Referral Readiness Score¶
Definition¶
Referral Readiness Score is a predictive metric that assesses how likely a user or account is to make a referral based on behavioral, usage, and sentiment signals. It helps identify high-potential advocates before they take action.
Description¶
Referral Readiness Score is a key indicator of latent advocacy potential and referral timing accuracy, reflecting how a blend of behavior, satisfaction, and product milestones predicts which users are primed to refer—even before being prompted.
The relevance and interpretation of this metric shift depending on the model or product:
- In B2B SaaS, it includes signals like high NPS, team expansion, successful onboarding, or QBR feedback
- In PLG or consumer apps, it may reflect repeat usage, goal achievement, or past reward interactions
A rising score signals product trust, value alignment, and readiness to promote, while a declining score suggests churn risk, disengagement, or unqualified referrer segments. By scoring users or accounts based on thresholds (e.g., NPS + activation milestone), you can proactively identify and prioritize high-quality advocacy candidates.
Referral Readiness Score informs:
- Strategic decisions, like who to include in advocacy programs or referral pilot groups
- Tactical actions, such as automating referral prompts when readiness crosses a threshold
- Operational improvements, including CSM alerts, lifecycle segmentation, and referral scoring models
- Cross-functional alignment, across CS, lifecycle, PMM, and RevOps, to ensure referral asks are sent at just the right moment
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
- NPS or CSAT History: Promoters are significantly more likely to refer than passive users.
- Product Usage and Feature Depth: Power users are proud of the product and more confident in recommending it.
- Customer Longevity and Expansion History: Engaged, long-term users tend to have more credibility and internal influence.
Improvement Tactics & Quick Wins¶
Actionable ideas to optimize this KPI, from fast, low-effort wins to strategic initiatives that drive measurable impact.
- If your model isn’t predictive, enrich with more behavioral signals (e.g., invite activity, community comments).
- Add post-renewal or milestone triggers to score updates.
- Run a test using your top quartile of scores for outreach — compare referral yield.
- Refine score logic based on recent referral conversion data.
- Partner with CS to surface “referral-ready” accounts before key touchpoints like QBRs.
-
Required Datapoints to calculate the metric
- NPS or CSAT scores
- Product usage signals (e.g., feature adoption, frequency)
- Milestone achievements (e.g., onboarding completion)
- Support/CS interactions or qualitative notes
- Optional: account tenure, renewal status
-
Example to show how the metric is derived
NPS = 9 (+40 pts) Onboarding complete (+20 pts) Active weekly user (+15 pts) CS call flagged as “delighted” (+25 pts) Score = 100 (Referral-Ready)
Formula¶
Formula
Data Model Definition¶
How this KPI is structured in Cube.js, including its key measures, dimensions, and calculation logic for consistent reporting.
cube('UserBehavior', {
sql: `SELECT * FROM user_behavior`,
measures: {
npsScore: {
sql: `nps_score`,
type: 'avg',
title: 'Average NPS Score',
description: 'Average Net Promoter Score for users.'
},
csatScore: {
sql: `csat_score`,
type: 'avg',
title: 'Average CSAT Score',
description: 'Average Customer Satisfaction Score for users.'
},
featureAdoption: {
sql: `feature_adoption`,
type: 'avg',
title: 'Feature Adoption Rate',
description: 'Average rate of feature adoption by users.'
},
usageFrequency: {
sql: `usage_frequency`,
type: 'avg',
title: 'Usage Frequency',
description: 'Average frequency of product usage by users.'
}
},
dimensions: {
userId: {
sql: `user_id`,
type: 'string',
primaryKey: true,
title: 'User ID',
description: 'Unique identifier for each user.'
},
createdAt: {
sql: `created_at`,
type: 'time',
title: 'Created At',
description: 'Timestamp when the user behavior record was created.'
}
}
})
cube('Milestones', {
sql: `SELECT * FROM milestones`,
measures: {
onboardingCompletion: {
sql: `onboarding_completion`,
type: 'count',
title: 'Onboarding Completion Count',
description: 'Count of users who have completed onboarding.'
}
},
dimensions: {
milestoneId: {
sql: `milestone_id`,
type: 'string',
primaryKey: true,
title: 'Milestone ID',
description: 'Unique identifier for each milestone.'
},
achievedAt: {
sql: `achieved_at`,
type: 'time',
title: 'Achieved At',
description: 'Timestamp when the milestone was achieved.'
}
}
})
cube('SupportInteractions', {
sql: `SELECT * FROM support_interactions`,
measures: {
interactionCount: {
sql: `interaction_id`,
type: 'count',
title: 'Support Interaction Count',
description: 'Count of support interactions per user.'
}
},
dimensions: {
interactionId: {
sql: `interaction_id`,
type: 'string',
primaryKey: true,
title: 'Interaction ID',
description: 'Unique identifier for each support interaction.'
},
interactionDate: {
sql: `interaction_date`,
type: 'time',
title: 'Interaction Date',
description: 'Date of the support interaction.'
}
}
})
cube('AccountDetails', {
sql: `SELECT * FROM account_details`,
measures: {
accountTenure: {
sql: `account_tenure`,
type: 'avg',
title: 'Average Account Tenure',
description: 'Average tenure of accounts in months.'
},
renewalStatus: {
sql: `renewal_status`,
type: 'count',
title: 'Renewal Status Count',
description: 'Count of accounts by renewal status.'
}
},
dimensions: {
accountId: {
sql: `account_id`,
type: 'string',
primaryKey: true,
title: 'Account ID',
description: 'Unique identifier for each account.'
},
renewalDate: {
sql: `renewal_date`,
type: 'time',
title: 'Renewal Date',
description: 'Date of the account renewal.'
}
}
})
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.
- Low NPS or CSAT Scores: Low scores in NPS or CSAT can decrease referral readiness as dissatisfied users are less likely to recommend the product.
- Limited Product Usage: Users with limited interaction with the product are less likely to refer due to a lack of familiarity and confidence.
- Short Customer Tenure: New customers may have lower referral readiness as they have not yet developed a strong relationship with the product.
- Lack of Feature Adoption: Failure to adopt key features can negatively impact referral readiness as users may not fully appreciate the product's value.
- Negative Sentiment: Negative sentiment expressed in feedback or reviews can reduce the likelihood of referrals as it indicates dissatisfaction.
-
Positive influences
Factors that push the metric in a favorable direction, supporting growth or improvement.
- NPS or CSAT History: Higher NPS or CSAT scores indicate a greater likelihood of referrals as promoters are more inclined to recommend the product.
- Product Usage and Feature Depth: Increased product usage and deeper engagement with features correlate with a higher readiness to refer due to user confidence and satisfaction.
- Customer Longevity: Long-term customers are more likely to make referrals as they have established trust and credibility with the product.
- Expansion History: Accounts with a history of expansion are more likely to refer, as they have demonstrated satisfaction and commitment to the product.
- User Engagement: Higher levels of user engagement typically lead to increased referral readiness as engaged users are more invested in the product.
Involved Roles & Activities¶
-
Involved Roles
These roles are typically responsible for implementing or monitoring this KPI:
Customer Success
Data & Analytics
Growth
Customer Lifecycle Management
Product Marketing (PMM)
Revenue Operations -
Activities
Common initiatives or actions associated with this KPI:
Referral Campaign Targeting
Health Scoring
NPS Programs
Lifecycle Nurturing
Intent Monitoring
Funnel Stage & Type¶
-
AAARRR Funnel Stage
This KPI is associated with the following stages in the AAARRR (Pirate Metrics) funnel:
-
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.
- Net Promoter Score: NPS directly predicts referral intent and advocacy likelihood, serving as an early indicator for high Referral Readiness Score. Increases in NPS typically precede improvements in referral readiness.
- Product Qualified Accounts: Accounts identified as PQAs have demonstrated engagement and product fit, acting as a strong signal that these users are likely to have a higher Referral Readiness Score soon after qualification.
- Customer Loyalty: High customer loyalty is a precursor to advocacy behaviors, raising the probability that users will soon be ready to make a referral, thus positively impacting Referral Readiness Score.
- Customer Health Score: A strong Customer Health Score aggregates satisfaction, engagement, and usage, forecasting which accounts are likely to be ready for referral and increasing the future Referral Readiness Score.
- Activation Rate: A high Activation Rate indicates early product adoption and value realization, which typically leads to increased readiness for referrals as users become more engaged advocates.
-
Lagging
These lagging indicators confirm, quantify, or amplify this KPI and help explain the broader business impact on this KPI after the fact.
- Referral Prompt Acceptance Rate: This measures the percentage of users accepting referral prompts, confirming the predictive accuracy of the Referral Readiness Score by showing actual referral actions taken.
- Referral Invitation Rate: Tracks how many users actively send referral invites, quantifying the conversion of referral readiness into observable referral activity and validating the Referral Readiness Score.
- Social Shares: Social Shares reflect users' willingness to advocate publicly, amplifying and validating high Referral Readiness Scores through observed advocacy behavior.
- Referral Discussion Initiation Rate: Measures proactive user behavior in starting referral conversations, providing a lagging confirmation of readiness to refer and the effectiveness of readiness identification.
- Referral Conversion Rate: Quantifies the percentage of referrals that convert, serving as a downstream validation of the Referral Readiness Score's impact on actual referral outcomes.