New Users from Referrals¶

Definition¶

New Users from Referrals measures the number of users who joined the platform via referral from an existing user or partner. It helps quantify the impact of referral and network-based growth strategies.

Description¶

New Users from Referrals is a key indicator of organic growth momentum and user trust, measuring how many users join via invite links, affiliate codes, or sharing flows.

Its application varies by motion:

In PLG, referrals often stem from teammates inviting others to collaborate
In B2B, it may include partner channels, customer advocacy, or employee invites

A rising referral rate reflects strong product satisfaction, community engagement, or incentive design, while a stall may suggest unclear value exchange or UX friction. By segmenting by referrer, plan, or usage behavior, you can refine incentive programs, sharing flows, and messaging, while surfacing top advocates.

New Users from Referrals informs:

Strategic decisions, like scaling virality loops and brand trust levers
Tactical actions, such as optimizing sharing CTAs or redesigning referral prompts
Operational improvements, including referral tracking, fraud detection, or incentive fulfillment

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

Referral Program Visibility and Simplicity: If users can’t find it or it’s too complex, they won’t share.
Reward Relevance: Incentives that match customer motivation (e.g., status, perks, discounts) drive higher participation.
Timing of Referral Ask: Asking after key value moments increases success rate.

Improvement Tactics & Quick Wins¶

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

If referral volume is low, add referral CTAs after “aha” moments — e.g., after feature activation or success screen.
Add one-click referral links and pre-written copy for social/email.
Run a test offering a tiered reward structure (“Refer 3 friends, get X”).
Refine the referral landing page to focus on what new users get out of it.
Partner with customer marketing to turn happy customers into visible advocates.

Required Datapoints to calculate the metric
- New Users with Referral Attribution
- Referral Source or Campaign
- Time Window
Example to show how the metric is derived

820 referred users in Q2

Formula¶

Formula

\[ \mathrm{New\ Users\ from\ Referrals} = \mathrm{Count\ of\ Net\ New\ Referred\ Users\ in\ Period} \]

Data Model Definition¶

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

NewUsers

cube(`NewUsers`, {
  sql: `SELECT * FROM new_users`,
  measures: {
    newUsersFromReferrals: {
      sql: `id`,
      type: 'count',
      title: 'New Users from Referrals',
      description: 'Counts the number of new users who joined via referral.'
    }
  },
  dimensions: {
    id: {
      sql: `id`,
      type: 'number',
      primaryKey: true
    },
    referralSource: {
      sql: `referral_source`,
      type: 'string',
      title: 'Referral Source',
      description: 'The source or campaign from which the user was referred.'
    },
    createdAt: {
      sql: `created_at`,
      type: 'time',
      title: 'User Creation Time',
      description: 'The time when the user account 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.
- Complexity of Referral Process: A complicated referral process discourages users from participating, reducing the number of new users from referrals.
- Lack of Incentive Appeal: If the rewards offered are not appealing, users are less likely to refer others, negatively impacting new users from referrals.
- Poor User Experience: A negative user experience can deter users from recommending the platform, decreasing new users from referrals.
- Infrequent Referral Program Updates: Stagnant referral programs that do not evolve with user needs can lead to decreased participation, reducing new users from referrals.
- Low Engagement of Existing Users: If existing users are not engaged, they are less likely to refer others, leading to a decrease in new users from referrals.
Positive influences

Factors that push the metric in a favorable direction, supporting growth or improvement.
- Referral Program Visibility and Simplicity: A well-promoted and easy-to-understand referral program encourages more users to participate, leading to an increase in new users from referrals.
- Reward Relevance: Offering incentives that align with user motivations enhances the likelihood of users referring others, thereby increasing new users from referrals.
- Timing of Referral Ask: Strategically asking for referrals after users experience key value moments increases the success rate of referrals, boosting new users from referrals.
- User Satisfaction: Higher user satisfaction leads to more enthusiastic referrals, resulting in an increase in new users from referrals.
- Network Size of Referring Users: Users with larger networks have a greater potential to bring in new users through referrals, positively impacting new users from referrals.

Involved Roles & Activities¶

Involved Roles

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

Customer Engagement
Growth
Customer Lifecycle Management
Product Marketing (PMM)
Activities

Common initiatives or actions associated with this KPI:

Referral Programs
Invite Flow Design
Advocacy Campaigns
Referral Attribution

Funnel Stage & Type¶

AAARRR Funnel Stage

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

Acquisition
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 Readiness Score: A higher Referral Readiness Score predicts a greater likelihood that users will make referrals soon, directly influencing future growth in New Users from Referrals. It acts as an early signal of upcoming referral-driven user acquisition.
- Referral Invitation Rate: This measures the percentage of users actively sending referral invitations, serving as a leading indicator for upcoming increases in New Users from Referrals as more invitations generally result in more sign-ups.
- Referral Prompt Acceptance Rate: Tracks the percentage of users who accept referral prompts. An increase here typically foreshadows an increase in New Users from Referrals, as more users are entering the referral flow.
- Referral Discussion Initiation Rate: Captures the percentage of users initiating referral-related conversations or actions. Early spikes in this metric often precede growth in New Users from Referrals, indicating rising intent and advocacy.
- Customer Referral Rate: While classified as a leading metric, it measures the proportion of customers initiating referrals, acting as a precursor to increases in New Users from Referrals by expanding the top of the referral funnel.
Lagging

These lagging indicators confirm, quantify, or amplify this KPI and help explain the broader business impact on this KPI after the fact.
- Referral Conversion Rate: Measures the percentage of referred leads that convert to users. It explains how efficiently referral invitations translate into actual New Users from Referrals, quantifying the downstream success of the referral process.
- Referral Funnel Drop-Off Rate: Tracks where users abandon the referral process. High drop-off rates can help explain missed targets for New Users from Referrals, highlighting friction points post-referral initiation.
- Referral Engagement Rate: Measures the engagement of referred contacts with referral content. Higher engagement rates often precede higher conversion into New Users from Referrals, while low engagement can explain underperformance.
- Referral Program Participation Rate: Quantifies how many eligible users are engaging with the referral program overall. Low participation may explain periods of stagnant or declining New Users from Referrals.
- Referral-Generated MQL Rate: Shows the quality of leads generated via referrals. A high rate suggests that referrals are bringing in marketing-qualified leads, which can amplify or explain increases in New Users from Referrals.