Share Rate Among ICPs

Definition

Share Rate Among ICPs measures the percentage of Ideal Customer Profile (ICP) users who share your content, product, or referral links. It helps quantify advocacy and resonance within your most strategically important audience.

Business Context

Share Rate Among ICPs is a key indicator of advocacy strength and ICP engagement quality, reflecting how often users that match your Ideal Customer Profile (ICP) share your product, content, or brand with others — a critical measure of product resonance and virality.

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

In B2B SaaS, it highlights whitepaper shares, teammate invites, or webinar forwards from decision-makers
In PLG, it reflects usage-based referrals like shared dashboards or team onboarding
In B2C and creator-led platforms, it surfaces referral links, social shares, or product mentions in content

A rising rate signals high-value audience alignment and organic growth potential, while a falling rate may indicate unclear value, unengaging content, or friction in sharing workflows.

By segmenting by ICP cohort, plan tier, or usage behavior, you unlock insights to refine referral incentives, messaging tone, or product-led share flows.

Share Rate Among ICPs informs:

Strategic decisions, like fine-tuning GTM messaging for ideal users
Tactical actions, such as revising share prompts or optimizing in-product referrals
Operational improvements, including referral UX updates or advocate reward systems
Cross-functional alignment, by linking efforts between product marketing, brand, lifecycle, and community teams

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

Content Relevance and Persona Fit: Generic content doesn’t get shared by strategic buyers.
Value Perception of the Share: Content that makes users look smart or helpful gets shared more.
Distribution Channels Aligned to Role: ICPs share differently — execs post on LinkedIn, practitioners in Slack.

Improvement Tactics & Quick Wins

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

If share rate is low among ICPs, build persona-specific content (e.g., “Ops Leader’s Guide to…”)
Add role tagging in product and email campaigns to track who shares and how often.
Run a “share this insight” campaign with peer-to-peer templates.
Refine CTAs in product and emails to highlight social currency (“Your peers need this too”).
Partner with advocacy or community teams to amplify top-shared assets through ICP champions.

Activities commonly tied to improving or operationalizing this KPI.

Activity	Description
Referral Program Optimization	Referral Program Optimization focuses on the strategic assessment, redesign, and ongoing optimization of customer and partner referral initiatives. It coordinates execution across touchpoints so teams can move users or accounts toward the target outcome. Relevant KPIs include Personalized Referral Outreach Rate and Referral Churn Rate.
Persona Strategy	Persona Strategy is essential for shaping an effective go-to-market strategy. It gives teams a clear plan for where to focus, how to sequence work, and what to measure. Relevant KPIs include Share Rate Among ICPs.
Advocacy Campaigns	Advocacy Campaigns focuses on Coordinated efforts to shape public perception, influence stakeholder opinions, and impact industry standards play a crucial role in creating favorable conditions for product adoption and business growth. It coordinates execution across touchpoints so teams can move users or accounts toward the target outcome. Relevant KPIs include New Users from Referrals and Personalized Referral Outreach Rate.
Content Distribution	Content Distribution focuses on strategically distributing marketing, sales, and product materials across diverse channels to increase visibility and engagement among target audiences. It helps teams translate strategy into repeatable execution. Relevant KPIs include Share Rate Among ICPs.
Sharing UX Enhancements	Sharing UX Enhancements focuses on systematically documenting and sharing recent or upcoming enhancements to the product’s user experience (UX) with both internal and external stakeholders. It improves performance by removing friction, testing changes, and scaling what works. Relevant KPIs include Share Rate Among ICPs.

Required Datapoints

Users Identified as ICP (via firmographics, role, etc.)
Total ICP Users Active During Timeframe
Number of ICP Users Who Shared a Resource or Link
Defined Share Events (email invite, share button, copy link, social share)

Example

800 active ICP users in April

180 shared a referral link, report, or invite

Formula: 180 ÷ 800 = 22.5% Share Rate Among ICPs

Formula

\mathrm{Share\ Rate\ Among\ ICPs} = \left( \frac{\mathrm{ICP\ Users\ Who\ Shared}}{\mathrm{Total\ ICP\ Users\ Active}} \right) \times 100

Positive & Negative Influences

Negative Influences

Content Irrelevance: Content that does not align with the interests or needs of the ICP is less likely to be shared, negatively impacting the Share Rate Among ICPs.
Low Value Perception: If the content does not provide perceived value or benefit to the sharer, it discourages sharing, reducing the Share Rate Among ICPs.
Misaligned Distribution Channels: Using distribution channels that do not align with the ICP’s preferences or professional role can decrease the likelihood of sharing, negatively affecting the Share Rate Among ICPs.
Complexity of Content: Overly complex or difficult-to-understand content can deter sharing among ICPs, leading to a lower Share Rate Among ICPs.
Lack of Engagement: Low engagement with content can signal a lack of interest or relevance, resulting in decreased sharing and a negative impact on the Share Rate Among ICPs.

Positive Influences

Content Relevance and Persona Fit: When content is highly relevant and tailored to the specific needs and interests of the ICP, it is more likely to be shared, increasing the Share Rate Among ICPs.
Value Perception of the Share: If the content enhances the sharer’s reputation by making them appear knowledgeable or helpful, it positively influences the Share Rate Among ICPs.
Distribution Channels Aligned to Role: Utilizing the appropriate distribution channels that align with the ICP’s professional role increases the likelihood of content being shared, thus boosting the Share Rate Among ICPs.
Engagement Rate: Higher engagement rates with content often lead to increased sharing among ICPs, as engaged users are more likely to advocate for the content.
Network Size of ICPs: ICPs with larger networks have a greater potential to share content widely, positively impacting the Share Rate Among ICPs.

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.

Ownership

Primary Owner

This role is directly accountable for the KPI and is expected to drive progress and decisions around it.

Demand Generation

Secondary Owners

These roles contribute directly to performance and typically partner on execution, reporting, or optimization.

Stakeholders

These roles should stay informed on changes in the KPI because it influences adjacent planning, coordination, or outcomes.

Sales Enablement

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 of advocacy and future share behaviors among ICPs. High loyalty predicts a higher likelihood that ICP users will share content or referrals, thus positively influencing the Share Rate Among ICPs.
Activation Rate: Activation Rate signals how many ICP users are reaching meaningful engagement. Higher activation among ICPs increases the pool of potential advocates, serving as an early driver of increased share rate.
Net Promoter Score: Net Promoter Score (NPS) gauges willingness to recommend the product, acting as a precursor to actual sharing behaviors. A rising NPS within your ICP typically precedes higher share rates among that segment.
Virality Coefficient: Virality Coefficient measures how many additional users each ICP referrer brings. Higher virality among ICPs often forecasts future spikes in Share Rate Among ICPs, as it reflects the effectiveness of sharing loops.
Customer Referral Rate: Customer Referral Rate tracks the percentage of ICPs willing to refer others. This is a direct leading indicator, as a higher referral rate signals greater advocacy potential and likely boosts in share 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 quantifies the percentage of ICP users actively sending referral invitations. It directly amplifies and explains increases in Share Rate Among ICPs by capturing the action that results from advocacy intent.
Social Shares: Social Shares measures the total sharing activity, including from ICPs. It confirms and quantifies the impact of advocacy, providing a broader context to the specific Share Rate Among ICPs metric.
Referral Link Shares: Referral Link Shares tracks the number of times ICP users share referral links. High numbers confirm and expand upon Share Rate Among ICPs by detailing actual referral distribution behaviors.
Referral Prompt Acceptance Rate: Referral Prompt Acceptance Rate reflects the percentage of ICPs who accept a referral prompt, preceding sharing. This metric validates the effectiveness of referral triggers and supports the observed share rate.
Product Sharing Rate: Product Sharing Rate measures overall sharing behaviors, of which ICP share rate is a subset. It explains the broader impact and can help attribute business growth directly to advocacy from ICPs.

Data Model Definition

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

cube('IcpUsers', {
  sql: `SELECT * FROM icp_users`,

  measures: {
    totalIcpUsersActive: {
      sql: `total_icp_users_active`,
      type: 'sum',
      title: 'Total ICP Users Active',
      description: 'Total number of ICP users active during the timeframe.'
    },
    icpUsersWhoShared: {
      sql: `icp_users_who_shared`,
      type: 'sum',
      title: 'ICP Users Who Shared',
      description: 'Number of ICP users who shared a resource or link.'
    },
    shareRateAmongIcps: {
      sql: `100.0 * ${icpUsersWhoShared} / NULLIF(${totalIcpUsersActive}, 0)` ,
      type: 'number',
      title: 'Share Rate Among ICPs',
      description: 'Percentage of ICP users who shared content, product, or referral links.'
    }
  },

  dimensions: {
    id: {
      sql: `id`,
      type: 'string',
      primaryKey: true
    },
    userId: {
      sql: `user_id`,
      type: 'string',
      title: 'User ID',
      description: 'Unique identifier for the user.'
    },
    eventTime: {
      sql: `event_time`,
      type: 'time',
      title: 'Event Time',
      description: 'Time when the event occurred.'
    }
  }
});

cube('ShareEvents', {
  sql: `SELECT * FROM share_events`,

  measures: {
    definedShareEvents: {
      sql: `defined_share_events`,
      type: 'count',
      title: 'Defined Share Events',
      description: 'Count of defined share events such as email invite, share button, copy link, social share.'
    }
  },

  dimensions: {
    id: {
      sql: `id`,
      type: 'string',
      primaryKey: true
    },
    eventType: {
      sql: `event_type`,
      type: 'string',
      title: 'Event Type',
      description: 'Type of share event.'
    },
    eventTime: {
      sql: `event_time`,
      type: 'time',
      title: 'Event Time',
      description: 'Time when the share event occurred.'
    }
  }
});

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