Spam Complaints¶

Definition¶

Spam Complaints measure the number of recipients who mark your email as spam or junk after receiving it. This metric reflects how well your emails align with recipient expectations and can significantly impact your sender reputation.

Description¶

Spam Complaints is a critical indicator of email list quality and message relevance, reflecting how often recipients mark your emails as spam — which directly impacts deliverability and sender reputation.

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

In SaaS or PLG, it often occurs after product emails, onboarding drips, or nurture sequences
In eCommerce, it can spike after high-frequency promo sends
In B2B, it can surface after gated content follow-ups or cold sequences

A high complaint rate puts your domain at risk and suggests you're sending irrelevant or intrusive content. A low rate reflects good list hygiene and audience trust. By segmenting by campaign, user segment, or engagement history, you can suppress risky cohorts and adjust frequency, tone, or opt-in clarity.

Spam Complaints informs:

Strategic decisions, like tightening audience segmentation and double opt-in standards
Tactical actions, such as rewording subject lines, pruning inactive lists, or creating better unsubscribe flows
Operational improvements, including sender authentication (SPF/DKIM), reputation monitoring, and list re-engagement
Cross-functional alignment, helping marketing ops, content, and lifecycle teams maintain channel health and customer trust

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

Audience Fit and Consent Quality: Cold or scraped lists spike spam rates fast.
Email Frequency and Expectation Setting: Surprise emails, or sending too often, trigger unsubscribes and complaints.
Copy Relevance and Deliverability: Misleading subject lines = frustration and clicks on the.

Improvement Tactics & Quick Wins¶

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

If complaints spike, prune inactive contacts and re-confirm opt-in for older segments.
Add clearer “from” names and preview text that sets the right expectation.
Run preference center updates — let users set frequency and topic preferences.
Refine onboarding emails to confirm consent and explain what’s coming next.
Partner with RevOps to monitor sender reputation and implement suppression rules.

Required Datapoints to calculate the metric
- Total Emails Delivered: The number of emails successfully sent to recipients.
- Spam Complaints: The count of emails marked as spam or junk by recipients.
Example to show how the metric is derived

An e-commerce brand sends a promotional email campaign:
- Total Emails Delivered: 10,000
- Spam Complaints: 50
- Spam Complaint Rate = (50 / 10,000) × 100 = 0.5%

Formula¶

Formula

\[ \mathrm{Spam\ Complaint\ Rate} = \left( \frac{\mathrm{Spam\ Complaints}}{\mathrm{Total\ Emails\ Delivered}} \right) \times 100 \]

Data Model Definition¶

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

EmailMetrics

cube('EmailMetrics', {
  sql: `SELECT * FROM email_metrics`,

  measures: {
    totalEmailsDelivered: {
      sql: 'total_emails_delivered',
      type: 'sum',
      title: 'Total Emails Delivered',
      description: 'The number of emails successfully sent to recipients.'
    },
    spamComplaints: {
      sql: 'spam_complaints',
      type: 'sum',
      title: 'Spam Complaints',
      description: 'The count of emails marked as spam or junk by recipients.'
    }
  },

  dimensions: {
    id: {
      sql: 'id',
      type: 'string',
      primaryKey: true,
      title: 'ID',
      description: 'Unique identifier for each email record.'
    },
    recipientEmail: {
      sql: 'recipient_email',
      type: 'string',
      title: 'Recipient Email',
      description: 'Email address of the recipient.'
    },
    deliveryDate: {
      sql: 'delivery_date',
      type: 'time',
      title: 'Delivery Date',
      description: 'The date and time when the email was delivered.'
    }
  }
});

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.
- Audience Fit and Consent Quality: Using cold or scraped email lists increases the likelihood of recipients marking emails as spam due to lack of prior consent or interest.
- Email Frequency: Sending emails too frequently can lead to recipient fatigue, resulting in higher spam complaints as recipients feel overwhelmed.
- Expectation Setting: Failing to set clear expectations about email frequency and content can lead to surprise emails, causing recipients to mark them as spam.
- Copy Relevance: Irrelevant or misleading email content frustrates recipients, increasing the chances of them marking the email as spam.
- Deliverability Issues: Poor deliverability, such as emails landing in the wrong folder, can lead to recipients marking them as spam due to perceived irrelevance.
Positive influences

Factors that push the metric in a favorable direction, supporting growth or improvement.
- Audience Fit and Consent Quality: Using well-targeted and consented email lists ensures recipients are more likely to engage positively, reducing spam complaints.
- Email Frequency: Maintaining an appropriate email frequency that aligns with recipient expectations helps minimize spam complaints.
- Expectation Setting: Clearly communicating what recipients can expect in terms of email content and frequency reduces the likelihood of spam complaints.
- Copy Relevance: Providing relevant and valuable content increases recipient satisfaction and reduces the likelihood of emails being marked as spam.
- Deliverability Optimization: Ensuring high deliverability rates by avoiding spam traps and maintaining a good sender reputation reduces spam complaints.

Involved Roles & Activities¶

Involved Roles

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

Email Marketing
Marketing
Activities

Common initiatives or actions associated with this KPI:

Content Marketing
Email Frequency Review
List Hygiene

Funnel Stage & Type¶

AAARRR Funnel Stage

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

Awareness
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.
- Open Rate: A low open rate is an early signal that recipients are not interested in or expecting your emails, which increases the likelihood of spam complaints as disengaged users may mark emails as spam instead of opening them.
- Unsubscribe Rate: High unsubscribe rates often follow or coincide with higher spam complaints, and analyzing unsubscribe reasons can recalibrate targeting, content, and frequency strategies to reduce future spam complaints.
- Click-Through Rate: A low click-through rate, especially when paired with high send volume, can indicate poor email relevance or targeting, forecasting a higher risk that users will mark emails as spam.
- Drop-Off Rate: High drop-off rates in email journeys or conversion flows can signal recipient frustration or mismatch in expectations, often preceding or correlating with increased spam complaints.
- Content Engagement: Low engagement with email content (e.g., little reading, few clicks) suggests poor content relevance, which is a precursor to recipients marking emails as spam.
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: High customer churn following periods of elevated spam complaints can reveal the downstream impact of negative email experiences, informing future segmentation and email strategy to prevent complaint-driven attrition.
- Customer Downgrade Rate: Elevated spam complaints often precede customer downgrades, indicating that poor email practices not only damage reputation but also drive users to lower-value plans. This insight can be used to recalibrate communication strategies.
- Conversion Rate: Drops in conversion rates after spikes in spam complaints provide feedback about the effectiveness and perception of email campaigns, enabling adjustment of pre-campaign signals to prevent future complaints.
- Customer Engagement Score: Declining engagement scores after increases in spam complaints highlight the negative effect of unwanted emails, helping refine leading indicators and improve targeting.
- Net Revenue Churn: Increases in spam complaints often correlate with revenue lost from downgrades and churn, showing the financial impact and helping update predictive models for future email risk.