Customer Effort Score

Customer Effort Score (CES) measures how much effort customers must exert to complete a task, resolve an issue, or get their questions answered when interacting with your business. Typically measured on a scale from 1 to 7, where 1 represents “very low effort” and 7 represents “very high effort,” this metric captures the customer’s perception of how easy or difficult their experience was. Understanding how to calculate customer effort score and applying the customer effort score formula helps businesses identify friction points that could drive customers away.

A low Customer Effort Score indicates that customers can easily accomplish their goals with minimal friction, which strongly correlates with higher customer loyalty and retention. Conversely, a high score signals that customers are struggling with complex processes, poor self-service options, or inefficient support interactions. Research consistently shows that reducing customer effort is one of the most effective ways to increase customer satisfaction and decrease churn rates.

Customer Effort Score works closely with other experience metrics like Customer Satisfaction Score and Self-Service Success Rate. It’s particularly valuable when analyzed alongside Repeat Contact Rate and Conversation Resolution Rate, as these metrics together reveal whether your support processes are truly resolving issues efficiently. Learning how to measure customer effort score effectively enables teams to prioritize improvements that have the greatest impact on customer loyalty.

Customer Effort Score is calculated by surveying customers immediately after an interaction and asking them to rate the effort required on a scale, then averaging those responses.

Formula:
Customer Effort Score = (Sum of all effort ratings) / (Total number of responses) × 100

The numerator represents the total of all individual effort ratings collected from your survey responses. These ratings typically come from a 7-point scale where customers rate their agreement with statements like “The company made it easy for me to handle my issue” (1 = strongly disagree, 7 = strongly agree).

The denominator is the total number of survey responses you received during your measurement period. This data comes directly from your survey platform or customer feedback system.

Worked Example

Let’s say you collected CES survey responses over one month:

• 50 customers rated their effort level
• Individual ratings: 25 responses of “6”, 15 responses of “5”, 7 responses of “4”, and 3 responses of “3”
• Sum of ratings: (25 × 6) + (15 × 5) + (7 × 4) + (3 × 3) = 150 + 75 + 28 + 9 = 262

Calculation:
Customer Effort Score = 262 ÷ 50 = 5.24

This score of 5.24 out of 7 indicates customers find interactions moderately easy, with room for improvement.

Variants

5-point vs 7-point scales: Some organizations use 5-point scales for simplicity, while others prefer 7-point scales for more granular feedback. Choose based on your survey strategy and customer preferences.

Percentage-based CES: Calculate the percentage of customers who gave high effort scores (typically 6-7 on a 7-point scale). This variant focuses on the proportion of “low-effort” experiences rather than the average.

Channel-specific CES: Measure effort separately for different channels (phone, chat, email, self-service) to identify which interactions require optimization.

Common Mistakes

Survey timing errors: Sending surveys too long after the interaction reduces response accuracy. Deploy surveys immediately or within 24 hours of the customer interaction.

Including irrelevant responses: Don’t include partial responses or surveys from customers who didn’t actually complete the interaction you’re measuring.

Mixing interaction types: Avoid combining effort scores from different types of interactions (billing inquiries vs technical support) as they require different effort levels naturally.

While it’s natural to want benchmarks for Customer Effort Score, context matters significantly more than hitting a specific number. These benchmarks should guide your thinking and help you understand where you stand relative to peers, but they shouldn’t become rigid targets that ignore your unique business circumstances.

Customer Effort Score Benchmarks

Source: Industry estimates based on customer experience research

Understanding Benchmark Context

Benchmarks provide a useful reference point to identify when something might be significantly off track. However, Customer Effort Score exists in tension with other metrics, and optimizing it in isolation can create unintended consequences. A lower effort score might come at the expense of thorough problem resolution, while reducing friction could mean missing opportunities to gather valuable customer feedback.

Related Metrics Interaction

Consider how Customer Effort Score interacts with related metrics like Customer Satisfaction Score and Repeat Contact Rate. If you aggressively reduce effort by streamlining support interactions, you might see your CES improve but your repeat contact rate increase if issues aren’t fully resolved. Similarly, implementing more self-service options could lower effort for simple requests while potentially increasing effort for complex issues that now require customers to navigate multiple channels before reaching human support.

A high Customer Effort Score signals that customers are struggling to accomplish their goals with your product or service. Here’s how to diagnose what’s driving elevated effort levels:

Complex or confusing processes
Look for patterns in customer feedback mentioning “difficult,” “complicated,” or “confusing.” High abandonment rates at specific steps in your customer journey, combined with increased support ticket volume, indicate process friction. Your Conversation Resolution Rate may also decline as agents spend more time explaining convoluted workflows.

Inadequate self-service resources
Monitor your Help Center Article Views alongside your Self-Service Success Rate. If customers are viewing many articles but not finding solutions, or if the same questions repeatedly generate support tickets, your knowledge base isn’t meeting their needs. This forces customers into high-effort channels like phone or chat.

Poor system performance or technical issues
Slow page load times, frequent errors, or system downtime directly increase customer effort. Watch for correlations between technical performance metrics and CES spikes. Customers forced to retry actions or switch between multiple systems will report higher effort scores.

Ineffective support interactions
A rising Repeat Contact Rate paired with high CES indicates customers aren’t getting complete resolutions. They’re forced to contact you multiple times for the same issue, exponentially increasing their perceived effort. This often stems from inadequate agent training or fragmented support processes.

Misaligned channel expectations
When customers choose a channel expecting quick resolution but face lengthy processes, effort perception skyrockets. Analyze CES by channel—if certain touchpoints consistently score higher, investigate whether customer expectations match the channel’s actual capabilities and resolution times.

Streamline your most common customer journeys
Identify the top 3-5 tasks customers perform most frequently and map every step required to complete them. Remove unnecessary clicks, form fields, and approval processes. Use cohort analysis to compare effort scores before and after streamlining — you should see measurable improvement within 2-3 survey cycles. A/B test simplified flows against existing ones to validate that reduced steps actually lower perceived effort.

Eliminate knowledge gaps with proactive information
Deploy contextual help, tooltips, and progress indicators exactly where customers get stuck most often. Analyze your Help Center Article Views data to identify the most-searched topics, then surface that information directly in your product interface. Track both CES improvements and reduced support ticket volume to measure success.

Optimize your self-service capabilities
Audit your FAQ, knowledge base, and automated support tools by tracking Self-Service Success Rate alongside effort scores. Customers who can’t find answers independently will rate their experience as high-effort. Implement search functionality, categorize content logically, and ensure your most critical information is easily discoverable. Monitor Repeat Contact Rate to confirm customers aren’t cycling through multiple failed attempts.

Reduce response times and improve first-contact resolution
Long wait times and multiple interactions dramatically increase perceived effort. Focus on improving Conversation Resolution Rate by equipping support agents with better tools and training. Use cohort analysis to compare CES scores for single-contact resolutions versus multi-contact cases — the difference will highlight the effort impact of inefficient support processes.

Leverage your existing data to identify improvement opportunities
Don’t guess where effort friction occurs. Explore Customer Effort Score using your Intercom data or Explore Customer Effort Score using your Pylon data to segment responses by customer type, interaction channel, and specific touchpoints. This reveals exactly which experiences need the most attention.

Stop calculating Customer Effort Score in spreadsheets and start getting actionable insights in seconds. Connect your data source and ask Count to automatically calculate, segment, and diagnose your Customer Effort Score with AI-powered analytics that reveal exactly why customers are struggling and how to fix it.