Managing a call center is a difficult feat. If you haven’t been in a contact center or call center, picture this to put it into perspective: there’s an office filled with hundreds of agents and managers, fielding tens of thousands of calls, emails, and chats from thousands of customers. Taking it further, the agents are often new to customer service, have little to no professional training, and their average tenure is fourteen months. The agents are under pressure to solve issues or requests quickly while handling angry or upset customers. To add to the challenge, customer service departments strive to give every customer the utmost experience possible.
This article explains contact center automation, why it is important to start thinking about automation, and what are the different automation alternatives. We go through a step-by-step process of call center automation with artificial intelligence and demonstrate real use cases with examples from the Telecommunication industry.
What is call center automation?
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 contact 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.
High call abandonment rate
A high call abandonment rate can cost companies hundreds of thousands of euros annually, as some of the clients that try to reach the call center might want to buy something. For instance, based on our experience with Telecom, a 20% abandoned call rate actually costs 2% of potential sales opportunities, as on average 10% of the calls that were abandoned were related to sales. Of course, this number can vary from niche to niche, but call abandonment is definitely a problem that requires attention.
Bad customer experience
From the client’s perspective, long waiting times can negatively affect customer experience. No one wants to wait several minutes on the line until they are connected to a virtual agent and a bad experience definitely leads to higher customer churn rates.
Cost optimization is another argument for automated customer interactions. The cost of contact in developed countries can vary from EUR 3 to 10 per contact and sometimes even more. Just imagine the effect on a contact center handling 100,000 calls per month spends EUR 300,000. So what could be a potential cost optimization if AI could automate 20% of the calls? We are speaking about hundreds of thousands per year.
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.
- 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.
- 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.
- 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.
- 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 is checked by a human in the loop of machine learning.
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 CS assist tool 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.
CS 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.
For solving inquiries, AI uses the knowledge base called solution flows that allow making all kinds of different API integrations with the backend systems. You only need to keep an eye on the first contact resolution rate (FCR) and make solution flow improvements until you get optimal numbers, as otherwise, these will not actually solve the customer’s problem. From our experience, it takes around 4 attempts to reach an optimal FCR. What is even better, the same solution flows can be shared with the chatbot and all other customer service channels, making it a unified knowledge base for solving customer issues.
Implementation of the CS Assist tool helps to significantly decrease the onboarding time of your staff, as agents will simply follow the solution flows and read the text. The tool helps to meet SLAs, and decrease the average handling time.
Step 3 Automate the call
As soon as the previous 2 steps are fulfilled, you might be thinking about fully automating customer interaction over the call. When the solution flow reaches a good level of FCR, it becomes possible to integrate a speech synthesis model for full-contact resolution without any human assistance.
To make it happen, you need to fulfill the main condition:
- Solution flow should achieve a high level of FCR. From our experience, it is around 80%.
The CS assist tool allows you to build solution flows for various questions, so as soon as some of them are tested and have reached the FCR of 80, they become good candidates for intelligent automation. As soon as this is achieved and previous steps fulfilled, you can start fully automating customer interaction with the contact center.
Step 4 Get customers forwarded to the chatbot with an SMS
Wait for a second, we just stated that it is possible to automate contact center interactions with AI, why can’t we automate all inquiries as described in the previous step? Well, we have an answer to that question. A phone call has technical limitations and compared to the chatbot you have an additional step which is the call transcription model that turns spoken language into writing. In a nutshell, this means that you can automate fewer questions over the call than by using a chatbot.
So how does it work? Well, that is very simple. When the customer calls and explains their issue, first the AI model classifies the topic of the question as mentioned in previous steps. If at this moment AI detects a possibility to fully resolve the issue over the chatbot (whereas solving this over the phone call is not possible or will be time consuming), AI will ask the customer whether it is possible to send the link to a chatbot with an SMS that will solve the issue. If a customer agrees, they will automatically get the link to a chatbot. If not, the call will be transferred to the right agent and solved using the CS assist tool.
This additional feature helps to increase the percentage of automation and gives an opportunity for customers to interact with a chatbot, experience a good quality of service, and probably use it in the future as a channel for communication.
Elisa Call center automation case study
Elisa is one of the biggest Telcos in the Nordics operating in Finland and Estonia. As the telecommunications industry is very competitive, it is crucial to consider every customer’s unique needs and treat every contact as special, so as to guarantee a smooth and enjoyable customer journey and experience. As Elisa aims to be the leader in digital transformation, AI is one of the key fields that the company pays attention to. Today, Elisa has embedded a variety of AI models into their customer service and contact center that create a great customer experience.
This article outlines the importance of call center automation and lists different methods that can be integrated for better customer interaction with the contact center. As discussed, call center automation is the process of automating some repetitive tasks with technology. One of the purposes of automation with AI is to give an agent a possibility to focus on the most important things, where they are most needed, and make their work more efficient.
Businesses should also take into account the fact that automation saves a lot on costs, as today an average cost per contact is around EUR 3. Automation also helps to decrease the abandoned call rate to nearly zero, whereas a 20% call abandonment rate costs companies 2% of potential sales opportunities.
While a single machine learning model improves the customer experience, combining multiple models across customer service and bringing in data from outside the contact center brings true efficiencies while vastly improving the customer experience. We recommend reading our comprehensive guide that outlines how customer service can be truly automated.