Customer Effort Score (CES) (Customers & Products KPI): Formula and Example

Business context

Why Customer Effort Score matters

Movement is meaningful only when scale direction is clear. On an ease scale, a higher average can mean easier; on an effort scale, a lower average can mean easier.

Business question: How easy did customers find this task or interaction?
Teams that use it: Customer experience, product, support, design, and operations teams.
Decisions it supports: Journey simplification, self-service design, workflow improvements, support processes, and issue prioritisation.

Calculation

Customer Effort Score formula

Survey metric based on a scale (e.g., 1–5).

Formula components

Survey response: A valid rating submitted for the specific task or interaction.
Scale: The response range, such as 1–5 or 1–7, and the label attached to every endpoint.
Direction: Whether higher values represent less effort or more effort.
Summary method: The agreed average score or percentage of low-effort responses.

How to calculate Customer Effort Score

Choose a clear effort or ease question tied to one journey moment.
Document the scale, endpoint labels, direction, and summary method.
Collect valid responses consistently after the interaction.
For an average score, sum ratings and divide by responses; retain the response distribution.

Worked example

Customer Effort Score example

A team asks customers to rate the statement “Completing this task was easy” from 1 (strongly disagree) to 5 (strongly agree). It receives 420 responses totalling 1,680 points.

Total response points = 1,680.
Valid responses = 420.
Average CES = 1,680 ÷ 420 = 4.0 out of 5.

Customer Effort Score is 4.0 on a 1–5 ease scale.

Respondents generally agreed that the task was easy. Because higher means easier in this survey, an increase would indicate lower perceived effort.

How to interpret the result

Always display the question, scale, direction, and response count near the score. Compare the same journey moment and segment, and use comments or behavioural data to identify friction.

Benchmarks vary by question wording, scale, channel, culture, task complexity, and collection timing. Scores on opposite scale directions cannot be compared without transformation.

Common mistakes and limitations

Ambiguous scale direction: Readers may assume higher is better when the survey asks about effort rather than ease.
Combining different tasks: Password reset and enterprise implementation are not comparable experiences.
Response bias: People who answer may differ from the full customer population.
Changing wording or scale: Even small survey changes can break the trend.