Call center automation with AI – how to actually make it work?

Why is call center automation important?

Call center automation has become especially critical during the Covid crisis. According to CGS research, 74% of CX leaders reported a significant increase in inquiries in their contact centers during the pandemic. This brought about longer waiting times, higher call abandonment rates, and lower first call resolution rates (FCR).

Even though a sudden increase in call volumes has a temporary effect, it actually can happen to any organization at any time simply due to a new service, a policy change, a technical failure, or even a merger of two organizations. No one can predict the occurrence of all such situations, but undoubtedly businesses should be ready for a sudden increase in customer inquiries or planned scaling of the business.

Call center automation alternatives

There are several alternative automation processes that can be integrated into a contact center. In theory all of these decrease the typical contact center problem of long waiting time.

1. Self-service that would allow customers to find the necessary information on their own without actually contacting a call center. The problem is that on average only 9% of customers that use self-service actually resolve their issues, again leading to a decrease in customer satisfaction and an increase in the number of inquiries.
2. AI-powered chatbot that could potentially resolve most of the customer inquiries. In fact, based on the example of one of the biggest Telcos in Nordics, Elisa, a chatbot can actually handle 60% of all written inquiries, out of which half can be fully resolved without any agent assistant. However, a chatbot cannot be an ideal solution and alternative to a contact center, simply because  53% of consumers still prefer to call, instead of texting a virtual agent or a chatbot.  The manager of a contact center at a prominent Northern European bank noted that they did not experience an immediate decline in customer contacts after they introduced live chat to their website. Hence, the volume of customer contacts increased after they deployed online chat, while the number of customer service agents remained the same.
3. Simplifying the IVR. As customers, we don’t like too many options when we reach a contact center, and customer service managers understand that, so they simplify. There are usually three, but there can be up to about ten options. The goal is to get the customer to the correct agent as fast as possible. However, based on the work MindTitan has done with contact centers, on average customers wait 2.4 minutes on the line before they are connected to the right agent.  Whether this happens due to a complicated IVR structure (as in fact, in 30% cases customers press the wrong button) or simply because of a misclick by a customer, it all leads to poor customer satisfaction and a high abandoned call rate.
4. Hiring more people. Well, some organizations do believe that opening a new contact center and hiring additional people might change the status quo. However, there are some problems you should consider before you choose this option.

First of all, pay attention to the agent turnover factor, which is considered the number one challenge for contact centers. As agents should often know answers to hundreds of different questions, the cost of staff training is high.

Secondly, managers do not have proper tools to monitor the quality of the actual conversations with clients, as they only have the capacity to choose some random calls to listen to. When you have several hundred agents in a contact center, quality monitoring becomes mission impossible.

Both of the mentioned factors lead to a situation where it is difficult to sustain the service level agreement (SLA). Particularly because new staff is constantly coming in and on average it takes 6 months of working experience and training to be well prepared for actually solving customers’ issues. Considering that the average agent turnover is fourteen months, this becomes a real challenge. We have interviewed Mailiis Ploomann, Head of Telecom services at Elisa, who mentioned the optimal number of contact center agents for customer service that is possible to manage efficiently without a decrease in quality of service.

Steps for contact center automation with AI

 

Contact center intelligent automation can be achieved in several steps. The exact number depends on the complexity of the customer service and contact center operations. Let’s go through the basic ones.

 

Step 1. Get rid of the traditional IVR

 

Wouldn’t it be much simpler, if a customer would just explain the reason for the call with their own words without the need of pressing any buttons? Often people are confused by the complicated structure of the old legacy IVR, that it is actually not very efficient anymore. Conversational IVR simply asks the customer, “What can I help you with?” as soon as the customer has reached the contact center.

 

 

As the customer describes the issue in their own words, the machine learning model listens and transcribes the call in real-time. Since the model is taught using previously recorded data, it knows which words and phrases are associated with specific customer issues – or reasons they’re calling. Based on the classification, the call is routed to the right agent the first time.  And the model only gets better as it categorizes call event data, which a human checks.

This significantly helps to improve customer experience as it decreases wait times and makes it possible for the business to prioritize particular clients, e.g. sales opportunities, customer retention, or technical failures.

Step 2. Increase efficiency of your agents

Call centers face problems of agent retention and costs of new staff training. As a business is trying to provide a better customer experience and fulfill the SLA’s, it needs to have a proper tool to support its agents. For example, the agent assist software helps agents to ask the right clarifying questions and give the right answers by following the conversation in real-time using enhanced customer-specific AI models.

Agent assist tool works in combination with Conversational IVR, so when a client is connected to customer support, the agent already sees the transcription of the call and the most probable reasons for the contact classified by AI.  The agent should simply follow the solution flow and read out the text. Check out the video below that demonstrates that in action.