HomeArtificial IntelligenceIs It Worth Investing In AI For Self-Service?

Is It Worth Investing In AI For Self-Service?

AI for self-service: The concern to justify investments in technology used for self-service is legitimate, guaranteeing the return or savings generated for the business.

However, there is no standard rule, but some indicators that show the results of these solutions are based on Artificial Intelligence approaches.

Potential customers often ask us about the benchmark for  ROI with self-services. This is a legitimate question as organizations need to justify the expenses and ensure that all investments bring a positive return to the business.

Even though there is no standard benchmark for self-service due to considerable differences in quality and types of solutions, here are some indications of the results your company can realistically expect.

But First, What Is Customer Self-Service?

Self-service is a solution or a set of keys that allow the user to access information or even perform some simple tasks autonomously, without the need for assistance from a human representative to serve him.

What queries or tasks can be handled or performed by customer self-service? Tracking an order, requesting a quote, or paying a bill online without contacting an employee for help are automated tasks that can be performed regularly.

Not all customer queries can be handled by artificial intelligence, as some complex issues still require human intervention. However, automated solutions are very efficient in the result of repetitive level 1 surveys, as they represent on average 80% of the questions received by customer service agents, take up a large part of their time, and can be easily romanticized.

What Metrics To Measure Self-Service?

Every organization must discover the best parameters to use when measuring the performance of self-service tools. However, there are some key metrics that a business should monitor regularly.

Call Diversion Rate

“Call Diversion” refers to forwarding a customer’s inquiry to an automated service channel, such as chatbots, FAQs, community forums, or Help Center databases. The goal is to ensure that customers receive the answers they are looking for most efficiently and reduce the number of requests routed to human agents.

Measuring the rate of deviations can be tricky, as we’re trying to gauge what didn’t happen. According to DB Kay & Associates, one method is to estimate the percentage of users who were successful with self-service and the percentage of users who would have contacted an agent. The difference between these two percentages represents the deflection rate.

Customer Satisfaction

Implementing self-service channels is an exciting project for any organization, aiming to improve the user experience. However, your strategy cannot be considered a success if they are not satisfied with the tools your company makes available or find them too difficult to use or inefficient. Customer satisfaction must be tracked for each service channel through surveys, direct feedback, and Net Promoter Score (NPS) to understand which processes are most successful and which need improvement.

Self-Service Success Rate

An easy way to determine the success of self-service can be to track how many queries are handled through automated channels without being routed to a human agent. This could be, for example, the percentage of times a FAQ leads to a result rather than a user-initiated chat session or the rate of times a knowledge base search leads to a helpful article, as indicated by your view or rating. From users as “this solved my problem.”

How To Calculate Self-Service Potential

Let’s start by defining the percentage of issues that can be resolved by customers themselves using self-service channels. Not all queries can be handled by artificial intelligence tools, as the most complex ones require human intervention. We’ve observed over the years that this percentage depends heavily on the use case, the organization, and even the company’s legacy systems, but typically 50% of queries can be resolved automatically by users.

Also Read: A Dive Into AI, Machine Learning, And Deep Learning

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