Three Ways AI and Machine Learning Is Improving Live Chat



By Dan Somers

Many companies are implementing live chat because it offers a better experience for some queries and with some customers. It also offers cost savings for companies compared to voice. Indeed, the channel has been growing 87 percent per year, according to CustomerThink.

BoldChat found that the top reasons given for why people prefer live chat are immediacy of responses, 71 percent; ability to multitask, 51 percent; and don’t like talking on the phone, 22 percent.

However, canned responses, complex queries, or poor staffing can lead to the opposite experience. This results in channel switching, repeat calls, abandonment, or even churn. Misunderstandings can happen more frequently than during a telephone conversation, and with both customers and agents multitasking, there is plenty of room for error. Offshore chat operations often compound these concerns with cultural issues and additional misunderstandings.

However, new techniques in AI and machine learning make the analysis of live chat both easy and immediately actionable. Here are three ways these tools can transform chat optimization:

New techniques in AI and machine learning make the analysis of live chat both easy and immediately actionable. Click To Tweet

1. Human in the Loop AI

The technology runs automatically in the background until it needs a nontechnical person to assist with tuning the models in a rapid and efficient way. It prompts for a human only when needed. This frees up agent resources and maintains a current, fine-tuned, and accurate model.

2. Automatic Identification of Sentiment and Intent 

Models can automatically tag the chats with customer intent, sentiment, and emotion, such as if they’re considering leaving or expressing some other actionable emotion. This frees agents from several seconds of manual work (that is, after call work), where they can only typically do one tag at a time even if there are multiple issues to address.

3. Automated Next Best Action 

Use these models to drive insight specific to the customer in the moment through the automation of next best actions, enhancing the overall customer experience. They can plug natively into chat software APIs to automatically classify tags tuned to the specific requirements of the business.

Chat provides many benefits to both businesses and customers. Take these three steps to optimize your chat services for even greater results.

Dan Somers is the CEO of Warwick Analytics, developers of PrediCX, a machine learning platform that generates automated and customizable models specific to a particular chat stream.