Customer Effort Score Survey Template
Measure how hard customers had to work to get help — the CES 1–7 agreement item, with a follow-up that appears only when the effort was high.
One question about how easy (or maddening) it was to get your issue handled. Thirty seconds — and if it was hard, we want the gory details.
The counterintuitive finding that built Customer Effort Score: reducing effort beats exceeding expectations. Customers rarely leave because support failed to delight them — they leave because getting help was work: repeating the story to three agents, waiting on transfers, decoding a help article that almost applied. CES measures exactly that burden, and it predicts repurchase and loyalty better than satisfaction scores in service contexts.
The instrument, kept standard. The core item is the canonical CES 2.0 phrasing — "the company made it easy to handle my issue" — rated on 1–7 agreement. Keep that wording and scale intact: the published benchmarks and the predictive validity belong to this exact formulation. Your CES is simply the mean of the 1–7 answers; track it monthly and after every support-process change.
Effort without a cause is just a mood. The two context questions — what the contact was about, and through which channel — turn the score into a diagnosis. Split CES by channel in the CSV export and you will usually find the story: chat scoring 6.1 while email drags at 3.9 is an SLA problem wearing a survey costume. The resolution question guards against a subtle trap — an easy "no" scores high on effort while still losing the customer, so effort and resolution must be read together.
The follow-up that respects the happy path. A conditional-logic rule watches the score: sixes and sevens finish in three clicks, while answers of three or below reveal one extra question asking what made it hard. That asymmetry is deliberate — high-effort customers want to vent, and the friction stories they type are process-fix instructions written by the people who paid for the failure.
What we left out. NPS (relationship-level, different cadence), agent star-ratings (they personalize what is usually a process failure), and identity fields — though pairing responses with tickets is easy if you add a ticket-number short-text block.
Who runs this. Support leads embedding it in ticket-close messages, e-commerce teams after returns and cancellations, IT helpdesks measuring internal friction, and ops teams watching whether the new help center actually reduced work.
Make it yours. Swap the topic options for your real contact drivers, send within an hour of ticket close while the effort is remembered, and set duplicate prevention so one ticket yields one score. Wire a webhook to flag any 1–3 score into your support channel the moment it lands — a same-day callback after a high-effort experience is the cheapest loyalty save in the business.
Frequently asked questions
How is the Customer Effort Score calculated?
Average the 1–7 agreement answers — that is your CES. The Summary view shows the distribution; the CSV export lets you split the average by channel or contact reason.
Why do only unhappy respondents see the extra question?
A logic rule reveals the friction question when the score is 3 or lower. Easy experiences finish in seconds, and high-effort customers get room to explain — asymmetry by design.
When should CES be sent?
Within the hour after a ticket closes or a return completes — effort fades from memory fast. Link it from your ticket-close message; no respondent login is needed.
Can high-effort responses alert my team immediately?
Yes — connect a webhook and filter on low scores in your receiving tool; deliveries are signed and retried. Email notifications work for lower volumes.