After this release and its fascinating success, it was just a matter of time until businesses adapted AI integrations for their products, and now the time has come. Numerous providers in the contact center industry are competing in releasing their new AI customer service platforms, as AI in call centers opens unbelievably wide opportunities for providing agent-free customer service.
Call center AI software is now something not special, but common - you can surf through the Internet and see numerous developers offering contact center AI solutions as platforms that integrate some AI-powered features.
But has call center AI already won? How will AI change call centers and how artificial intelligence is transforming contact centers now? How will AI affect call center jobs? How can artificial intelligence improve customer service? What is the future of AI in call centers?
All those questions are still unanswered, and we are going to change this.
What is call center AI?
Call center AI, or contact center AI is a technology that is used by call centers to automate tasks, replace human interactions with AI support in some cases, improve data analysis processes and their efficiency, establish a deeper understanding of customer sentiment, and improve overall customer experience and customer service by offering new solutions, more efficient call routing and self-service.
To put it simply, contact center AI isn’t just one technology you just get and integrate with your contact center solution. It is an extensive term, that refers to such technologies as Machine Learning, Natural Language Understanding (NLU), and so on.
In this way, contact center AI now can be used to enhance every single process in a contact center, as there are almost no limits to this technology. It can help contact center agents, route calls, handle customer queries, analyze data, compile reports, and so on.
Top features of contact center AI
Contact center AI sentiment analysis
Customer sentiment analysis is a term that covers all practices and techniques used by AI call centers to analyze customer sentiment by researching customer interactions. Sentiment analysis as a part of contact center AI technology is one of the main advantages that contact center AI provides over traditional contact centers.
Sentiment analysis allows you to identify customer intent critical moments in customer conversations and determine at what stage of the customer journey or even at what moment of customer service conversations the conflict may occur. Thus, as you are equipped with this data, you can adapt your call scripts, agent training programs, and so on to meet customer expectations and improve customer satisfaction.
Contact center AI helps to establish customer sentiment analysis in different ways. First of all, contact center leaders can use Machine Learning (ML) algorithms to teach them to identify some keywords in customer interactions that point to both positive and negative emotions, so the contact center AI platform can identify these phrases or keywords on its own and provide you with detailed reports.
Secondly, you can use the Speech analysis feature - a part of the Natural Language Understanding (NLU) technology - so the contact center AI system will listen to customer calls and identify those critical moments to measure customer service experiences - this is called Voice Recognition. The system can even provide real-time transcriptions of the calls.
Contact center AI self-service
Contact center AI is a very strong technology when it comes to customer self-service, as it is the only technology that can provide conversational self-service for customers, which means it offers a different level of quality.
Contact center AI self-service can be provided via IVAs (Intelligent Virtual Assistants), or AI-driven chatbots.
IVA, also known as virtual agents, is a conversational contact center AI platform that answers customer calls and provides self-service options or routes calls further to human agents. In other words, it is like IVR (Interactive Voice Response) v.2.0. What differentiates it from the IVR system? Well, first of all, IVR systems aren’t conversational systems at all. They provide only pre-recorded answers to customers, so there is no flexibility in such self-service and it can’t simply resolve complex customer issues. Secondly, IVR relies on its design only, so if a company pays too little attention to customer needs and customer preferences, poor IVR design leads to sky-high customer dissatisfaction rates, poor customer experience, and even conflicts with customer service agents. Yes, there are IVR systems that can recognize human voice and speech, but still - this is just another example of contact center AI integration, as such IVR solutions use Natural Language Processing (NLP) technology.
IVA, on the other hand, can provide conversational self-service, which means customers can communicate with it as if it were a human agent - so it can furnish exceptional customer experiences. This helps businesses kill two birds with one stone - IVA still does the work of the IVR, which means it can optimize inbound call volumes by handling customer inquiries in self-service mode and providing agents with less workload, and it will provide such self-service most efficiently, so you don’t have to worry about all issues that relate to IVR use.
The second example of contact center AI for self-service is contact center AI chatbots. Contact center AI chatbots aren’t limited by pre-designed messages, so they can provide opportunities efficiently, which means customers will resolve more of their issues through the contact center AI chatbots without involving agents in this process. Contact center AI chatbots will still route complex issues to human agents when needed, so there is no way customers will be left without the assistance they seek.
Contact center AI call routing
How does call routing work now in modern contact centers that don’t use contact center AI? Well, some different technologies and algorithms are widely used, but some of them are very inefficient and do nothing but harm customer experience - first of all, this relates to auto attendant call routing, where customers can choose nothing in the auto attendant menu, but the organization’s department he wants to contact with. This algorithm is bad at least because the system can’t identify the key parameters of a call, so there is no way the system can provide intelligent call routing to a relevant agent.
Another example is IVR plus ACD (Automatic Call Distributor) call routing, where during customer interaction with the IVR system, the system collects customer data about the call parameters (it is called call logging), and the customer request itself, and the ACD system uses this data to route call further. This allows you to set up intelligent call routing, but still, its efficiency and accuracy are limited.
Contact center AI call routing goes much further than any algorithm that was used before - it understands customer speech, and has access to customer data stored in databases, such as CRM systems and so on, so it can choose the most relevant agent available and route customer in a few seconds, without long data processing contact center operations.
What do we have as a result? First of all, the First Call Resolution rate (FCR) is greatly improved, as a customer is routed directly to the specialist who is qualified to handle the issue. Secondly, the Average Handle Time (AHT) is reduced. Finally, the Average On-Hold Time is also reduced, as agents have access to needed information to provide qualitative customer service - but how it works we’ll tell in the following paragraph.
Contact center AI knowledge management
Contact center AI's is one of the best technologies to manage knowledge and empower agents to provide customer service of the highest quality. Contact center AI can provide agents with all the needed knowledge sources during customer conversations, which means that no customer questions will ever put agents in difficult situations.
Imagine that agents don’t know some vital technical information about the product, and the customer needs it now to handle the issue, so the conflict seems to be unavoidable. But the contact center AI system can in a few seconds find needed data, and agent even doesn’t need to ask for it - contact center AI understands what the customer says, finds the information, and provides it to an agent. The speed of such assistance is so high that customers won’t even notice the delay.
Contact center AI quality assurance
If you have a quality assurance team, you do know that it is impossible to check out the quality of every call - or you will just spend too much time and money on quality assurance, which is again impossible. Contact center AI eliminates this issue and provides you deep insights into each call and agent performance of each single employee.
Contact center AI can identify customer sentiment during calls, agent performance, and even the tone of voice can be evaluated. Contact center AI also identifies whether agents are meeting company service standards or not, and this data is accessible to every agent. In other words, you can identify imperfections in work without putting any effort into it and make necessary adjustments in time.
Contact center AI resource management
Contact center AI can also help you plan and manage your resources - especially human resources. How does it work? Contact center AI can forecast call volumes, customer preferences, customer satisfaction levels, and your downtimes and peak hours, so you can adapt agent scheduling to meet those forecasts.
It doesn’t only save your operational costs but also helps to manage agent satisfaction, agent experience, agent productivity and a healthy environment in the team. Additionally, with the help of contact center AI technologies, you can improve your schedules and agent training programs to retain staff motivation and agent engagement.
Contact center AI benefits
Outstanding customer experience
With the help of contact center AI, your AI-based call center will be able to retain fantastic rates of customer satisfaction, customer trust and customer loyalty. First of all, let’s define how contact center AI impacts customer experience. Artificial intelligence in call center helps to improve the quality and efficiency of call routing, which means your average wait times will be reduced and so will the call abandonment rate be.
Advanced self-service options through different communication channels will allow customers to choose contact channels according to their preferences, no one will push customers to choose only phone support. Deep sentiment analysis provides you with comprehensive data to learn more about customer experience, customer expectations, and current service levels in your contact center.
Reduced inbound call volumes
Thanks to powerful self-service tools, AI for call center reduces the workload on inbound lines, which means agents can allocate more time to resolving customer problems that deserve a human touch. Common issues and elementary questions can be resolved in self-service mode, both via IVA or via chatbots on the website.
Even with reduced call volumes, AI in call centers will still collect data about your peak hours and downtimes to help you design agent schedules in accordance with the real needs of your AI call centers.
Better team productivity and motivation
Call center AI provides agents with all the needed information and tools to improve their performance while servicing customers. This includes providing knowledge sources, insights, customer data, and so on. Thus, call center AI is not designed to replace call center agents, but to help them grow professionally and improve their productivity and the quality of service they provide.
Also, call center AI automates numerous routine and repetitive tasks that only consume agent time and effort instead of spending these resources on something that deserves such spending. Call center AI is the best solution if you want to help your team achieve new levels of performance rates and build stable and healthy communication among team members. Call center AI improves both agent efficiency and operational efficiency.
Operation costs reduction
Call center AI is also the way to reduce operational costs as it helps you to understand your imperfections, especially when it comes to resource management. Call center AI provides you with deep insights into your call center performance, so you can see whether you can change your staffing policies or not. Moreover, AI call center software can help you improve the onboarding and training processes for agents, which means you will spend less on both these aspects of your business.
Contact center AI software can also help you to use your agents more effectively - for instance, if you can’t measure the number of required agents for both peak hours and downtimes, you lose a lot on inefficient scheduling.
Conclusion
To answer the key questions we have mentioned at the beginning of the article: no, call center AI isn’t going to replace either agents or any other staff of the call centers. Call center AI will focus on improving customer experience, overall quality of customer service, internal quality assurance procedures, data collection and analysis, and finally yes, call center AI is going to change call center self-service and the way contact center software works now. Prebuilt agents, real-time insights, a wide range of self-service options, enhanced workforce management, streamlined customer communications, and control over the entire customer journey - that's what call center AI offers to contact center providers and their customers.
Nonetheless, even though virtual agents already exist it doesn’t mean that they are going to eliminate the call center agent job. Yes, some call centers may rely on call center AI more in the future, which means fewer agents will be hired. But still, customer service is impossible without human touch - that’s why call center AI is just a tool, even though it is a kind of game changer, but it all depends on whose arms will guide this tool.
Contact center artificial intelligence is also going to change the analysis of customer behavior, not only by providing personalized customer experience but by fast identification of the smallest signs of customer frustration and by providing of fast resolution of customer issues.
