What if your call center could predict what your customers want regarding a product or other issues even before your agents pick up the call? It may sound futuristic, but it is not. Predictive AI-based analytics is making it possible today. Instead of waiting on hold and looking for an agent to solve the problem, call centers can now find out what their customers need and provide personalized interactions. Predictive analytics has also helped prevent common issues before they escalate.
With this technology, businesses can bring desired efficiency in their call centers and improve it up to 30% and offer real-time, data-driven solutions to achieve customer satisfaction. From predictive call routing to anticipating customers and providing automated resolutions, predictive analytics revolutionizes contact centers.
In this blog, we will look into the key components of predictive AI, its use cases, and its impact on customer experience and businesses.
The Major Components of Predictive AI-Based Analytics
AI-based predictive analytics brings together different forms of raw data to provide value insights. You can imagine it as a well-orchestrated symphony where data, algorithms, and predictions work harmoniously. All these three components work together to provide you with the required information, depending on which you can make informed decisions for your business.
Data
Data may come in all shapes and sizes. It can appear in neatly organized data like numbers and categories. Also, it can be unstructured, such as in texts, images, and videos. It doesn’t matter in what form it appears, it must be accurate, relevant, and comprehensive so that predictive analytics can properly provide intelligent predictions.
Algorithms
These are the powerhouses behind AI-driven predictive analytics based on mathematical models. These algorithms analyze the different patterns in data and, after learning from them, provide and refine predictions over time. It can be taken as AI’s way of recognizing trends, whether detecting potential risks, looking into customer preferences, or improving automation.
Predictions
Once the data is fed through selected AI algorithms, it transforms into actionable insights. Using the insights, businesses can make informed and required decisions to improve their operations after anticipating trends and detecting anomalies.
How Predictive AI Works in Call Centres
AI-based predictive analytics helps call centers to become proactive from reactive by implementing various methods like data analysis and machine learning algorithms. Contact centers get actionable insights that help them reduce wait times and improve customer satisfaction.
Data Analysis
It gathers historical call center data from various sources, including CRM systems, call logs, and customer interactions. After analyzing customer demographics, call duration, previous interactions, and agent performance, the technology helps predict the future behavior of customers to help businesses reach a required decision.
Use of Machine Learning Algorithms
Predictive AI uses machine learning algorithms to identify statistical models, trends, and patterns in the data to predict future outcomes and possibilities. Machine learning algorithms can analyze language and voice tone to determine customer sentiment, power chatbots to handle basic customer inquiries, and leverage customer data to provide recommendations.
Actionable Insights
Predictive AI-based tools transform raw data into meaningful information that managers and related people can use to make informed decisions. Whether it is about looking into customer feedback or analyzing agent performance, the tools make it easy and allow you to address potential issues. Examples of actionable insights in call centers include analyzing customer sentiment, peak call volumes, call abandonment rates and identifying common customer questions.
AI Tools Used in Call Centres
The rise of artificial intelligence has led modern call centers to use a wide range of tools based on this technology to serve their customers’ problems.
Conversational AI & Chatbots
Conversational AI Chatbot uses natural language processing, allowing customers to interact with virtual agents.
Predictive Analytics
Analyzing historical data allows call centers to anticipate future call volumes, customer behavior, and potential problems. The technology helps predict call surges based on seasonal trends, marketing campaigns, or the launch of new products. Based on these call patterns, identifying customers at high risk of leaving becomes easy.
Sentiment Analytics
AI-based advanced technology in modern call centers uses natural language processing to analyze recorded calls. After studying these customers’ calls, it can extract sentiment from them and identify positive, negative, or neutral tones. Based on the emotional state of the customers, agents can adjust their approach toward customers and provide more personalized resolutions. Also, call centers can generate dashboards and reports to view sentiment trends over time and identify patterns and areas that require more focus.
| 💡Also Read | Sentiment Analysis for Better Customer Interactions in Contact Center |
Use cases of Predictive AI
Predictive AI is already transforming call centers through predictive call routing, anticipating customer needs, providing automated resolutions, and recommending best actions.
Predictive Call Routing
You can think of it as an innovative matchmaking system for customer service calls in your call center. Earlier, call centers randomly assigned customers to the next available agent. AI-based predictive call routing transfers customer calls to the best-suited agent who can solve problems quickly. It happens because AI analyses many things about customers, such as the interactions they have had in the past about products or some other issues, their reasons for calling, and even their mood. The mood of the customers is analyzed based on tone or history.
Anticipating Customer Needs
AI-powered predictive analytics looks at customers’ past interactions, purchase history, and browsing behavior. It acts like a crystal call, so you can anticipate your customer’s needs even before picking up their calls. Using the insights from predictive analytics, you can instantly suggest the right products to your customers at the right time or connect them to the right support agent.
Imagine John is your customer and facing billing issues. To get his problem resolved, he frequently calls your agents. He finds it frustrating to repeat his details whenever he contacts agents. This is what AI-based predictive analytics doesn’t allow to happen. The software instantly recognizes the past interactions between John and different agents, pulls up relevant billing FAQs, and directs him to a billing specialist. In this scenario, he doesn’t need to explain everything again.
Automated Resolutions for Customers
Various examples of AI tools generate automated resolutions in call centers to help agents and customers. Take the example of interactive voice response, a menu-driven system that provides customers with basic information or directs them to the appropriate department to solve their issues based on the input.
Likewise, chatbots are AI-powered virtual assistants that can resolve customers’ simple questions on chats through different channels. There are online self-service portals that can manage accounts and access information to resolve the issues immediately. Such self-service portals allow customers to resolve their problems at their own pace.
| 💡Get the Guide | The Ultimate Guide to Chatbot |
Let’s understand this with a real-life scenario. Imagine you want to check your bank statement, track online, or reset your password. Instead of waiting on hold for an agent, you can get guidelines through an IVR that can guide you through a menu to reset your password or check your bank statement.
Or, in another scenario, let’s suppose you are doing online shopping and want to return the product. But you don’t know the return policies and have many questions about it. In that case, a chatbot can instantly provide you with the answer.
Next Best Action Recommendations
AI-based predictive analytics analyze customers’ past conversations, preferences, and current needs. Based on this, it sends personalized offers, recommendations, and solutions to each customer, known as following best action recommendations. Next best action recommendations help agents get clear guidance on the next best step they can take and reduce the need for extensive research during a call.
To understand it in more detail, we can consider this scenario. Imagine you are an agent and talking to a customer. The customer has just bought a new smartphone. As an agent, you can thank the customer for purchasing and move on. However, AI-based predictive analytics can make this conversation smarter by suggesting to the customer a matching pair of wireless earbuds or a discounted protection plan based on your customer’s past product preference and purchases. These things genuinely add value to your customer’s purchase.
The Impact of AI Predictive Analytics on Customer Experience & Cost Reduction
Imagine a world where customer interactions are faster and more personalized. Businesses leverage AI-based tools and software, such as predictive analytics, to provide faster resolutions and reduce call volumes, resulting in higher customer satisfaction.
Provide Faster Resolutions
Imagine a world where agents have the correct answers for customers even before they finish explaining their problems. This power to agents comes from predictive AI analytics. Predictive AI helps agents get ahead of customer needs by providing quicker and more personalized resolutions. AI-based predictive analytics tools can anticipate customers’ needs and suggest more relevant solutions by analyzing customer preferences, past interactions, and real-time behavior.
This saves agents from scrambling for information on multiple channels and allows them to provide quick answers that reduce customer wait times and average handle times. Eventually, this improves first-contact resolutions, as customers get an instant answer from the agent and don’t need to contact the agent multiple times for the same problem.
Hence, customers get a smoother, frustration-free experience, and your business leads to more efficient and cost-effective operation. When your customers feel helped quickly and heard, they want to remain loyal to your brand and recommend it to others.
Reduced Call Volume
Not even a single customer wishes to wait on hold or navigate phone menus to get his simple question answered. Here comes the role of AI-driven self-service channels such as intelligent virtual assistants, knowledge bases, and chatbots that help customers quickly find the information they need without ever needing to speak to any agent.
When customers try to track their orders, update account details, or find a solution to a technical glitch, they can get instant help through an AI-powered voice or chatbot instead of calling in and waiting for assistance, which often takes time. With these self-service options, customers get instant help, and agents are free to handle and focus on more complex problems.
For businesses, this means savings on operational costs, as they can manage call volume efficiently, leading to less need for staffing during peak hours. Agents can spend more time on individual customer interactions, leading to better resolution rates and faster handling times. Hence, quick resolutions and shorter wait times due to lower call volumes result in more satisfied and happier customers who stay loyal to your brand.
Higher Level of Customer Satisfaction
When customers feel like your company truly cares about their needs, they tend to come to you every time. Also, it raises their satisfaction level and expectations from your company and support channels. Here, predictive analytics makes your customer support proactive and allows your agents to provide proactive service by delivering insights about customers’ needs. With the help of such insights, your customer support service can provide more personalized solutions to customers before they arise.
Proactive service ensures that your customers receive personalized solutions, timely updates, and helpful information right when needed instead of chasing down solutions for their problems or struggling with them. Suppose you are at the airport and receive a notification announcing that your flight departure gate has been changed from gate no. 8 to gate no.12. Now, you don’t need to ask the agent or search for information. This act will provide you with a smooth traveling experience without any stress.
Take another example of proactive service that can make your life much more convenient. Suppose you are facing a slow internet problem in your area. Instead of calling to report it, your telecom provider will message you that the department is working on it and that the internet facilities will be restored in two hours. This action will keep customers like you informed and prevent frustration and stress.
Cost Savings for Businesses
When your contact center offers quick and efficient services to customers with streamlined operations, it reduces unnecessary calls and burdens on your agents and improves overall productivity. For example, suppose a customer has purchased an electronic product and wants to know its return policy to initiate a return. Instead of calling an agent and waiting on hold, your contact center can provide the customer with a self-service option, an automated IVR system, or an AI chatbot to generate a return label instantly or get accurate time tracking updates.
Now, if this starts happening at scale and just 20% of routine inquiries are handled by AI-based automated tools, then it will free up your agents to focus on more important and complex interactions. As a result, you will get lower operational costs and happier customers.
Wrapping Up
At the end of the day, AI-based predictive analytics is not only about technology; it’s about people, too. It’s about liberating your customers from the need to repeat their problems every time they reach your agents, wait on hold for longer times, and navigate frustrating menus to get answers to their simple questions.
Predictive analytics empower agents by providing the right insights and customer information at the right time to provide more personalized and quick support. When talking about businesses, the benefits of this technology go beyond just happier customers. They improve first contact resolution due to lower call volumes and reduced handled times. Hence, they get enough time to make wise decisions that add to significant cost savings.
Contact centers that are adopting predictive analytics are paving the way for a better future and more growth, and they will stay ahead of the curve by offering seamless, proactive service, due to which customers will always keep coming back.
If you are ready to unlock the full potential of AI-based predictive analytics in your call center, then NovelVox experts are always there to help you make this technology work smoothly and bring excellent results.
