Principal Program Manager at Microsoft, driving Strategy, Growth and Success in Conversational AI for Customers and Partners.
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Digital contact centers have been evolving for a while now. A significant component of that evolution is self-service through human language. A technology commonly used to achieve this is called "conversational artificial intelligence (AI)." Understanding conversational AI's role in contact center experiences is essential, specifically when using virtual agents or bots. This understanding is so business leaders and designers can determine where to focus on providing the most value to customers as soon as possible.
What is a contact center experience? We can look at this question broadly, where a contact center experience is a customer (paid or unpaid) who requires information or action and makes that inquiry through multiple channels. Arguably, we can say a complete journey is when that customer reaches a natural person on a track, which could be via an escalation like live chat or phone. A contact center experience can be longer or shorter depending on the customer's actions to get there. Also, considering where the customer's question or action was resolved, there can also be many optional forks in each journey.
Let's take a look at bots. I won't use this article to delve into the semantic differences between bots, agents, etc. Instead, I will use the term "bot" to refer to the conversational AI technology that allows anyone to interact with a company by initiating a conversation using human language, like they would another person, via text or speech.
It is worth noting that a bot is a representation of conversational AI technology. Conversational AI is a broad term, and while it is the technology powering the bot, conversational AI technology can be used outside of a bot as well. Examples include when a customer searches by just typing a question (e.g., using NLU-powered search) or when a transcript is "read" to surface insights for follow-up review and action. This article looks primarily at bots but does take into consideration the latter.
So, where does conversational AI fit into the contact center experience? A standard implementation of bots in a contact center journey is to place one or more at the point in a trip where a customer could begin a line of inquiry. This is often to help the customer change direction from needing to speak to someone straight away to using a bot to answer general questions. This process is known as "deflection."
A bot at the beginning of a journey can do many things. They often serve up information, perform actions and handle the most-asked queries that specific customers have based on their industry and product (obtained through reporting). The bot could also offer relevant articles to the user to help answer their question immediately. The customer gets what they need conveniently and quickly, and this doesn't cost the contact center as much as it would if they had to speak to a human agent.
The bonus here is that the scale is only constrained by the limitations of the technology, allowing for an almost unlimited potential to scale, which helps manage seasonality-type spikes. Another common thing for a bot to do is to create a support case or perform some action with data, especially if the bot is authenticated with the customer it is speaking with; they can have access to be able to write or even read data.
Other types of journeys involve a bot in the middle or near the end of a contact center experience and when agents hand the customer back to a bot. This sounds revolutionary, but it has been happening for decades. Have you ever had a phone call that asked you to stay on the line for a survey? That is technically a bot. More recently, though, bots are much more intelligent. Agents can hand you back to a more interactive and helpful bot for scenarios like walking through a set of steps or reading out knowledge articles.
The role of conversational AI doesn't end after a call. Conversational AI technology could have already taken the completed transcript, summarized it and made notes on behalf of the agent, helping with training and triggering automation to suggest new training to the agents. Another useful role it can play—and arguably the most valuable—is through offering suggestions after contact center experiences, so the bot supervisors and admins could be suggested new topics based on live production data and trends they are seeing from customers' questions. This can lead to improvements and improved support through a feedback loop between the technology and the bot supervisors.
In this article, we considered what role conversational AI technology plays in a contact center, summarizing both primary and secondary support roles. Primary roles are where this technology is front and center, such as when it is being used in an interactive voice response (IVR) or a text bot at the start of a journey. Secondary roles go a little bit more unnoticed, being used more "silently," such as when searching for a knowledge article, reviewing a transcript and surfacing critical topics in that transcript for agents and supervisors. Consider how you can use this technology in your contact center journey to make both significant and minor changes to improve your customer experiences.
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