Downgrade to Churn Conversion Rate¶

Definition¶

Downgrade to Churn Conversion Rate measures the percentage of customers who downgrade their plan or usage and later churn. It helps identify whether downgrades are leading indicators of customer loss.

Description¶

Downgrade to Churn Conversion Rate is a key indicator of customer retention health and recovery risk, reflecting how often users who downgrade end up churning altogether — often signaling dissatisfaction, unclear value, or poor post-downgrade experience.

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

In B2B SaaS, it highlights how pricing tiers and feature gating affect long-term retention after plan changes
In usage-based or metered billing models, it reflects declining consumption trends that precede cancellation
In freemium or PLG environments, it surfaces downgrades from paid back to free, often before silent churn

A rising trend typically signals weak recovery from customer downgrades and growing churn risk, which helps teams refine lifecycle comms, feature exposure, and CS interventions during the downgrade window. By segmenting by cohort — such as plan type, NPS score, support history, or engagement depth — you unlock insights for improving downgrade recovery paths and identifying at-risk personas earlier.

Downgrade to Churn Conversion Rate informs:

Strategic decisions, like tiered pricing structures, packaging simplification, or freemium exit criteria
Tactical actions, such as triggering outreach from CS or lifecycle marketing after a downgrade
Operational improvements, including self-service downgrade flows, in-app nudges, or usage-based save offers
Cross-functional alignment, by connecting signals across product, CS, lifecycle, and RevOps, keeping everyone focused on protecting revenue and reducing passive churn

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

Post-Downgrade Engagement and Value: Users who downgrade and still find value are likely to stay. If usage drops, churn is imminent.
Reason for Downgrade (Price vs. Fit): Downgrades due to budget cuts may bounce back. Downgrades due to poor fit usually lead to churn.
Re-Onboarding or Feature Discovery Post-Downgrade: If there’s no support to re-engage or reframe value, users fade out.

Improvement Tactics & Quick Wins¶

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

If downgrade → churn is high, trigger a post-downgrade email flow that highlights retained features and value.
Add an in-app survey to capture downgrade reason and route to success or sales accordingly.
Run a test offering feature preview prompts from higher tiers, even after downgrade.
Refine success playbooks to prioritize re-engagement for downgraded accounts in their first 30 days.
Partner with CS to hold 15-min “optimize your new plan” calls for mid/high-value customers.

Required Datapoints to calculate the metric
- Number of Customers Who Downgraded in a given period
- Number of Those Who Later Churned within a defined timeframe
- Churn Window: e.g., 30/60/90 days post-downgrade
Example to show how the metric is derived
- 200 customers downgraded in Q1
- 60 churned within 60 days
- Formula: 60 ÷ 200 = 30% Downgrade to Churn Conversion Rate

Formula¶

Formula

\[ \mathrm{Downgrade\ to\ Churn\ Conversion\ Rate} = \left( \frac{\mathrm{Downgraded\ Customers\ Who\ Churned}}{\mathrm{Total\ Downgraded\ Customers}} \right) \times 100 \]

Data Model Definition¶

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

CustomerDowngradesCustomerChurnsDowngradeToChurnConversion

cube(`CustomerDowngrades`, {
  sql: `SELECT * FROM customer_downgrades`,
  measures: {
    downgradedCount: {
      sql: `customer_id`,
      type: 'count',
      title: 'Number of Customers Who Downgraded',
      description: 'Total number of customers who downgraded their plan or usage in a given period.'
    }
  },
  dimensions: {
    id: {
      sql: `id`,
      type: 'number',
      primaryKey: true
    },
    downgradeDate: {
      sql: `downgrade_date`,
      type: 'time',
      title: 'Downgrade Date',
      description: 'The date when the customer downgraded their plan or usage.'
    }
  }
})

cube(`CustomerChurns`, {
  sql: `SELECT * FROM customer_churns`,
  measures: {
    churnedCount: {
      sql: `customer_id`,
      type: 'count',
      title: 'Number of Customers Who Churned',
      description: 'Total number of customers who churned within a defined timeframe post-downgrade.'
    }
  },
  dimensions: {
    id: {
      sql: `id`,
      type: 'number',
      primaryKey: true
    },
    churnDate: {
      sql: `churn_date`,
      type: 'time',
      title: 'Churn Date',
      description: 'The date when the customer churned.'
    }
  }
})

cube(`DowngradeToChurnConversion`, {
  sql: `SELECT d.customer_id, d.downgrade_date, c.churn_date FROM customer_downgrades d LEFT JOIN customer_churns c ON d.customer_id = c.customer_id AND c.churn_date BETWEEN d.downgrade_date AND DATE_ADD(d.downgrade_date, INTERVAL 90 DAY)`,
  measures: {
    conversionRate: {
      sql: `100.0 * COUNT(c.customer_id) / COUNT(d.customer_id)`,
      type: 'number',
      title: 'Downgrade to Churn Conversion Rate',
      description: 'Percentage of customers who downgraded and later churned within a 90-day window.'
    }
  },
  dimensions: {
    customerId: {
      sql: `customer_id`,
      type: 'number',
      title: 'Customer ID',
      description: 'Unique identifier for the customer.'
    },
    downgradeDate: {
      sql: `downgrade_date`,
      type: 'time',
      title: 'Downgrade Date',
      description: 'The date when the customer downgraded their plan or usage.'
    },
    churnDate: {
      sql: `churn_date`,
      type: 'time',
      title: 'Churn Date',
      description: 'The date when the customer churned.'
    }
  }
})

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.
- Post-Downgrade Engagement and Value: A significant drop in user engagement and perceived value after a downgrade is a strong predictor of churn, as users are not finding enough reasons to stay.
- Reason for Downgrade (Fit): Downgrades due to poor fit with the product often lead to higher churn rates, as these users are unlikely to find renewed value in the service.
- Lack of Re-Onboarding or Feature Discovery: Without effective re-onboarding or feature discovery efforts post-downgrade, users are more likely to disengage and eventually churn.
- Customer Support Responsiveness: Slow or ineffective customer support following a downgrade can exacerbate user dissatisfaction, increasing the likelihood of churn.
- Competitor Offerings: Attractive competitor offerings can lure downgraded users away, especially if they perceive better value elsewhere.
Positive influences

Factors that push the metric in a favorable direction, supporting growth or improvement.
- Post-Downgrade Engagement and Value: Maintaining or increasing engagement and perceived value post-downgrade can reduce churn, as users continue to find the service beneficial.
- Reason for Downgrade (Price): Users who downgrade due to budget constraints but still find the service valuable are more likely to return to higher plans when financially feasible.
- Effective Re-Onboarding or Feature Discovery: Proactive re-onboarding and feature discovery can help users find new value in the service, reducing the likelihood of churn.
- Loyalty Programs: Implementing loyalty programs can incentivize downgraded users to stay, as they feel rewarded for their continued patronage.
- Personalized Communication: Tailored communication that addresses user needs and highlights relevant features can enhance user satisfaction and reduce churn risk.

Involved Roles & Activities¶

Involved Roles

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

Customer Success
Data & Analytics
Product Marketing (PMM)
Revenue Operations
Activities

Common initiatives or actions associated with this KPI:

Retention Forecasting
Pricing Strategy
Lifecycle Playbooks
Downgrade Trigger Analysis

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 Loyalty: Customer Loyalty is a leading indicator for Downgrade to Churn Conversion Rate because declining loyalty often precedes behaviors such as downgrades and eventual churn. By monitoring loyalty trends, you can anticipate increased downgrade-to-churn conversions before they are reflected in lagging churn metrics.
- Net Promoter Score: Net Promoter Score (NPS) serves as an early warning for customer dissatisfaction. Lower NPS scores can signal future downgrades and churn, making it a predictive input for shifts in the Downgrade to Churn Conversion Rate.
- Activation Rate: Activation Rate tracks the proportion of users who reach key onboarding milestones. Low activation rates may reflect poor product fit or onboarding friction, which can later manifest as downgrades and increased churn conversion, thus influencing the target metric.
- Customer Health Score: Customer Health Score aggregates engagement, satisfaction, and support data to predict renewal or churn risk. Declining health scores often precede customer downgrades and eventual churn, directly influencing Downgrade to Churn Conversion Rate.
- Drop-Off Rate: Drop-Off Rate identifies points of friction in customer journeys where users disengage. High drop-off rates in critical flows can forecast future downgrades and serve as an early indicator that may result in higher Downgrade to Churn Conversion Rates.
Lagging

These lagging indicators confirm, quantify, or amplify this KPI and help explain the broader business impact on this KPI after the fact.
- Customer Downgrade Rate: Customer Downgrade Rate directly quantifies the population at risk for conversion to churn, as the target rate is explicitly calculated from customers who first downgrade. A higher downgrade rate increases the pool of potential churners, amplifying the Downgrade to Churn Conversion Rate.
- Churn Risk Score: Churn Risk Score, though predictive, is classified as lagging here and quantifies the likelihood of churn among accounts, including those that have downgraded. High churn risk among downgraded customers is a strong explanatory factor for increases in the Downgrade to Churn Conversion Rate.
- Customer Churn Rate: Customer Churn Rate confirms and contextualizes the broader impact of downgrade-induced churn. A rising Downgrade to Churn Conversion Rate will typically be reflected in the overall churn rate, validating and quantifying its impact.
- Net Revenue Churn: Net Revenue Churn captures the revenue impact of customers who downgrade and then churn, providing a financial dimension to the customer-based Downgrade to Churn Conversion Rate and confirming its effect on recurring revenue.
- Customer Retention Rate: Customer Retention Rate, as the inverse of churn, contextualizes the effectiveness of retention strategies. A decline in retention among downgraded cohorts amplifies the Downgrade to Churn Conversion Rate, confirming challenges in keeping these at-risk customers.