What is Customer Effort Score (CES)? Examples & Implementation

Customer effort score (ces)

CES measures how much effort customers exert interacting with your product, helping teams reduce friction and improve experience.

Understanding Customer Effort Score

• Definition of ces

  Customer Effort Score (CES) quantifies the amount of effort a customer must expend to accomplish a specific task, such as resolving an issue or completing a purchase.

• Origins and purpose

  Introduced by CEB (now Gartner), CES is designed to predict customer loyalty and identify friction in the customer journey. It emphasizes reducing effort to boost retention.

Importance of CES in Analytics

• Reducing customer friction

  High CES indicates points of friction where customers struggle, allowing teams to streamline processes and improve user flows.

• Impact on loyalty and retention

  Lower effort correlates with higher satisfaction, increasing the likelihood of repeat business and positive word-of-mouth.

Implementing CES Surveys

• Survey design best practices

  Craft a clear, concise question and choose an appropriate scale to ensure reliable responses.

  • Question format:

    Use a single-question survey like ‘How much effort did you personally have to exert to handle your request?’

  • Scale selection:

    Common scales include 1–5 or 1–7, where lower values indicate less effort and higher values indicate more effort.

• Using plainsignal

  PlainSignal provides a cookie-free, lightweight way to embed CES surveys and view results in a simple dashboard.

  • Tracking code:

    <link rel="preconnect" href="//eu.plainsignal.com/" crossorigin />
    <script defer data-do="yourwebsitedomain.com" data-id="0GQV1xmtzQQ" data-api="//eu.plainsignal.com" src="//cdn.plainsignal.com/PlainSignal-min.js"></script>
    

• Using google analytics 4

  Leverage GA4’s custom events to collect CES responses alongside other user interactions.

  • Ga4 event setup:

    Create a custom event named ces_response with a parameter ces_score to capture the response value.

  • Example code:

    gtag('event', 'ces_response', { 'ces_score': score, 'survey_id': 'ces_s1' });
    

Analyzing and Acting on CES Data

• Calculating ces

  Compute CES as an average score or a percentage of low-effort responses to understand overall ease of use.

  • Average score method:

    Sum all CES scores and divide by the number of responses to get the mean effort level.

  • Low-effort percentage:

    Calculate the percentage of responses that fall within the ‘low effort’ range (e.g., 1–2 on a 1–5 scale).

• Interpreting results

  Benchmark CES against industry standards and identify specific touchpoints that drive high effort.

  • Benchmark values:

    Aim for average CES scores below 3 on a 1–5 scale; higher scores signal friction issues.

  • Identifying pain points:

    Correlate high-effort responses with customer journey data to pinpoint problematic steps.

Best Practices and Common Pitfalls

• Best practices

  Follow these proven strategies to ensure your CES program yields accurate, actionable data.

  • Optimal timing:

    Trigger CES surveys immediately after task completion or support interactions for contextually relevant feedback.

  • Clear wording:

    Keep the phrasing simple and focused on effort rather than satisfaction or emotion.

  • Adequate sample size:

    Collect enough responses to achieve statistical significance without causing survey fatigue.

• Common pitfalls

  Watch out for these mistakes that can undermine the reliability of your CES metric.

  • Survey bias:

    Avoid leading questions and ensure a representative sample to reduce bias.

  • Low response rate:

    Use unobtrusive prompts and reminders to improve participation without annoying users.

  • Overemphasis on score alone:

    Combine CES with qualitative feedback and other metrics like CSAT and NPS for a holistic view.

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