Call centres on the AI frontlines: Improving customer care

Illustration by Ana Duje.

Call centres don’t look like they used to. Automation, customer data access and added paths for communication allow for more customers to have their needs met at once, through self-service or by human representatives. 

Technology has changed call centres, but the goal of customer care remains the same: to build an emotional connection between the customer and your brand. This evolution of “contact centres” hasn’t changed what customers want either: they still expect a personalized experience throughout their journey with a brand, especially when it comes to customer care. 

Customers expect to be treated with empathy and responsiveness—even from a chatbot. And whether their needs are met or not, customers often go online to share exactly what they think and feel. That’s valuable data within the customer care sphere too. Harnessing the insights within that data is the next step to providing more emotionally responsive, loyalty-boosting customer care through call centres and throughout your company. 

 Harnessing insights is the next step to providing more emotionally responsive, loyalty-boosting customer care.

As part of a complete customer care strategy within CX, text analytics has allowed companies to understand their customers’ needs and motivations through a variety of feedback channels, including online feedback sites, surveys and call centre conversations. Analyzed correctly, all that valuable data can be used to build more successful customer experience, service and care strategies. Let’s dive into what insightful data analysis looks like in a call centre setting and how it can improve customer care. 

AI text analysis and call centre improvements 

Several call centre advances can be traced back to AI, including text analytics of feedback data:

  • Automated messaging and voice response answer common problems and FAQs so customers don’t have to bounce through a phone or messaging system. 
  • Online chat gives customers an option that is more immediate and preferred than making a phone call, providing answers about products and services they’d like to buy or have already purchased.
  • Advances in chatbot technology are focused on creating chatbots that are more accurate in their answers to customers’ questions and more non-judgementally empathetic in all their responses. 

The better your company knows its customers, the more high-quality, mistake-free customer care you can provide.

Yet even in these technologically advanced call centres, both human agents and chatbots make mistakes that can cost companies customers and revenue. The better your company knows its customers, the more high-quality, mistake-free customer care you can provide. AI data analysis offers the kinds of insights into customer behaviour that translates into solutions to customer experience problems. 

An AI-powered call centre use case 

A customer goes to a company site they’ve used before. They log in, ready to make a purchase. Yet in the course of looking through product specs, they come up with a number of questions that seed doubt the product would work for their circumstance. 

So they go for it. They hit the chat button in search of answers. 

In this case, a live agent answers the chat. That agent has access to a company database featuring every customers’ purchasing data, including reviews and customer care and service interactions. Even before the agent responds, they have access to the customers’ historical touchpoints with the brand, can assess the customer’s relationship with the brand and how responsive the brand has been to the customer’s needs. In essence, the chat is about more than the product or service discussed directly: it now has the potential to accurately determine how the customer feels about the brand as a whole.

What customer have to say won’t be forgotten, but instead integrated with all company-gathered data to be analyzed strategically.

With that context in mind, the chat goes ahead, problems are solved, human connections between customer and brand are made. And data is gathered that represents a two-way street, that is, this level of data gathering and analysis reassures customers: what they had to say won’t be forgotten but instead integrated with all company-gathered data to be analyzed strategically. 

Customer care is only the beginning

With advanced call centres on the front lines of customer care gathering data, companies can apply the insights determined by AI text analysis tools such as Keatext to several departments, from customer experience and marketing to creating customer responsive products, as a recent Forbes piece pointed out: “companies can determine what a future product should do and look like by gathering information via buying habits, surveys and even case scenarios from customers… Product design is focused on fulfilling the needs of customers in ways never possible before.” 

As a way to improve customer care and reduce customer churn, AI text analytics tools provide insights that companies can transform into actionable information for customer care agents and chatbots. In turn, they can gather equally relevant data from calls and chats. The end result is a healthy retention rate with customers who feel more connected to the brand. 

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