Referral Prompt Acceptance Rate¶

Definition¶

Referral Prompt Acceptance Rate measures the percentage of users who respond positively when presented with a referral prompt—e.g., clicking "Yes, I’ll refer" or continuing into the referral flow. It helps assess referral intent and the effectiveness of trigger timing.

Description¶

Referral Prompt Acceptance Rate is a key indicator of advocacy intent and timing alignment, reflecting how often users agree to refer after receiving a targeted referral nudge—via in-app, email, or lifecycle automation.

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

In SaaS, it follows moments like NPS submission or onboarding success
In eComm, it might appear post-purchase with a limited-time offer
In PLG, it often maps to activation milestones or paywall hits

A high acceptance rate signals emotional readiness, great timing, and aligned messaging, while a low rate may reflect fatigue, misfires, or unclear value exchange. By segmenting by prompt type, user segment, or channel, you can refine who gets asked, when, and with what incentive framing.

Referral Prompt Acceptance Rate informs:

Strategic decisions, like when to introduce referral prompts into lifecycle flows
Tactical actions, such as optimizing the language, tone, and placement of asks
Operational improvements, including prompt orchestration via marketing automation tools
Cross-functional alignment, connecting lifecycle, PMM, and CS to drive conversion from happy users to vocal advocates

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

Prompt Relevance to User Stage: Early-stage users need to experience value before referring.
Incentive Framing and Simplicity: Generic or unclear rewards lead to skipped prompts.
Visual Priority and UX Placement: If referral CTAs are buried or passive, users ignore them.

Improvement Tactics & Quick Wins¶

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

If prompt acceptance is low, test different placements: try modal nudges vs. persistent banners.
Add a micro-incentive for engaging (“Click to learn how you can earn X”).
Run a message A/B test focused on emotional/social framing (“Help others like you…”).
Refine in-app timing — prompt after positive feedback or repeated usage streaks.
Partner with product and growth to monitor prompt click-to-share ratio and optimize placement.

Required Datapoints to calculate the metric
- Number of Referral Prompts Shown
- Number of Users Who Accepted the Prompt (clicked “Yes” or continued)
- Prompt Type and Delivery Method
Example to show how the metric is derived

1,800 in-app referral prompts shown this month 630 users clicked “Yes” or “Continue” Formula: 630 ÷ 1,800 = 35% Referral Prompt Acceptance Rate

Formula¶

Formula

\[ \mathrm{Referral\ Prompt\ Acceptance\ Rate} = \left( \frac{\mathrm{Accepted\ Prompts}}{\mathrm{Total\ Prompts\ Shown}} \right) \times 100 \]

Data Model Definition¶

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

ReferralPrompts

cube('ReferralPrompts', {
  sql: `SELECT * FROM referral_prompts`,

  measures: {
    numberOfPromptsShown: {
      sql: `number_of_prompts_shown`,
      type: 'sum',
      title: 'Number of Referral Prompts Shown',
      description: 'Total number of referral prompts shown to users.'
    },
    numberOfAcceptances: {
      sql: `number_of_acceptances`,
      type: 'sum',
      title: 'Number of Users Who Accepted the Prompt',
      description: 'Total number of users who accepted the referral prompt.'
    },
    acceptanceRate: {
      sql: `100.0 * ${numberOfAcceptances} / NULLIF(${numberOfPromptsShown}, 0)`,
      type: 'number',
      title: 'Referral Prompt Acceptance Rate',
      description: 'Percentage of users who accepted the referral prompt.'
    }
  },

  dimensions: {
    id: {
      sql: `id`,
      type: 'number',
      primaryKey: true,
      title: 'ID',
      description: 'Unique identifier for each referral prompt record.'
    },
    promptType: {
      sql: `prompt_type`,
      type: 'string',
      title: 'Prompt Type',
      description: 'Type of referral prompt shown to the user.'
    },
    deliveryMethod: {
      sql: `delivery_method`,
      type: 'string',
      title: 'Delivery Method',
      description: 'Method used to deliver the referral prompt to the user.'
    },
    createdAt: {
      sql: `created_at`,
      type: 'time',
      title: 'Created At',
      description: 'Timestamp when the referral prompt 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.
- Prompt Relevance to User Stage: If the referral prompt is shown to users who have not yet experienced the core value of the product, they are less likely to accept the referral prompt, leading to a lower acceptance rate.
- Incentive Framing and Simplicity: Complex or poorly communicated incentives can confuse users, resulting in a decreased likelihood of accepting the referral prompt.
- Visual Priority and UX Placement: Referral prompts that are not prominently displayed or are difficult to find can lead to users ignoring them, reducing the acceptance rate.
- User Engagement Level: Users with low engagement levels are less likely to respond positively to referral prompts, negatively impacting the acceptance rate.
- Timing of Prompt: Prompts shown at inopportune times, such as during a user's first interaction, can lead to lower acceptance rates.
Positive influences

Factors that push the metric in a favorable direction, supporting growth or improvement.
- Prompt Relevance to User Stage: When referral prompts are aligned with the user's journey and shown after they have experienced value, acceptance rates increase.
- Incentive Framing and Simplicity: Clear and attractive incentives that are easy to understand can encourage users to accept referral prompts.
- Visual Priority and UX Placement: Prominently placed and visually appealing referral prompts can capture user attention and increase acceptance rates.
- User Satisfaction: High levels of user satisfaction and positive experiences with the product can lead to higher acceptance rates of referral prompts.
- Social Proof: Incorporating social proof, such as testimonials or user reviews, can positively influence users to accept referral prompts.

Involved Roles & Activities¶

Involved Roles

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

Customer Success
Growth
Customer Lifecycle Management
Product Marketing (PMM)
Support Manager
UX Designer / Researcher
Activities

Common initiatives or actions associated with this KPI:

Referral Prompt Design
Lifecycle Campaigns
NPS-Based Targeting
Offer Personalization

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.
- Referral Prompt Interaction Rate: Referral Prompt Interaction Rate acts as a leading indicator for Referral Prompt Acceptance Rate by measuring the percentage of users who engage with referral prompts (e.g., click, hover, expand), regardless of their decision. High interaction rates typically forecast higher acceptance rates, as engaged users are more likely to convert when prompted.
- Referral Readiness Score: Referral Readiness Score is a predictive metric that estimates the likelihood of a user or account to accept a referral prompt. It directly influences Referral Prompt Acceptance Rate by quantifying advocacy potential before users are prompted, allowing for better targeting and higher acceptance outcomes.
- Customer Loyalty: Customer Loyalty provides early signals of a user's propensity to advocate for the brand. High loyalty often precedes positive responses to referral prompts, making it a critical upstream factor in boosting Referral Prompt Acceptance Rate.
- Activation Rate: Activation Rate measures the percentage of users reaching meaningful engagement milestones. Activated users are more likely to be receptive to referral prompts, so improvements in activation directly drive higher acceptance rates of referral offers.
- Customer Health Score: Customer Health Score synthesizes engagement, satisfaction, and support data to predict renewal and advocacy. Healthier customers are more likely to accept referral prompts, making this a crucial leading metric for driving the acceptance rate.
Lagging

These lagging indicators confirm, quantify, or amplify this KPI and help explain the broader business impact on this KPI after the fact.
- Referral Invitation Rate: Referral Invitation Rate measures the proportion of users who actively send referrals after responding to prompts. It confirms and quantifies the downstream impact of Referral Prompt Acceptance Rate, as high acceptance typically results in more invitations.
- Referral Conversion Rate: Referral Conversion Rate quantifies how many referred leads turn into paying customers or complete a desired action. It amplifies the business impact of Referral Prompt Acceptance Rate, as higher acceptance leads to more conversions.
- New Users from Referrals: New Users from Referrals tracks the number of users acquired due to referral activity. It is influenced by Referral Prompt Acceptance Rate and demonstrates the tangible growth impact of increased acceptance.
- Referral Engagement Rate: Referral Engagement Rate measures the percentage of referred contacts who engage with referral invitations. High Referral Prompt Acceptance Rate should correlate with more engaged referral recipients, linking prompt acceptance to downstream engagement.
- Referral-Driven Expansion Revenue: Referral-Driven Expansion Revenue highlights the additional revenue generated from referred users who expand their usage or upgrade. It quantifies the broader financial impact resulting from increased Referral Prompt Acceptance Rate.