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Net Promoter Score (NPS)

Definition

Net Promoter Score (NPS) measures customer loyalty by gauging how likely customers are to recommend your product, service, or brand to others. It’s based on a single-question survey: “How likely are you to recommend our [product/service] to a friend or colleague?”

Description

Net Promoter Score (NPS) is a key indicator of customer sentiment, loyalty, and word-of-mouth potential, reflecting how likely users are to recommend your product or service to others.

Customers respond on a scale of 0 to 10, and their responses categorize them into:

  • Promoters (9–10): Loyal enthusiasts likely to recommend your brand and drive growth.
  • Passives (7–8): Satisfied but unenthusiastic customers who are less likely to actively promote your brand.
  • Detractors (0–6): Unhappy customers who may discourage others from using your product or service.

Its interpretation varies based on where you deploy it:

  • In B2B SaaS, NPS helps spot renewal risks and customer champions
  • In consumer apps, it reflects brand love and social sharing propensity
  • In services and support, it gauges experience quality and resolution satisfaction

A rising NPS indicates positive experiences and product fit, while a low score highlights frustrations or loyalty erosion. By segmenting by user role, plan, or lifecycle stage, you can uncover persona-specific pain points or evangelist signals.

Net Promoter Score informs:

  • Strategic decisions, like retention roadmap, loyalty campaigns, or advocacy programs
  • Tactical actions, such as launching save plays for Detractors or amplifying Promoter feedback
  • Operational improvements, including support training and customer comms updates
  • Cross-functional alignment, rallying product, CS, marketing, and UX around voice-of-customer insights

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

  • Product Performance and Reliability: Bugs and crashes tank NPS. Smooth, fast experiences raise it.
  • Onboarding and Early Wins: Customers who succeed early are more likely to become promoters.
  • Support and Relationship Quality: Customers who feel heard — especially after something went wrong — tend to rate higher.

Improvement Tactics & Quick Wins

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

  • If NPS is low, dig into detractor feedback themes and prioritize the top 2 fixable issues.
  • Add “You said, we did” messages to close the loop with customers who gave feedback.
  • Run a test timing NPS prompts after major wins or milestones — not randomly.
  • Refine survey microcopy to clarify the ask (“How likely are you to recommend [Product] to a peer like you?”).
  • Partner with CX and product to segment NPS by role, account size, and lifecycle stage for targeted follow-up.

  • Required Datapoints to calculate the metric


    • Survey Responses: Scores collected from customers (0 to 10).
    • Total Respondents: The total number of customers surveyed.
    • Promoters: (Number of Promoters / Total Respondents) × 100
    • Detractors: (Number of Detractors / Total Respondents) × 100
  • Example to show how the metric is derived


    A subscription service collects NPS responses from 1,000 customers:

    • Promoters: 600 (60%)
    • Passives: 250 (25%)
    • Detractors: 150 (15%)
    • NPS = 60% – 15% = 45

Formula

Formula

\[ \mathrm{Net\ Promoter\ Score} = \left( \mathrm{\% \ of\ Promoters} - \mathrm{\% \ of\ Detractors} \right) \]

Data Model Definition

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

cube('SurveyResponses', {
  sql: `SELECT * FROM survey_responses`,

  measures: {
    totalRespondents: {
      sql: `id`,
      type: 'count',
      title: 'Total Respondents',
      description: 'The total number of customers surveyed.'
    },
    numberOfPromoters: {
      sql: `CASE WHEN score >= 9 THEN 1 ELSE 0 END`,
      type: 'sum',
      title: 'Number of Promoters',
      description: 'The number of customers who are promoters (score 9 or 10).'
    },
    numberOfDetractors: {
      sql: `CASE WHEN score <= 6 THEN 1 ELSE 0 END`,
      type: 'sum',
      title: 'Number of Detractors',
      description: 'The number of customers who are detractors (score 0 to 6).'
    },
    promotersPercentage: {
      sql: `100.0 * ${numberOfPromoters} / NULLIF(${totalRespondents}, 0)`,
      type: 'number',
      title: 'Promoters Percentage',
      description: 'Percentage of promoters among total respondents.'
    },
    detractorsPercentage: {
      sql: `100.0 * ${numberOfDetractors} / NULLIF(${totalRespondents}, 0)`,
      type: 'number',
      title: 'Detractors Percentage',
      description: 'Percentage of detractors among total respondents.'
    },
    netPromoterScore: {
      sql: `${promotersPercentage} - ${detractorsPercentage}`,
      type: 'number',
      title: 'Net Promoter Score',
      description: 'Net Promoter Score calculated as the difference between promoters and detractors percentages.'
    }
  },

  dimensions: {
    id: {
      sql: `id`,
      type: 'number',
      primaryKey: true,
      title: 'ID',
      description: 'Unique identifier for each survey response.'
    },
    score: {
      sql: `score`,
      type: 'number',
      title: 'Score',
      description: 'Score given by the customer in the survey (0 to 10).'
    },
    createdAt: {
      sql: `created_at`,
      type: 'time',
      title: 'Created At',
      description: 'The time when the survey response was created.'
    }
  }
});

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.

    • Product Performance and Reliability: Frequent bugs and crashes lead to customer frustration, reducing their likelihood to recommend the product, thus lowering NPS.
    • Support and Relationship Quality: Poor customer support experiences, where customers feel unheard or unresolved, negatively impact their perception and decrease NPS.
    • Onboarding and Early Wins: A lack of clear guidance or early success can lead to customer dissatisfaction, reducing their likelihood to recommend the product, thus lowering NPS.
    • Pricing Perception: If customers perceive the product as overpriced relative to its value, they are less likely to recommend it, negatively impacting NPS.
    • Competitor Comparison: If customers find competitors offering better value or features, they may rate the product lower, decreasing NPS.
  • Positive influences


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

    • Product Performance and Reliability: A smooth, fast, and reliable product experience enhances customer satisfaction, increasing their likelihood to recommend the product, thus raising NPS.
    • Onboarding and Early Wins: Effective onboarding that leads to early customer success increases satisfaction and the likelihood of recommendation, boosting NPS.
    • Support and Relationship Quality: High-quality customer support, where customers feel valued and heard, positively influences their perception and increases NPS.
    • Brand Reputation: A strong, positive brand reputation can enhance customer pride in association, increasing their likelihood to recommend the product, thus raising NPS.
    • Customer Engagement: Active engagement with customers through personalized communication and community building can enhance loyalty and increase NPS.

Involved Roles & Activities


Funnel Stage & Type

  • AAARRR Funnel Stage


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

    Referral

  • 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: High CSAT scores typically forecast strong NPS performance as they reflect immediate post-interaction satisfaction, indicating customers are likely to become promoters in subsequent NPS surveys.
    • Customer Loyalty: Customer Loyalty measures repeat engagement and preference, acting as a precursor to recommendation intent; higher loyalty usually signals future increases in NPS.
    • Brand Awareness: Growing brand awareness broadens the pool of potential promoters and sets the stage for higher NPS by increasing top-of-mind presence and positive sentiment.
    • Engagement Rate: High engagement rate reflects strong user involvement and product resonance, often preceding or correlating with higher NPS as engaged customers are more likely to recommend.
    • Product Qualified Leads: An increase in PQLs indicates that users are experiencing core product value, which generally leads to higher satisfaction and positive NPS responses.
  • 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: A rising churn rate can reveal dissatisfaction not fully captured by NPS; analyzing churn reasons helps recalibrate NPS thresholds and early warning signals.
    • Customer Downgrade Rate: Downgrades indicate declining perceived value; examining downgrade trends provides feedback to refine NPS-related action plans or identify at-risk segments.
    • Customer Feedback Retention Score: Understanding which feedback givers are retained versus lost helps validate whether NPS is predictive of actual loyalty, informing weighting or segmentation strategies.
    • Referral Prompt Acceptance Rate: High or low acceptance rates on referral prompts validate the accuracy of NPS as an advocacy indicator and can highlight gaps between stated intent and actual behavior.
    • Sentiment Analysis: Aggregated sentiment from customer communications and reviews can confirm or challenge NPS trends, offering a granular view that informs adjustments to NPS survey timing, targeting, or interpretation.