they produce. Also, brands can present hyper-personalized, relevant content and offers to customers.
• Omnichannel Marketing
Omnichannel refers to the concept of using all of your channels, both online and offline, to provide your customers with a single, seamless, and personalized shopping experience. When customers interact with your brands through various channels, your underlying IT platform is expected to provide them with relevant and consistent service. It is possible with Artificial Intelligence in telecom. They will provide a recommendation that you will like with a good understanding of the customers’ preferences.
• Natural Language Processing
In the coming days, customers will manage 85% of their communication with the organization without interacting with a human, thanks to NLP. You usually think of chatbots when you hear the word natural language processing (NLP). Chatbots, on the other hand, do not fully leverage AI’s capabilities. Instead, they have AI listen in on client discussions when they escalate to an agent and provide suggested responses, pertinent documents, and, most importantly, prior engagement history. Connect your call center to your CRM so that all customer interactions are tracked in one place. Enabling AI to learn by putting new data into the model is one of the most important tasks for customer service. Agents can source content from the organization and feed it into the knowledge base when data science, AI, and machine learning are unable to answer a question.
What is your organization doing to improve customer experience? Is it using AI/ML for your businesses? If yes, then feel free to share your comments in the section below.
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Subex is a leading telecom analytics solution provider and leveraging its solution in areas such as Revenue Assurance, Fraud Management, Partner Management, and IoT Security.