According to CMP Research, 30% of customers start their support journeys in self-service, but only 25% of cases get resolved by self-service. As a result, most support users — even if they start with a self-service solution like a chatbot — end up with your live support team anyway to solve their problems. By trying to solve ticket reduction, many simplistic chatbots tend to focus on deflection rates instead of solving actual customer issues. In this post, we will address the differences between deflection vs. resolution rate, explain why focusing on the resolution rate is important, and unpack some common pitfalls of basic support chatbots.
Overview
Before we get into the details, here’s a video briefly overviewing deflection vs. resolution rates. Watch it to learn how your team can focus on metrics that really count.
What is Deflection Rate?
Deflection rate is the percentage of support requests addressed by self-service or self-help tools that agents would otherwise service. In other words, it refers to the number of tickets your team avoids dealing with due to automation.
For example, if 23 out of your 100 tickets are intercepted by a chatbot and avoid being sent to your support team, the deflection rate would be 23%.
The Problem with Deflection Rate
While deflection rate helps measure tickets not sent to your agents, it ignores the customer experience side of customer support: resolved customer issues. In our example above, 23 tickets deflected does not mean 23 fixed customer issues.
Where do these 23 deflected tickets go? Good question. These automated tickets can end up in several different states. Ideally, the requests get resolved, and the customer is happy. However, this is seldom the case. Typically, these tickets get neglected because they time out, the chatbot fails to provide adequate information, or the user becomes frustrated and ends the chat session. All of these negative user experiences are also consolidated into the deflection rate.
Effective self-service automation needs to reduce the volume of issues your team handles and solve customer problems. Companies need to measure both to get a skewed sense of the effectiveness of their self-service support tools, and customers continue to be dissatisfied. That’s where the resolution rate metric comes in.
What is Resolution Rate?
Resolution rate measures the percentage of support requests fully resolved by self-service or support automation. In other words, the resolution rate reflects your ability to solve your customers' problems.
Referring to the previous example, if 23 out of 100 tickets avoid escalation to your customer support team, but only 18 get resolved, the deflection rate is still 23%; however, the resolution rate is 18%. When considering these tickets, your goal should be to close the loop and resolve your customers’ issues, not deflect them. That’s why the resolution rate is so important — it indicates your support loop is closed and your user’s problems are resolved.
Why You Should Measure Resolution Rate
Unlike the deflection rate, the resolution rate incorporates the customer perspective in the metric. Customer-centric support organizations recognize that incorporating a customer perspective is vital in measuring the success of your support automation, just as it is when measuring overall customer support. Ultimately, in the comparison of deflection rate vs. resolution rate, the resolution rate provides a better metric for understanding when your ticket loops are closed and offers accurate insights into your overall customer satisfaction.
Why Simple Chatbots Stress Deflection Rate Rather Than Resolution Rate
The basic chatbots we’ve grown to expect as the first step of troubleshooting are great at handling requests with distinct, one-directional logic paths. That means that the chatbot can process information with clear and definitive information. There is no ambiguity or room for interpretation. Think “yes or no” questions or ones with obvious answers, such as “Is the light red or green?”
Below is an example of a simple one-directional logic flow that portrays a common conversation users experience when asking for help from a chatbot.
You can see the support path only flows in one direction, and each step needs to have a distinct and clear answer. That works well for more general cases, but there is often more nuance when it comes to product support.
Simple chatbots typically use deflection as a mechanism to deflect users from the customer support team but do not fully solve the user’s problem. As a result, customers will continue to have issues with their products. They will either a) restart the troubleshooting process more frustrated than before, potentially taking this out on your support team, or b) give up and move on to a competitor's product. Both cost your business money and ultimately affect your company’s reputation.
Neither are the outcomes that you want. However, the chatbot provider will count this as a success when measuring the deflection rate simply because the initial request wasn’t sent to your support team.
Simple chatbots don't want to highlight when their product doesn't meet expectations; they’re incentivized to show off a higher deflection rate because it increases their perceived value. If you don't care about outcomes, having more automated requests is better and leads to less time, effort, and money you have to spend on human support.
Measure Metrics That Count
Most chatbots we interact with aren't very good at solving problems and try to dazzle you with deflection rates. The delta between resolutions and deflections can be high, especially for complex requests like physical product and device support. Measuring the resolution rate clarifies what really matters for the business: how often support automation handles requests correctly.
To help your business and customers, focus on resolution rates instead. Unlike a basic chatbot, Mavenoid is an Intelligent Support Platform specifically designed for products and devices. Its purpose-built technology delivers best-in-class customer support with intelligent troubleshooting and personalized remote support that delivers resolution rates of 58% and up.
Learn how Mavenoid’s Virtual Assistant Improves Your Resolution Rates.
