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Complaints Received

Definition

Complaints Received refer to the number of formal or informal complaints submitted by customers or users about a product, service, or experience. These complaints highlight dissatisfaction and can cover a range of issues, from product defects to customer service challenges.

Description

Complaints Received tracks the volume of customer-reported issues — a direct, often emotional feedback loop that uncovers product gaps, experience friction, or communication misfires. It’s not just noise — it’s insight.

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

  • In SaaS, it highlights support or UX pain points post-onboarding
  • In consumer brands, it reflects expectation mismatches or product quality issues
  • In high-touch models, it flags trust risks or process breakdowns

An increased volume of complaints signals friction that could drive churn or reputational damage. A decline — assuming consistent volume — suggests product improvements or better education. Segment by topic, channel, or segment to pinpoint where action is most urgently needed.

Complaints Received informs:

  • Strategic decisions, like prioritizing roadmap fixes or comms improvements
  • Tactical actions, such as launching educational content or updating FAQs
  • Operational improvements, including ticket routing or resolution SLAs
  • Cross-functional alignment, by helping product, support, and marketing teams respond to real-world frustrations and turn feedback into fixes

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 Quality and Stability: Bugs, downtime, or janky workflows generate frustration and complaints. Clean UX reduces noise.
  • Support Accessibility and Responsiveness: When users can’t get help easily, complaints pile up. Good support catches friction early.
  • Clarity of Expectations in Marketing and Onboarding: Misleading promises → unhappy users. Honest positioning reduces surprise-based complaints.

Improvement Tactics & Quick Wins

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

  • If complaints are rising, tag and categorize them by theme (UX, pricing, bugs) to prioritize fixes.
  • Add proactive support prompts in high-friction workflows, helping users solve issues before they escalate.
  • Run a post-onboarding survey asking what’s confusing or missing, and use that to get ahead of complaint spikes.
  • Refine help center and support content to match the top 5 complaint categories.
  • Partner with product to log and prioritize “silent complaint” zones — areas where usage drops sharply without feedback.

  • Required Datapoints to calculate the metric

    • Number of Complaints: The total complaints received in a given time period.
    • Total Number of Customers: The total number of active customers during the same period.
    • Source of complaints (e.g., customer service, social media, product reviews).
    • Nature of complaints (e.g., product defects, user experience issues, customer service).

  • Example to show how the metric is derived

    A subscription service tracks complaints for Q2:

    • Complaints Received: 500
    • Total Customers: 50,000
    • Complaint Rate = (500 / 50,000) × 100 = 1%

Formula

Formula

\[ \mathrm{Complaint\ Rate\ (\%)} = \left( \frac{\mathrm{Number\ of\ Complaints}}{\mathrm{Total\ Number\ of\ Customers}} \right) \times 100 \]

Data Model Definition

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

cube('Complaints', {
  sql: `SELECT * FROM complaints`,

  measures: {
    numberOfComplaints: {
      sql: `id`,
      type: 'count',
      title: 'Number of Complaints',
      description: 'The total number of complaints received in a given time period.'
    }
  },

  dimensions: {
    id: {
      sql: `id`,
      type: 'number',
      primaryKey: true
    },

    source: {
      sql: `source`,
      type: 'string',
      title: 'Source of Complaints',
      description: 'The source from which the complaint was received, such as customer service, social media, or product reviews.'
    },

    nature: {
      sql: `nature`,
      type: 'string',
      title: 'Nature of Complaints',
      description: 'The nature of the complaint, such as product defects, user experience issues, or customer service.'
    },

    createdAt: {
      sql: `created_at`,
      type: 'time',
      title: 'Complaint Date',
      description: 'The date when the complaint was received.'
    }
  }
});

cube('Customers', {
  sql: `SELECT * FROM customers`,

  measures: {
    totalNumberOfCustomers: {
      sql: `id`,
      type: 'countDistinct',
      title: 'Total Number of Customers',
      description: 'The total number of active customers during the same period.'
    }
  },

  dimensions: {
    id: {
      sql: `id`,
      type: 'number',
      primaryKey: true
    },

    createdAt: {
      sql: `created_at`,
      type: 'time',
      title: 'Customer Registration Date',
      description: 'The date when the customer was registered.'
    }
  }
});

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 Quality and Stability: Poor product quality, frequent bugs, and system downtimes lead to increased customer frustration, directly resulting in a higher number of complaints received.
    • Support Accessibility and Responsiveness: Lack of accessible and responsive customer support causes unresolved issues, prompting customers to file more complaints.
    • Clarity of Expectations in Marketing and Onboarding: Misleading marketing and unclear onboarding processes create unmet expectations, leading to dissatisfaction and more complaints.
    • Pricing Transparency: Hidden fees or unclear pricing structures can lead to customer dissatisfaction and an increase in complaints.
    • Delivery Timeliness: Delays in product or service delivery can frustrate customers, resulting in more complaints.

  • Positive influences

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

    • Product Quality and Stability: High product quality and system reliability reduce customer frustration, leading to fewer complaints.
    • Support Accessibility and Responsiveness: Efficient and accessible customer support resolves issues quickly, decreasing the number of complaints.
    • Clarity of Expectations in Marketing and Onboarding: Clear and honest marketing and onboarding set accurate expectations, reducing surprise-based complaints.
    • Customer Feedback Mechanisms: Proactive collection and action on customer feedback can address issues before they escalate to formal complaints.
    • Loyalty Programs: Effective loyalty programs can enhance customer satisfaction and reduce the likelihood of complaints.

Involved Roles & Activities

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.

    • Ticket Volume: Increases or spikes in customer support ticket volume often precede and forecast a rise in Complaints Received. Monitoring ticket volume helps identify emerging issues or friction points before they escalate into formal complaints.
    • Drop-Off Rate: High drop-off rates in user journeys (e.g., onboarding, checkout) signal user frustration or unmet expectations, which often translate into future complaints as customers encounter obstacles or confusion.
    • Error Rate: A higher frequency of errors or failures in product/service usage is a strong predictor of incoming customer complaints, as errors directly impact user experience and satisfaction.
    • Escalation Rate: An uptick in the percentage of support cases being escalated to higher tiers signals issues that frontline support cannot resolve, often resulting in more severe complaints being formally submitted.
    • Customer Satisfaction Score: Declining CSAT scores indicate growing dissatisfaction and are typically followed by an increase in complaints as unhappy customers formalize their feedback.

  • 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 high volume of complaints is often correlated with an increase in churn, as unresolved issues or negative experiences drive customers to leave. Churn rate quantifies the broader business impact of dissatisfied customers.
    • Customer Downgrade Rate: After complaints are received, some customers may downgrade their plans or subscriptions, using downgrade rate as a signal of product/service fit issues and ongoing dissatisfaction.
    • Net Revenue Churn: Persistent or widespread complaints can contribute to lost revenue through both churn and downgrades, with net revenue churn quantifying the financial impact after complaints have occurred.
    • Sentiment Analysis: Analyzing the sentiment of complaints and related feedback provides post-facto insights into the severity, themes, and emotional drivers behind the complaints received.
    • Cost to Serve: A rise in complaints typically increases the resources and costs associated with customer support, impacting the overall cost to serve customers after issues are reported.