Customer Feedback Score¶

Definition¶

Customer Feedback Score measures customer sentiment and satisfaction based on responses to feedback requests, often collected through surveys, ratings, or qualitative input. This metric provides direct insight into customer opinions about your product, service, or overall experience.

Description¶

Customer Feedback Score (CFS) is a composite metric that captures customer sentiment through structured and unstructured feedback, such as NPS, CSAT, qualitative responses, or sentiment analysis. It serves as a direct indicator of customer happiness, brand perception, and future loyalty.

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

In SaaS, it surfaces feature satisfaction, usability sentiment, and CSM experience
In eCommerce, it reflects delivery, product quality, and support performance
In B2B, it supports renewal forecasting and roadmap prioritization

A high CFS suggests strong alignment with customer needs. A low or declining trend may flag pain points, experience gaps, or competitive threats. Segment by persona, touchpoint, or lifecycle stage to target improvements where they’ll matter most.

Customer Feedback Score informs:

Strategic decisions, like roadmap investments, support scaling, or messaging pivots
Tactical actions, such as closing the loop on low scores or highlighting feedback-driven wins
Operational improvements, including feedback routing and dashboarding
Cross-functional alignment, by uniting product, CS, and marketing around listening, acting, and improving

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

Survey Frequency and Context: Too many or poorly timed surveys drive low scores and fatigue. Smart timing improves reliability.
Product Performance and Feature Quality: Buggy features, missing capabilities, or UX friction erode customer happiness quickly.
Support and Relationship Experience: Positive interactions with support and CS elevate overall perception, even during tough moments.

Improvement Tactics & Quick Wins¶

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

If feedback scores are low, segment by product area or touchpoint and prioritize the top negative drivers.
Add survey microcopy that sets expectations (“This will help us improve X”) to boost response rates and honesty.
Run a test delivering surveys only after successful experiences (e.g., issue resolved, feature completed).
Refine escalation workflows for negative scores, enabling fast follow-up and sentiment repair.
Partner with product and design to fix recurring UX issues tied to low feedback scores.

Required Datapoints to calculate the metric
- Feedback Responses: Numerical or textual feedback provided by customers.
- Total Survey Participants: The total number of customers surveyed.
Example to show how the metric is derived

A SaaS platform asks users to rate their experience on a scale of 1–10:
- Total Feedback Score: 7,500
- Total Responses: 1,000
- Average Feedback Score = 7,500 / 1,000 = 7.5
An e-commerce store collects feedback categorized as positive, neutral, or negative:
- Positive Responses: 300
- Total Responses: 500
- Percentage of Positive Feedback = (300 / 500) × 100 = 60%

Formula¶

Formula

$$ \mathrm{Average\ Feedback\ Score} = \frac{\mathrm{Total\ Feedback\ Score}}{\mathrm{Number\ of\ Responses}}

\mathrm{Percentage\ of\ Positive\ Feedback} = \left( \frac{\mathrm{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.

CustomerFeedback

cube('CustomerFeedback', {
  sql: `SELECT * FROM customer_feedback`,

  measures: {
    feedbackScore: {
      sql: `feedback_score`,
      type: 'avg',
      title: 'Average Feedback Score',
      description: 'Average score from customer feedback responses.'
    },
    totalSurveyParticipants: {
      sql: `total_survey_participants`,
      type: 'sum',
      title: 'Total Survey Participants',
      description: 'Total number of customers surveyed.'
    }
  },

  dimensions: {
    id: {
      sql: `id`,
      type: 'number',
      primaryKey: true,
      title: 'ID',
      description: 'Unique identifier for each feedback entry.'
    },
    feedbackResponse: {
      sql: `feedback_response`,
      type: 'string',
      title: 'Feedback Response',
      description: 'Textual feedback provided by customers.'
    },
    createdAt: {
      sql: `created_at`,
      type: 'time',
      title: 'Created At',
      description: 'Timestamp when the feedback was submitted.'
    }
  }
});

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.
- Survey Frequency and Context: Excessive or poorly timed surveys can lead to customer fatigue and frustration, resulting in lower Customer Feedback Scores.
- Product Performance and Feature Quality: Bugs, missing features, or a poor user experience can significantly decrease customer satisfaction, negatively impacting the Customer Feedback Score.
- Response Time in Support: Slow or ineffective responses from customer support can lead to dissatisfaction, reducing the Customer Feedback Score.
- Pricing Perception: If customers perceive the product or service as overpriced, it can lead to dissatisfaction and lower the Customer Feedback Score.
- Competitor Comparison: Negative comparisons with competitors in terms of features or pricing can lead to a decrease in the Customer Feedback Score.
Positive influences

Factors that push the metric in a favorable direction, supporting growth or improvement.
- Survey Frequency and Context: Well-timed and relevant surveys can increase engagement and provide more accurate Customer Feedback Scores.
- Product Performance and Feature Quality: High-quality features and a seamless user experience can enhance customer satisfaction, positively influencing the Customer Feedback Score.
- Support and Relationship Experience: Positive and effective interactions with customer support can improve customer perception and increase the Customer Feedback Score.
- Loyalty Programs: Effective loyalty programs can enhance customer satisfaction and lead to higher Customer Feedback Scores.
- Brand Reputation: A strong and positive brand reputation can positively influence customer perceptions and increase the Customer Feedback Score.

Involved Roles & Activities¶

Involved Roles

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

Product Management (PM)
Product Marketing (PMM)
Insights Manager
Activities

Common initiatives or actions associated with this KPI:

Voice of Customer
CSAT Improvements
Feedback Collection Design

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.
- Customer Satisfaction Score: Customer Satisfaction Score provides immediate, quantitative feedback about customer satisfaction following interactions, serving as an early signal correlated with Customer Feedback Score trends and helping triangulate overall sentiment.
- Net Promoter Score: Net Promoter Score measures customers’ willingness to recommend the brand, often highly correlated with overall feedback score changes. It acts as an early indicator of advocacy and underlying satisfaction issues.
- Brand Awareness: Brand Awareness represents how familiar customers are with the brand. Shifts in awareness often precede sentiment changes captured in Customer Feedback Score, adding context to why feedback may improve or decline.
- Customer Loyalty: Customer Loyalty gauges the strength of repeat engagement and preference, which typically aligns closely with positive feedback trends. Changes in loyalty can foreshadow shifts in feedback sentiment.
- Engagement Metrics: Engagement Metrics, such as feature usage or interaction frequency, provide behavioral signals that contextualize or predict feedback scores. Fluctuations in engagement often precede or explain changes in customer sentiment.
Lagging

These lagging indicators confirm, quantify, or amplify this KPI and help explain the broader business impact on this KPI after the fact.
- Churn Risk Score: Churn Risk Score aggregates various signals to predict the likelihood of churn. If high churn risk follows low feedback scores, it can recalibrate how much weight is given to feedback as a leading indicator for retention forecasting.
- Customer Downgrade Rate: Customer Downgrade Rate quantifies the percentage of users reducing their subscription, often after negative feedback. Analyzing this rate helps refine how feedback scores are used to anticipate account health risks.
- Customer Feedback Score (Post-activation): Customer Feedback Score (Post-activation) ties feedback directly to users after onboarding, allowing calibration of early feedback’s predictive value for downstream satisfaction and retention.
- Customer Engagement Score: Customer Engagement Score measures actual usage and engagement following feedback. Comparing drops in engagement after negative feedback can improve the weighting and actionability of feedback as a leading signal.
- Sentiment Analysis: Sentiment Analysis quantifies the emotional tone within qualitative feedback, validating and refining scoring methodologies. It can be used to recalibrate feedback-based signals with deeper text analytics.