Five Contact Center Automation Examples You Can Implement Right Now

We’ve rounded up five of the best contact center automation examples for help you drive efficiencies and meet the changing needs and expectations of your customers.

The global pandemic accelerated these changes, and highlighted the competitive advantages that contact center automation provides. According to CCW Digital research, nearly 60% of consumers now care more about the customer service experience, as a result of COVID-19. The top two priorities consumers demand are “accuracy of information and resolution,” (42%) and “speed and efficiency” (33%) – only obtainable through the right technologies.

Many customer-centric companies are choosing to automate their contact centers so they can keep their business running while:

To help you keep up, we’ve highlighted the best contact center automation examples, and how to implement them, below.

The first step to automating your contact center is to find out where your data lives. Whether it’s on-premises or in the cloud, understanding how to get your hands on it is crucial. If your organization doesn’t have a proper strategy for unifying data, AI can become costly and unmanageable.

For this reason, it’s best to consider implementing a solution that has a built-in customer segmentation tool that keeps it all in one place – and preferably one that doesn’t need massive technical or development investment. Once all your data’s been connected, this type of solution makes it easy for you to:

Combining and segmenting data in this way eliminates the need to prepare messages manually and creates a ˝data catalog˝ so agents of all skill levels can pick and choose based on customer needs – helping them provide personalized customer support.

Automation is all about adapting the data you have coming in to help agents better serve customers through the channels they prefer. It’s also why more and more companies are moving their contact center to the cloud, introducing chatbots, and providing automated voice services.

What’s more is the automation examples mentioned above are channel agnostic, meaning the AI and customer data needed in one channel can be easily incorporated into another. They also learn over time, as they expand and are fed more data, through natural language processing (NLP) and machine learning.

Although implementing these new technologies seems like a process, the right cloud contact center solution can make it easier – for both you and your agents. Below we’ve outlined how to make the most of automation in your contact center when you couple it with a web interface like Conversations.

With Conversations, your contact center team can access all the data they need, including information from your own CRM, ticketing, and ecommerce systems in one place.

When your agents log into the web interface, they can access:

This eliminates the need for agents to switch between tabs and tools and gives them relevant information at-hand – improving their time to resolution.

Using the drag-and-drop chatbot building feature within Conversations enables your support team to create a keyword- or intent-based chatbot – no coding required. These chatbots help offload simple queries such as FAQs from your agents and provide an immediate response to customers – improving your contact center’s first contact resolution.

Keyword-based chatbots enable you to set up specific responses for customers who use a predefined keyword in their message.

Common predefined keywords include ˝Hi˝ where your chatbot sends a welcome message with menu options. Then, when the customer replies with an option from the menu such as ˝Option 1˝, your chatbot sends the predefined response for the ˝Option 1˝ keyword. If the customer replies ˝Option 2˝ the chatbot sends the predefined response you’ve set up for that keyword.

You can also set up multiple keywords to trigger the same response. In the example below, the menu option tells the customer to reply ˝1˝ for a map, but since they replied ˝map˝ instead, the chatbot knew to send the same response.

Intent-based chatbots have a different approach. Using the AI technology mentioned above, these chatbots can recognize what the customer is looking for based on a phrase rather than a keyword. You can also set up one predefined response for multiple variations of the phrase.

For example, one customer might contact you saying ˝Hi, I want to book an appointment˝ and another might say ˝Hi, when is your next available slot?˝ You can set up ˝book an appointment˝ and ˝next available slot˝ as two phrases that receive ˝Please choose your branch location˝ as an answer.

Although chatbots are a great way to immediately respond to customers who contact you over chat channels, what about the ones who call in? Interactive Voice Response (IVR) technology is a favorable way to automate your customer support for phone calls.

Using IVR lets you set up a welcome message to greet customers with menu options. As the customer chooses their preferred menu options, IVR technology decides on the best agent to route to for that query.

In addition, when you connect Conversations to your IVR, you can enable agents and customers to continue the conversations through chat rather than over the phone. This helps your agents handle more queries at once and keeps customers engaged rather than waiting on hold.

Using the contact center automation examples listed above gives you the flexibility to meet different customer needs in a cost-effective way. Not only do they offload tedious queries from your agents, allowing them to focus on more complex ones, it also gives your customers the immediate support they seek when contacting you over their preferred channels.