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Chatbots have steadily grown in popularity to become a key component of customer service today. With an AI chatbot in place, organizations can resolve as much as 91% of chats without involving a human agent. With a bot handling most chats, this means organizations can increase support capacity without growing team size, while also delivering 24/7 availability to their customers. Research shows that 70% of customers are already using or interested in using chatbot software for support.
To understand how effective chatbots can be, there are several chatbot success metrics you need to track. With that in mind, in this article we’ll break down the top chatbot performance metrics that you should pay attention to, starting with the staple chatbot metrics and then moving onto the more technical chatbot metrics.
At their most basic, chatbots can work alongside traditional support teams and reduce workloads for human agents. In this section, we look at chatbot performance metrics that can be used to compare chatbot outcomes to your human agents.
The Total Number of Chats metric allows you to see just how many chats a bot is handling. By comparing this metric against the number of chats resolved by agents, you can easily understand the impact that a chatbot is having on support volumes. Organizations can use this metric to see the direct cost savings from a chatbot, based on how much the bot has reduced agent workloads. When tracked over time, this metric also allows organizations to understand how AI chatbots are developing.
Average Handle Time is a measure of the duration chatbots spend in each interaction. It’s expected that AHT should be shorter for chatbots than for live chat agents. With chatbots handling basic requests, agents will spend more time on complex queries. As AHT for chatbots changes over time, it can be used to measure the efficacy of a chatbot.
Wait Time indicates how long visitors spend waiting for a live chat session to begin. With chatbots handling the majority of requests, you should see wait times decrease. By tracking wait time as one of the key chatbot success metrics, organizations can work to balance the number of chats handled by bot to improve customer experience.
This metric indicates how quickly your chatbot is responding to requests. Unlike human agents, chatbots can respond without delay and with unlimited capacity, meaning that Response Time should be near instantaneous.
Customer Satisfaction Rate, or CSAT, is a measured rating from chatbot interactions. Using survey results, you can measure CSAT for your chatbot against your live agents to find the right balance of chatbot efficiency and human touch.
The First Contact Resolution (FCR) metric indicates how many chats a bot has handled on its own without assistance. Another chatbot success metrics used to measure this handoff is Support Agent Takeover Rate. When comparing First Contact Resolution against Total Number of Chats, you can see how your chatbot performs against your live chat agents.
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Chatbot interactions have become increasingly complex as organizations have leaned on chatbots to do more for customers. In this section, we’ll look at more technical chatbot success metrics that can’t be applied to human agents.
As the name suggests, Disorientation Rate measures how often your chatbot doesn’t understand a question. This also includes how many times a customer was asked to repeat themselves, which research shows 75% of consumers say they hate. Using Disorientation Rate, organizations can measure the efficacy of their chatbot’s decision tree. Measuring poorly here could indicate a need to further develop a chatbot or improve its ease of use.
The Activation Rate metric indicates how much a chatbot is being used by customers. By comparing total users against chatbot engagements, Activation Rate differs from other chatbot metrics in its ability to effectively draw engagements from customers.
This metric measures how many visitors return to a chatbot during a selected time frame. The utility of Retention Rate compared to other chatbot success metrics will vary based on industry and use case. A high retention rate could indicate repeat business or customer preference, but it could also indicate a low First Contact Resolution that requires follow-ups. By combining Retention Rate and CSAT score, you can get a more complete picture of how your chatbot is stacking up.
The Volunteer Users metric looks at how many users interact with your chatbot without any prompting. This can be used to understand how effective efforts to market a chatbot have been, or to measure how often customers recognize the value of a chatbot.
Looking at Number of Interactions Per User allows you to understand how many chats an average user exchanges with a chatbot. High numbers of interactions can indicate a high level of engagement but can also indicate poor service that needs repeated clarification. As with Retention Rate, pairing this metric with CSAT score allows you to understand more about the quality of these interactions.
Chatbots can be configured to have one or more goals, and the Goal Completion Rate metric allows you to understand how successful your chatbot is in meeting these objectives. An example of this in higher education might look at how many visitor engagements result in registrations for a virtual information session. Looking at the most used phrases by a chatbot can help you understand how effective the chatbot is at communicating its goal to your visitors.
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