During my college and postgraduate years, (AI) was the emerging technology to be aware of. Fast forward to now, I’ve been reading articles about the same technology, but how it can impact businesses across all sorts of orgs, especially in the customer service sector.
And one of the biggest ways that service orgs can improve customer experience is by leveraging this type of technology, and it can be done in more ways than one.
In this post, we’ll review how AI is used in call centers specifically, and what an artificial intelligence call center might look like. But first, let’s go over what AI call center software is.
Table of Contents
Call Center Software Solutions
Some common uses of AI call center software include:
- Automated
- Personalized customer engagement and feedback analysis
- Automated call routing for improved customer service
AI call center software can reduce costs, improve scalability, and increase speed and accuracy in customer interactions. But how so? We’ll breakdown these solutions in detail.
1. Predictive Call Routing
According to HubSpot’s State of AI survey data, 50% of service reps found that AI tools that route customer service requests to the correct representative somewhat improve customer experience, and 40% cite significant improvements to CX.
When I first heard of predictive call routing, I assumed that it was the technology that was able to route a call to a certain department. However, it’s actually more sophisticated than that.
Predictive call routing is when AI will match call center customers to specific customer service agents who are best able to handle an issue — whether it be because of personality models, or expertise.
This technology relies on customer behavior profiles to give a comprehensive understanding of the customer journey and customer personas. Meaning customer service (and the customer experience overall) can be hyper-personalized to each customer.
The software will look at natural predispositions and communication habits and match each query with the best-equipped agents to deal with specific types of customers and queries (based on personality, communication style, and call history), ensuring that tickets are closed quickly and effectively to free up time across the board.
Getting started with this AI requires companies to identify metrics to determine the personality characteristics of certain agents, average ticket time, and expertise on particular issues.
, featuring shows in the HubSpot Podcast Network.
2. Interactive Voice Response (IVR)
Interactive voice response (IVR) is the AI that most of us have interacted with during our customer service experiences. This is when you answer recorded questions such as what language you speak, your name, account number, etc. It’s true that many of us dislike this type of AI because we’ve had calls where we had to repeat the information.
However, this technology continues to improve. A solution created by helped the life insurance company route 60% of their over 1 million calls every month to AI with well-defined answers.
This type of IVR is for companies who have a lot of calls about routine, specific, pre-service questions such as hours, eligibility, copay, or bank statement information, that don’t require a human call center representative.
Since the system was implemented, the percentage of callers who use the AI-enabled system has doubled, and the cost of running it has dropped by two-thirds. Members calling in today can complete their initial inquiry in less than two minutes—and don’t wait to talk to a live agent.
3. Conversational AI
is mostly known as nowadays. This is when a call center will have an online chat option that is powered by AI. And it’s a necessary form of customer service since worldwide would like to message with brands, up from 65% last year.
As you can tell, chatbots have become one of the most popular channels for customer service inquiries. Customers can quickly engage with website content and use self-service support options in a live environment without meeting face to face with a service rep. This gives customers the ability to problem solve on-demand and reduces the load on organizations’ service teams.
The best part about chatbots is the ability to reduce call volume, so agents in call centers won’t need to answer simple, repetitive questions, and can focus on more complex issues.
But conversational AI goes beyond chatbots, it can provide a helping hand for your team internally, too.
Here at HubSpot, we have of our own that easily tracks your team’s performance. HubSpot automatically captures voice data in your CRM and provides deeper insights into your calls so you can coach your team better, and understand their performance.
4. Emotional Intelligence AI
Another form of artificial intelligence in call centers is emotional intelligence AI that can track customer sentiment during a phone call.
HubSpot’s State of AI survey data found that 50% of service reps believe AI tools that analyze the sentiment of customer service conversations somewhat improve the customer experience — and 34% claiming significant improvement.
For example, when a customer is frustrated, their voice might raise or there might be a long pause in the conversation. This type of AI is trained in different languages and cultural contexts, so it can be used in countries with different linguistic and cultural styles. It can analyze the tone of voice and cadence of language to try to detect the caller’s mood.
This AI will also measure how many times an agent interrupts a customer and the tone of voice of both the customer and the support rep. It will then give live feedback (via popup messages) to the agent to have insight into how the customer is feeling as the call is taking place.
After using this technology, at Cogito, clients have seen a .
5. AI-Powered Recommendations
Similar to the emotional intelligence AI above, other AI tools can give recommendations to a customer support rep during a call. This technology also uses sentiment analysis to understand what a customer is trying to accomplish. It can then give recommendations for the best solutions to the support rep.
This helps reduce call times and provides a personalized, positive customer experience. The technology can analyze how many times a customer has called or referenced canceling their account, then it can give that customer a customer risk score so agents are aware during the phone call.
6. Call Analytics
One of the main ways that AI is used in call centers is to provide in-depth analytics on call times, first resolution, and more. These technologies can spot trends and have access to customer data that will provide insight on whether customers are having a positive or negative experience.
Since AI measures customer sentiment, tone, and personality, it can provide more well-rounded analytics than a human customer support manager could.
Now that we’ve discussed how AI is used in call centers, you might be wondering, “How will AI impact my customer service team? Will it replace call center agents?” Let’s discuss it below.
Can AI replace call center agents?
The short answer is yes and no.
In a way, artificial intelligence can handle repetitive and simple calls by automating a portion, or all of a customer call.
This helps customer support reps because it gives them time to handle more sophisticated calls. But in a way, this can reduce call volume to live agents and can impact the number of reps needed in a call center.
In fact, Zendesk found that by 2022, will be handled by conversational agents.
However, there will always be complicated issues that AI simply can’t handle. The purpose of using AI in call centers is to improve the customer experience and relieve human agents of time and energy spent on simple requests.
AI can help customer support reps be more productive, have engaging and personally satisfying conversations.
Additionally, since the COVID-19 pandemic, digital shopping and customer service have become an expectation for many consumers. That being said, brands will need to adapt and continue to implement AI in their call centers.
Ultimately, AI can automate simple tasks, provide in-depth analysis, and help agents achieve a faster response time, better first-call resolution, and happier customer service agents who have the tools to do their job better.
