We recently discussed why measuring customer effort is important and how it’s different from surveying customer effort. This post introduces a five-step framework to help you think through how to measure customer effort using organic customer feedback and AI. The Customer Effort Score (“CES”) survey can provide solid directional cues about how customers perceive effort. But to improve effort, you need to measure what’s really happening — beyond how your customers feel.

Customers’ effort perception can be complex. Sometimes, customers perceive effort as high for a task that they felt should have been simpler, even if it only took a few minutes — for example, resetting a password. Conversely, customers can perceive effort as low for a task they view as complex, or high-value, even if it took hours of back and forth — for example, customizing a report template. While it’s important to understand how customers feel, it won’t necessarily help you improve your customer experience. So how do you make sense of customer effort?

Understanding how much time it takes for customers to accomplish tasks and resolve issues in objective terms unlocks clarity that allows you to identify and remove friction from your customer experience easily. Measuring effort based on time, interaction quantity, and customer language helps validate perception with reality so that you can effectively prioritize simplifying parts of your customer experience.

Measuring customer effort using organic customer feedback and AI provides two critical advantages.

1. It’s objective. Natural Language Understanding, coupled with analysis of timing patterns, measures how much time customers spend on what kinds of activities, when, and why.
2. It tells you what’s happening _right now_ , not what’s already happened. This way, you can intervene appropriately to help reduce effort while it’s still in flight.

Here’s our five-step framework for measuring customer effort with AI.

1. Use your organic customer feedback to generate a customer effort baseline.

While you may have a baseline from your CES about how customers feel, it’s important to get a baseline of how much time customers actually spend in pursuit of different tasks. Using Natural Language Understanding (“NLU”) technology to analyze your customer interactions can generate a baseline of how much effort your customers expend to solve various issues. Coupled with communication pattern analysis, NLU helps you rank issues across your customer experience by the amount of customer effort that they cause. And it’s a true baseline because it reflects all customer interactions, not just the ones where customers answered your survey.

Your baseline helps you understand what’s typical and what great looks like — for your business. Depending on the type of business you are and the types of issues your customers experience, an interaction that required 45 minutes of customer effort might be a regrettable outlier — or it might be the gold standard! Different companies will have different baselines for different issue types — it’s important to identify yours so you can set the right goals.

2. Prioritize areas to improve.

Now that you know your baseline, it’s easy to spot outliers. Where and when are you seeing pockets of higher customer effort? Do some product issues take way longer to solve than others? What do customers find confusing? Are these issues known and well-understood? Are they unknown and require some time to investigate?

While CES surveys can help identify common sources of customer frustration, they will only reflect what customers who answered the survey experienced. Using NLU can illuminate sources of friction in your customer experience that may not yet be on your radar.

3. Build the business case.

Now that you know what’s driving customer effort, it’s time to implement solutions that reduce it. But improving customer experience can take a village. Enter the business case. Now is the time to tell a clear, data-driven story about why the target improvement area you’ve identified is urgent and important.

Since you’re using AI to measure customer effort, it should be easy to find out the following — how many customers does the issue affect? How valuable are those relationships to your organization? What’s the relative importance of addressing this issue? What happens if we do nothing?

A business case that answers these questions is tough to argue with. The best AI-powered CX insights tools will allow you to rank different customer effort sources by their impact on your customer relationships.

4. Take action.

Time to take action. Especially if the solution is a big cross-functional project, action might not happen immediately — and when it does, you probably won’t see a massive reduction in effort overnight. In the meantime, there are small things you can do that can have an outsized positive impact on customer experience, one interaction or one account at a time. Let’s say you’re building a new feature to make things way easier on your customers.

While the build is underway, you can flag customer interactions that would benefit from the new functionality. You could prevent escalations by training customer-facing teams on a workaround. You could trigger an alert to the relationship owner, who can send a friendly note to let the customer know that you’ve listened to their feedback and have prioritized the new feature.

5. Measure the impact of your actions.

Because you’re using real-time data, it’s easy to see if the actions you’ve taken are working as you’d expected or whether you need to adjust your course.

Your CX insights tooling should make it easy to track progress toward goals. Over time, you should see that customers mention the issue you’ve addressed less often — and when it does come up again, it’s less of a burden on your customers.