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Customer Effort Score (CES)

Definition

Customer Effort Score (CES) measures how easy it is for customers to accomplish a task, such as resolving an issue, making a purchase, or using a feature. Typically, customers are asked to rate their experience on a scale, with lower effort indicating a better experience.

Description

Customer Effort Score (CES) tracks how easy it is for users to complete tasks — like onboarding, purchasing, or resolving issues — making it a key indicator of friction, frustration, and future loyalty.

The relevance and interpretation of this metric shift depending on the model or product:

  • In SaaS, it reflects onboarding simplicity and support UX
  • In CX-heavy brands, it measures checkout ease and ticket resolution
  • In mobile apps, it surfaces UI/UX friction in key workflows

A high CES score = smooth experience. A low score flags process pain, UX confusion, or service breakdowns. Segment by journey stage, issue type, or device to uncover hotspots.

Customer Effort Score informs:

  • Strategic decisions, like automation investments or product simplification
  • Tactical actions, such as self-service enhancements or UI improvements
  • Operational improvements, including support routing and training
  • Cross-functional alignment, by bringing product, support, and CX teams together on experience quality

Key Drivers

These are the main factors that directly impact the metric. Understanding these lets you know what levers you can pull to improve the outcome

  • UI Clarity and Process Complexity: Confusing menus, multi-step workflows, or missing instructions increase perceived effort.
  • Support Accessibility and Channel Options: If help is hard to find or too slow, customers feel frustrated — even if the issue is minor.
  • Task Completion Flow and Feedback Loops: Users want confirmation that they’ve done things right. Lack of clear feedback = uncertainty = effort.

Improvement Tactics & Quick Wins

Actionable ideas to optimize this KPI, from fast, low-effort wins to strategic initiatives that drive measurable impact.

  • If CES is low, identify top-rated “difficult” flows and add in-line help, tooltips, or microcopy.
  • Add in-app feedback prompts after key tasks (e.g., “Was this easy to complete?”) to measure in real time.
  • Run a test simplifying your most friction-heavy task flow by reducing steps or choices.
  • Refine support entry points — make chat/help clearly accessible from all major screens.
  • Partner with UX to ensure consistent success confirmation and feedback at every stage of the journey.

  • Required Datapoints to calculate the metric

    • Sum of All Customer Effort Scores: The total of all individual CES ratings.
    • Total Number of Responses: The total number of customers who answered the CES question.

  • Example to show how the metric is derived

    An e-commerce platform tracks CES for its customer support interactions:

    • Sum of CES Scores: 400
    • Total Responses: 100
    • CES = 400 / 100 = 4.0

Formula

Formula

\[ \mathrm{Customer\ Effort\ Score} = \frac{\mathrm{Sum\ of\ All\ Customer\ Effort\ Scores}}{\mathrm{Total\ Number\ of\ Responses}} \]

Data Model Definition

How this KPI is structured in Cube.js, including its key measures, dimensions, and calculation logic for consistent reporting.

cube(`CustomerEffortScore`, {
  sql: `SELECT * FROM customer_effort_scores`,

  measures: {
    totalEffortScore: {
      sql: `effort_score`,
      type: 'sum',
      title: 'Total Effort Score',
      description: 'Sum of all individual Customer Effort Scores (CES) ratings.'
    },

    totalResponses: {
      sql: `response_id`,
      type: 'count',
      title: 'Total Number of Responses',
      description: 'Total number of customers who answered the CES question.'
    },

    averageEffortScore: {
      sql: `${totalEffortScore} / NULLIF(${totalResponses}, 0)` ,
      type: 'number',
      title: 'Average Effort Score',
      description: 'Average Customer Effort Score (CES) based on total scores and responses.'
    }
  },

  dimensions: {
    id: {
      sql: `id`,
      type: 'number',
      primaryKey: true,
      title: 'ID',
      description: 'Unique identifier for each customer effort score entry.'
    },

    customerId: {
      sql: `customer_id`,
      type: 'number',
      title: 'Customer ID',
      description: 'Unique identifier for the customer.'
    },

    responseDate: {
      sql: `response_date`,
      type: 'time',
      title: 'Response Date',
      description: 'Date when the customer provided the effort score.'
    }
  }
});

Note: This is a reference implementation and should be used as a starting point. You’ll need to adapt it to match your own data model and schema

Positive & Negative Influences

  • Negative influences

    Factors that drive the metric in an undesirable direction, often signaling risk or decline.

    • UI Clarity and Process Complexity: Complex or unclear user interfaces and processes increase the perceived effort required by customers, leading to a lower Customer Effort Score.
    • Support Accessibility and Channel Options: Limited or slow support options frustrate customers, increasing their perceived effort and negatively impacting the Customer Effort Score.
    • Task Completion Flow and Feedback Loops: Lack of clear feedback during task completion creates uncertainty, increasing perceived effort and reducing the Customer Effort Score.
    • Response Time: Long response times from customer service or support channels increase customer frustration and perceived effort, lowering the Customer Effort Score.
    • Error Frequency: Frequent errors or bugs in the system require additional effort from customers to resolve, negatively affecting the Customer Effort Score.

  • Positive influences

    Factors that push the metric in a favorable direction, supporting growth or improvement.

    • UI Clarity and Process Complexity: A clear and intuitive user interface reduces the effort required by customers, improving the Customer Effort Score.
    • Support Accessibility and Channel Options: Easily accessible and responsive support options reduce customer frustration and perceived effort, enhancing the Customer Effort Score.
    • Task Completion Flow and Feedback Loops: Providing clear feedback and confirmation during task completion reduces uncertainty and perceived effort, positively impacting the Customer Effort Score.
    • Self-Service Options: Effective self-service options empower customers to resolve issues independently, reducing perceived effort and improving the Customer Effort Score.
    • Personalization: Personalized experiences that cater to individual customer needs reduce the effort required to accomplish tasks, positively influencing the Customer Effort Score.

Involved Roles & Activities

Funnel Stage & Type

  • AAARRR Funnel Stage

    This KPI is associated with the following stages in the AAARRR (Pirate Metrics) funnel:

    Retention
    Activation

  • Type

    This KPI is classified as a Lagging Indicator. It reflects the results of past actions or behaviors and is used to validate performance or assess the impact of previous strategies.

Supporting Leading & Lagging Metrics

  • Leading

    These leading indicators influence this KPI and act as early signals that forecast future changes in this KPI.

    • Customer Satisfaction Score: Customer Satisfaction Score (CSAT) captures immediate customer sentiment after interactions, providing early warning signals about friction points. A high CSAT often correlates with a lower Customer Effort Score (CES), as satisfied customers typically perceive experiences as easier and less effortful.
    • Drop-Off Rate: Drop-Off Rate measures where users abandon processes, directly indicating friction or complexity in the customer journey. High drop-off rates often precede lower CES, making it a critical input for diagnosing and forecasting effort-related issues.
    • First Contact Resolution: First Contact Resolution reflects the percentage of issues resolved in a single interaction. High FCR reduces customer effort, serving as a proactive signal for improving CES and highlighting support process efficiency.
    • Onboarding Completion Rate: Onboarding Completion Rate indicates how successfully users navigate initial setup. A higher rate suggests less friction and lower perceived effort, directly impacting and contextualizing CES as users begin their journey.
    • Time to First Value: Time to First Value measures how quickly users realize value. Shorter times mean less effort is required from customers to achieve their goals, strongly influencing CES and signaling areas where effort can be minimized.

  • Lagging

    These lagging indicators confirm, quantify, or amplify this KPI and help explain the broader business impact on this KPI after the fact.

    • Average Resolution Time: Average Resolution Time quantifies how long it takes to resolve customer issues. Longer times can result in lower CES, and analyzing this lagging indicator helps recalibrate CES targets and identify process improvements for reduced effort.
    • Customer Churn Rate: Customer Churn Rate measures attrition and often spikes following periods of high customer effort. Post-hoc analysis of churn informs leading indicators like CES, highlighting the downstream business impact of high-effort experiences.
    • Complaints Received: Complaints Received provides detailed insight into pain points that may have required excessive customer effort. Reviewing complaint trends can guide the refinement of CES measurement and trigger targeted interventions.
    • Customer Downgrade Rate: Customer Downgrade Rate tracks the percentage of customers reducing their engagement or spend, often as a result of accumulated negative experiences or high effort. This feedback loop refines CES as a predictive signal for retention risk.
    • Net Revenue Churn: Net Revenue Churn captures the revenue lost due to customer departures and downgrades. When correlated with CES, it enables recalibration of leading effort metrics to better forecast and mitigate future revenue risks.