Customer Effort Score (CES)

What Is Customer Effort Score (CES)?

Customer Effort Score (CES) measures how much effort a customer must exert to get an issue resolved, a question answered, or a task completed. It is typically captured through a single survey question asked immediately after a support interaction: "How easy was it to resolve your issue today?"

CES has become one of the most important loyalty metrics in customer service. Research consistently shows that reducing customer effort is a more reliable path to loyalty than trying to "delight" customers with over-the-top service. Customers do not want to be impressed. They want their problem resolved with as little friction as possible.

How to Calculate Customer Effort Score

CES = Sum of All Survey Responses / Number of Responses

The most common CES survey uses a 1 to 5 or 1 to 7 scale, where 1 means "very difficult" and the highest number means "very easy." Some teams invert the scale to measure effort directly (lower is better), so it is critical to document your scale direction for consistent benchmarking.

For example, if 10 customers rate their effort as 2, 4, 4, 5, 3, 2, 3, 4, 4, and 5 on a 1-5 scale, your CES is 3.6 out of 5.

Alternative calculation methods include:

• Percentage favorable: Calculate the percentage of respondents who selected the top 2 or top 3 options on the scale. This simplifies reporting and makes trends easier to track.
• CES by channel: Segment scores by live chat, email, phone, and self-service to identify where friction exists.

Industry Benchmarks

CES benchmarks depend on your scale, industry, and interaction complexity:

• 1 to 5 scale: A good CES is 4.0 or higher. Scores below 3.5 indicate significant friction in your support experience.
• 1 to 7 scale: Target 5.5 or higher, which corresponds to roughly 80% or more of customers agreeing the experience was easy.
• B2C and e-commerce: Simpler interactions tend to produce higher CES scores because the issues are often straightforward.
• B2B and technical support: Complex troubleshooting, multi-step processes, and longer time to resolution often produce lower CES scores.

Research Insight: Gartner research, based on more than 125,000 customer interactions, found that service interactions are 4x more likely to drive disloyalty than loyalty. Reducing effort, not exceeding expectations, is the most effective strategy for retaining customers.

Why It Matters

CES is one of the strongest predictors of future customer behavior. The data is clear:

• Loyalty: 94% of customers who report a low-effort experience say they intend to repurchase. Among high-effort customers, 96% report increased disloyalty.
• Predictive power: CES is approximately 1.8x more predictive of customer loyalty than CSAT and 2x more predictive than NPS for service interactions.
• Revenue impact: High-effort experiences drive churn. Reducing effort at key touchpoints, especially in support, directly protects recurring revenue.

Every transfer, repeated explanation, channel switch, and follow-up contact increases effort. CES captures that cumulative friction in a single number.

How AI Improves This Metric

AI-powered customer service reduces customer effort at every stage of the support journey:

• Instant answers: AI Agents resolve common questions immediately, eliminating the need to wait in a queue, navigate phone trees, or search through help articles.
• No repeated explanations: AI retains full conversation context across channels, so customers never have to re-explain their issue after a transfer or escalation.
• Proactive resolution: AI can detect patterns and resolve potential issues before the customer even contacts support, which represents the lowest possible effort score.
• Fewer contacts per issue: When AI resolves issues on the first interaction, follow-up contacts drop, directly improving first contact resolution and CES together.

The Maven AGI Advantage

Maven AGI is purpose-built to create low-effort customer experiences. Agent Maven resolves 80-93% of issues autonomously, meaning the customer describes their problem once and gets an answer immediately, with no transfers, no hold times, and no repeated context.

• Enumerate (PropTech): 91% resolution rate, reducing the number of interactions required per issue.
• Roo (Healthcare): 50% reduction in ticket volume through proactive AI resolution.
• K1x (FinTech): 80% resolved, with 10x improvement over their prior AI tool, deployed in just 1 week.

Maven AGI Approach: Every transfer, queue, and repeated explanation adds effort. Maven AGI eliminates those friction points by resolving issues in a single interaction. With 100+ integrations, Agent Maven pulls context from your CRM, billing, and knowledge systems to give customers accurate answers without asking them to do the research themselves.

Frequently Asked Questions

What is a good Customer Effort Score?

On a 1-5 scale, a CES of 4.0 or higher is considered good. On a 1-7 scale, target 5.5 or above. The most important thing is consistency in how you measure, using the same question, scale, and timing, so you can track improvement over time.

How is CES different from CSAT and NPS?

CES measures how easy a specific interaction was. CSAT measures satisfaction with that interaction. NPS measures overall brand loyalty and willingness to recommend. CES is the best predictor of repeat behavior after a service interaction, while NPS better captures long-term brand sentiment.

When should I send a CES survey?

Send the CES survey immediately after the interaction is complete, ideally within the same session or within minutes of ticket resolution. Delayed surveys produce lower response rates and less accurate data because customers forget the details of their experience.

Can AI help improve Customer Effort Score?

Yes. AI reduces effort by resolving issues instantly, retaining context across channels, and eliminating the need for customers to repeat information. According to Harvard Business Review, the single most important factor in customer loyalty is reducing the effort required to get a problem resolved, which is exactly what AI Agents are designed to do.

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