Customer Satisfaction Score (CSAT)¶

Definition¶

Customer Satisfaction Score (CSAT) measures how satisfied customers are with a specific product, service, or interaction. It is typically calculated by asking customers to rate their experience on a scale, such as 1–5 or 1–10, with higher scores indicating greater satisfaction.

Description¶

Customer Satisfaction Score (CSAT) is your frontline metric for capturing real-time sentiment after key touchpoints—support, purchases, onboarding, or feature use. It’s typically measured via a quick survey: “How satisfied were you with [X]?” (on a scale of 1–5 or 1–10).

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

In support-led models, it reflects team responsiveness and resolution success
In product experiences, it reveals delight—or friction—after feature use
In services or onboarding, it tracks expectation alignment and delivery quality

A high CSAT score reflects positive micro-moments, while a drop can indicate pain points that escalate into churn if left unaddressed. Segmenting CSAT by touchpoint, team, or persona unlocks where to double down and where to fix fast.

CSAT informs:

Strategic decisions, like improving key interactions across the lifecycle
Tactical actions, such as triggering follow-up based on low scores
Operational improvements, including team training, ticket routing, or product UX
Cross-functional alignment, by linking support, product, and CX on shared quality benchmarks

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

Resolution Quality and Speed: Fast, effective help = happy customers. Delayed or incomplete responses drive lower scores.
Tone and Empathy in Communication: Even when the answer isn’t ideal, a helpful and human tone boosts CSAT. Robotic responses lose points.
Clarity of Outcome and Next Steps: Confused customers don’t rate well — they need to know what happened and what to expect next.

Improvement Tactics & Quick Wins¶

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

If CSAT is trending down, review recent low-score tickets for tone, timing, and resolution themes.
Add “Was this helpful?” or CSAT popups immediately post-interaction, while context is fresh.
Run a test sending CSAT surveys via SMS vs. email, especially for time-sensitive support experiences.
Refine support training to prioritize active listening and outcome framing, not just speed.
Partner with CX to close the loop on negative CSATs with fast follow-up and coaching insights.

Required Datapoints to calculate the metric
- Positive Responses: The number of responses in the top range of the scale (e.g., 4–5 on a 5-point scale).
- Total Responses: The number of customers who responded to the survey.
Example to show how the metric is derived

An e-commerce store collects CSAT after a support interaction:
- Survey Responses: 500
- Satisfied Responses (4 or 5): 450
- CSAT = (450 / 500) × 100 = 90%

Formula¶

Formula

\[ \mathrm{Customer\ Satisfaction\ Score} = \left( \frac{\mathrm{Sum\ of\ Positive\ Responses}}{\mathrm{Total\ Responses}} \right) \times 100 \]

Data Model Definition¶

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

CustomerSatisfaction

cube('CustomerSatisfaction', {
  sql: `SELECT * FROM customer_satisfaction`,

  measures: {
    positiveResponses: {
      sql: `positive_responses`,
      type: 'sum',
      title: 'Positive Responses',
      description: 'The number of responses in the top range of the scale (e.g., 4–5 on a 5-point scale).'
    },
    totalResponses: {
      sql: `total_responses`,
      type: 'sum',
      title: 'Total Responses',
      description: 'The number of customers who responded to the survey.'
    },
    customerSatisfactionScore: {
      sql: `positive_responses * 1.0 / NULLIF(total_responses, 0)`,
      type: 'number',
      title: 'Customer Satisfaction Score',
      description: 'The ratio of positive responses to total responses, indicating customer satisfaction.'
    }
  },

  dimensions: {
    id: {
      sql: `id`,
      type: 'number',
      primaryKey: true,
      title: 'ID',
      description: 'Unique identifier for each survey response.'
    },
    surveyDate: {
      sql: `survey_date`,
      type: 'time',
      title: 'Survey Date',
      description: 'The date when the survey was conducted.'
    }
  }
});

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.
- Resolution Speed: Delayed responses lead to customer frustration, reducing the Customer Satisfaction Score.
- Resolution Quality: Incomplete or ineffective solutions result in dissatisfaction, negatively impacting the Customer Satisfaction Score.
- Tone in Communication: Robotic or impersonal communication decreases customer satisfaction, lowering the Customer Satisfaction Score.
- Empathy in Communication: Lack of empathy in interactions makes customers feel undervalued, reducing the Customer Satisfaction Score.
- Clarity of Outcome: Unclear outcomes leave customers confused and dissatisfied, negatively affecting the Customer Satisfaction Score.
Positive influences

Factors that push the metric in a favorable direction, supporting growth or improvement.
- Resolution Speed: Quick responses enhance customer satisfaction, increasing the Customer Satisfaction Score.
- Resolution Quality: Effective and complete solutions improve customer satisfaction, boosting the Customer Satisfaction Score.
- Tone in Communication: A helpful and human tone in communication increases customer satisfaction, raising the Customer Satisfaction Score.
- Empathy in Communication: Empathetic interactions make customers feel valued, positively impacting the Customer Satisfaction Score.
- Clarity of Outcome: Clear communication of outcomes and next steps enhances customer understanding and satisfaction, improving the Customer Satisfaction Score.

Involved Roles & Activities¶

Involved Roles

These roles are typically responsible for implementing or monitoring this KPI:

Executive
Product Marketing (PMM)
Activities

Common initiatives or actions associated with this KPI:

Retention Strategies
Customer Support
Experience Improvement Initiatives

Funnel Stage & Type¶

AAARRR Funnel Stage

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

Retention
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.
- Net Promoter Score: NPS measures customers' willingness to recommend and loyalty, often correlating with and predicting changes in Customer Satisfaction Score (CSAT); a drop in NPS often precedes a decline in CSAT.
- Customer Effort Score: CES tracks the ease of customer interactions—lower effort typically leads to higher satisfaction, making it a strong contextual signal for upcoming trends in CSAT.
- Customer Loyalty: Measures long-term commitment and preference for the brand, providing a broader context for CSAT by indicating whether high satisfaction translates into loyal, returning customers.
- Engagement Rate: High engagement often signals positive experiences and satisfaction; a drop in engagement can be an early warning for falling CSAT.
- Onboarding Completion Rate: High rates of successful onboarding often lead to better customer experiences and higher satisfaction, serving as a precursor to improvements in CSAT.
Lagging

These lagging indicators confirm, quantify, or amplify this KPI and help explain the broader business impact on this KPI after the fact.
- Customer Churn Rate: Elevated churn often validates prior drops in CSAT and provides a feedback loop to recalibrate satisfaction benchmarks and early warning indicators.
- Customer Downgrade Rate: An increase in downgrade rate after low CSAT confirms the negative impact of dissatisfaction and can be analyzed to improve CSAT forecasting.
- Customer Retention Rate: High retention rates are typically observed following periods of high CSAT; drops here confirm and quantify the impact of satisfaction efforts.
- Customer Feedback Retention Score: Examines if customers giving feedback (positive/negative) remain loyal over time, helping refine how CSAT surveys are interpreted and targeted.
- Sentiment Analysis: Provides qualitative detail on satisfaction trends after the fact, helping to adjust and validate the predictive power of CSAT and related leading indicators.