CES Benchmarks: What Is a Good Customer Effort Score?

Because standard CES is an average, it sits on whatever scale your question uses. On Surveys.expert that is 1–7 from Very difficult to Very easy, so a higher average means customers found the interaction easier — less effort. Before reading any range, confirm the scale and direction, because they change everything.

Treat these as rough guides, not hard grades. On a 1–5 scale the equivalent bands shift down proportionally, which is exactly why you must always report the scale with the number. Calculate yours from your responses with the CES calculator.

As a general rule on our 1–7 scale, a score above 5 means most customers found the interaction easy and above 6 is excellent, while anything below 4 signals that effort is a real problem. But 'good' is always relative to three things: the touchpoint you measured, your industry, and your own past scores.

Unlike a relationship metric, CES is almost always tied to a specific moment — and different moments carry very different natural effort levels:

• Support resolution tends to score lower, because customers only reach out when something has already gone wrong.
• Checkout usually scores higher, because it is a designed, optimized flow that most people complete without help.
• Returns and cancellations often score lowest, since they involve extra steps, approvals and waiting.
• Self-service and onboarding vary widely with the quality of your documentation and product design.

Because of this, comparing a support CES to a checkout CES tells you little. Compare each touchpoint to itself over time, and treat a low-scoring touchpoint as a place to investigate friction rather than a failing grade.

Two companies can measure CES honestly and get very different scores for reasons that have nothing to do with how well either performs. For any comparison to be valid, the method has to match:

• Scale and direction — a 1–5 'effort' scale and a 1–7 'easy' scale are not comparable, and they run in opposite directions.
• Timing — asking immediately after a smooth interaction produces a different score than a delayed survey.
• Channel — in-app, email and post-chat surveys reach different customers and different moods.
• Category expectations — customers in some industries tolerate more friction than others, shifting the whole baseline.
• Who answers — surveying everyone versus only those who contacted support changes the mix of effort experiences.

The most useful benchmark you have is your own history. A rising CES measured consistently means the changes you made are reducing effort; a falling one is an early warning — regardless of how you compare to an outside figure of uncertain provenance.

• Track each touchpoint on a fixed cadence so movements are real, not seasonal noise.
• Freeze the wording, scale, direction, timing and audience so a change in the score reflects real effort, not a method change.
• Segment the trend by plan, region and channel — an overall flat score can hide one journey improving and another degrading.
• Pair every score with the open-text 'why' so a change in the trend comes with an explanation you can act on.

To move the trend in the right direction, see how to improve Customer Effort Score and reducing customer effort.

A benchmark is only useful if it changes what you do. Use your score range to set a realistic target for each touchpoint, then focus on the reasons behind the number rather than the number itself.

AI makes this practical at volume: it themes hundreds of open-text comments, flags sentiment and surfaces the specific frictions dragging a score down — so you spend your time removing effort, not tallying averages. Describe your goal in AI Studio to build a CES survey with the follow-ups that make benchmarking meaningful, or start from the CES survey template.