Talk about artificial intelligence customer service indicators

We often receive business inquiries about what are the evaluation criteria for intelligent customer service. This is a very difficult question to answer because we need to justify using smart customer service and ensure how using smart customer service will benefit the business.

Although there are no "real" evaluation criteria for intelligent customer service solutions, here I list some cases, hoping to help everyone and give you some tips from these cases.

First of all, what is intelligent customer service?

Smart customer service is a solution or a set of solutions that allows users to gain access to information and even perform simple tasks autonomously without the help of customer service personnel.

So what are the queries or tasks that intelligent customer service can handle or perform?

Track a package, request a quote, or pay your bill online without contacting customer service are all business tasks we perform on a regular basis.

In terms of customer inquiries, not all inquiries can be handled by intelligent customer service, as some complex issues still require manual intervention. However, smart customer service solutions are very effective at resolving repeat Tier 1 inquiries. These are very common and very frequent request types. More than 80% of users ask these questions, which takes up a lot of resources. We can use automation to answer these questions.

What indicators can quantify intelligent customer service indicators?

When trying to quantify the indicators of intelligent customer service, each company has its own indicator evaluation standards. The following are some commonly used indicators that need to be updated and monitored regularly.

Call Deflection Rate

"Call deflection" refers to routing customer inquiries to alternative service channels, such as chatbots, FAQs, knowledge center databases. The goal of call forwarding is to ensure customers receive the answers they are seeking in the most efficient manner and to reduce the number of calls routed to human agents. This metric refers to "calls," but also includes any other means of communication, such as live chat and email.

Measuring call deflection rate can be complicated because we are trying to measure something that is not happening! According to DB Kay & Associates, one approach is to estimate the percentage of users who are successful with intelligent customer service and the percentage of users who switch to manual service. The difference between these two percentages represents the deflection rate.

Customer Satisfaction

Advancing the use of intelligent customer service channels is an exciting project for any business to improve/enhance the customer experience. However, if customers are dissatisfied with the tools provided to them by smart customer service, if they find it too difficult to use or inefficient, then the smart customer service channel cannot be considered successful. Customer satisfaction for each intelligent agent channel must be tracked through surveys, direct feedback, and Net Promoter Score (NPS) to gain a clear understanding of which channels are most successful and which ones need improvement.

Smart Customer Service Success Rate

A simple way to determine the success of smart customer service is to track how many customer inquiries are handled by the smart customer service channel without being escalated to a human agent. For example, this could be the percentage of times a "how to order" FAQ resulted in an order rather than a customer-initiated chat session, or the percentage of times a knowledge base search resulted in a helpful article, with a user rating indicating the article was "useful" or indicating "this solved the problem" my question".

Many current solutions automatically track, calculate and provide relevant reports, as well as many other useful indicators.

How to Calculate Smart Customer Service Ratio

Let’s first define the percentage of issues that can be resolved by customers themselves using smart customer service channels. As mentioned before, not all inquiries can be handled by intelligent customer service, and more complex inquiries require human intervention. Years of experience in artificial intelligence customer service tell us that this percentage depends largely on business scenarios, industry experience and even APP usage, but usually, 50% of queries can be solved by customers themselves.

Of this 50%, we need to quantify how much is redundant or duplicated. As mentioned earlier, approximately 80% of inquiries received by human agents fall into this category. These are suitable for smart customer service.

The maximum usefulness of smart customer service will be the product of these two percentages, which is 0.5 x 0.8 = 0.4, so 40% will be the maximum smart customer service rate that can be expected.

Finally, you need to consider the efficiency of the artificial intelligence powering your tool. With the right AI, the right content, and a powerful industry knowledge base, your intelligent customer service solution can answer up to 80% of these repeat queries.

So, 32% (0.4 x 0.8 = 0.32) is a good target for a smart customer service ratio.

Of course, these are just examples, and results can vary greatly depending on the business, industry, or type of technology supporting your intelligent customer service solution, which can provide you with a good basis for comparison.

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