Warm Introduction Offer Rate¶

Definition¶

Warm Introduction Offer Rate measures the percentage of customers, partners, or users who are asked or prompted to provide a warm introduction to a relevant contact. It helps assess how often teams or workflows are activating social referrals as part of GTM efforts.

Description¶

Warm Introduction Offer Rate is a key indicator of customer advocacy readiness and network-based growth activation, reflecting how often your team asks for or offers introductions to new prospects via trusted relationships.

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

In CS-led GTM, it highlights QBR follow-ups and success moments
In Sales-led B2B, it reflects momentum after closed-won deals
In Partner plays, it surfaces integration-driven referral opportunities

A low rate suggests missed opportunities or discomfort with the ask, while a higher rate shows process maturity and scalable relationship-led growth. By segmenting by CSM, rep, lifecycle stage, or NPS, you pinpoint when and where referrals are most likely to succeed.

Warm Introduction Offer Rate informs:

Strategic decisions, like referral program scaling and advocacy timing
Tactical actions, such as CSM enablement or in-app intro prompts
Operational improvements, including CRM tagging, template prompts, and talk track training
Cross-functional alignment, across sales, CS, PMM, and partnerships, fueling pipeline growth via trust

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

CS and Sales Playbooks: If intros aren’t scripted in, they don’t happen.
Advocate and Partner Programs: Build ecosystems where intros are natural, not forced.
Product or Lifecycle Prompts: Users are more likely to ask for help or intros after wins.

Improvement Tactics & Quick Wins¶

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

If offer rate is low, build QBR checklists with a “Would you benefit from an intro to X?” prompt.
Add in-app help or success prompts offering intros to peers or CS experts.
Run outreach campaigns targeting power users with “invite a peer” plays.
Refine partner referral flows to include warm intro flows instead of cold links.
Partner with CS and community teams to track intro-to-activation performance.

Required Datapoints to calculate the metric
- Number of Active Customers/Users in Scope
- Number of Times a Warm Introduction Was Prompted or Offered
- Defined Triggers (e.g., QBR, NPS score, renewal success)
Example to show how the metric is derived

200 high-satisfaction customers (NPS 9–10) in Q2 38 were asked for warm intros during onboarding check-ins or renewals Formula: 38 ÷ 200 = 19% Warm Introduction Offer Rate

Formula¶

Formula

\[ \mathrm{Warm\ Introduction\ Offer\ Rate} = \left( \frac{\mathrm{Warm\ Intro\ Prompts}}{\mathrm{Total\ Eligible\ Contacts}} \right) \times 100 \]

Data Model Definition¶

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

WarmIntroductionOffersActiveCustomers

cube('WarmIntroductionOffers', {
  sql: `SELECT * FROM warm_introduction_offers`,

  measures: {
    warmIntroductionOfferCount: {
      sql: `warm_introduction_id`,
      type: 'count',
      title: 'Warm Introduction Offer Count',
      description: 'Counts the number of times a warm introduction was prompted or offered.'
    },
    warmIntroductionOfferRate: {
      sql: `100.0 * ${warmIntroductionOfferCount} / NULLIF(${activeCustomerCount}, 0)` ,
      type: 'number',
      title: 'Warm Introduction Offer Rate',
      description: 'Calculates the percentage of active customers or users who were prompted for a warm introduction.'
    }
  },

  dimensions: {
    warmIntroductionId: {
      sql: `warm_introduction_id`,
      type: 'string',
      primaryKey: true,
      title: 'Warm Introduction ID',
      description: 'Unique identifier for each warm introduction offer.'
    },
    customerId: {
      sql: `customer_id`,
      type: 'string',
      title: 'Customer ID',
      description: 'Identifier for the customer involved in the warm introduction offer.'
    },
    triggerEvent: {
      sql: `trigger_event`,
      type: 'string',
      title: 'Trigger Event',
      description: 'The event that triggered the warm introduction offer, such as QBR or NPS score.'
    },
    offerDate: {
      sql: `offer_date`,
      type: 'time',
      title: 'Offer Date',
      description: 'The date when the warm introduction was offered.'
    }
  },

  joins: {
    ActiveCustomers: {
      relationship: 'belongsTo',
      sql: `${CUBE}.customer_id = ${ActiveCustomers}.customer_id`
    }
  }
});

cube('ActiveCustomers', {
  sql: `SELECT * FROM active_customers`,

  measures: {
    activeCustomerCount: {
      sql: `customer_id`,
      type: 'count',
      title: 'Active Customer Count',
      description: 'Counts the number of active customers or users in scope.'
    }
  },

  dimensions: {
    customerId: {
      sql: `customer_id`,
      type: 'string',
      primaryKey: true,
      title: 'Customer ID',
      description: 'Unique identifier for each active customer.'
    },
    customerName: {
      sql: `customer_name`,
      type: 'string',
      title: 'Customer Name',
      description: 'Name of the active customer.'
    },
    activationDate: {
      sql: `activation_date`,
      type: 'time',
      title: 'Activation Date',
      description: 'The date when the customer became active.'
    }
  }
});

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.
- Lack of CS and Sales Playbooks: Without structured playbooks, teams may not consistently prompt for warm introductions, leading to a lower offer rate.
- Weak Advocate and Partner Programs: If ecosystems are not well-developed, introductions may feel forced or unnatural, reducing the likelihood of them occurring.
- Infrequent Product or Lifecycle Prompts: Without timely prompts after user wins, the opportunity to ask for introductions may be missed, decreasing the offer rate.
- High Customer Turnover: Frequent changes in customer base can disrupt relationship building, leading to fewer warm introductions.
- Poor Internal Communication: Lack of alignment between teams can result in missed opportunities to request introductions.
Positive influences

Factors that push the metric in a favorable direction, supporting growth or improvement.
- Robust CS and Sales Playbooks: Well-defined playbooks ensure that teams consistently ask for warm introductions, increasing the offer rate.
- Strong Advocate and Partner Programs: A well-established ecosystem encourages natural introductions, enhancing the offer rate.
- Effective Product or Lifecycle Prompts: Timely prompts after user successes increase the likelihood of asking for introductions.
- High Customer Satisfaction: Satisfied customers are more willing to provide introductions, boosting the offer rate.
- Frequent Engagement with Partners: Regular interactions with partners can lead to more opportunities for warm introductions.

Involved Roles & Activities¶

Involved Roles

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

Account Management
Customer Success
Partner Manager
Product Marketing (PMM)
Sales Manager
Activities

Common initiatives or actions associated with this KPI:

QBRs
Advocacy Programs
Renewal Campaigns
Referral Activation
Customer Success Enablement

Funnel Stage & Type¶

AAARRR Funnel Stage

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

Referral
Revenue
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.
- Product Qualified Leads: Product Qualified Leads signal high engagement and product fit before an offer for a warm introduction is made. A rise in PQLs often precedes an increase in Warm Introduction Offer Rate as more accounts become good candidates to prompt for introductions.
- Customer Loyalty: Customer Loyalty indicates the level of satisfaction and repeated engagement from users. Loyal customers are more likely to respond positively when asked for warm introductions, making this a key early indicator of future increases in Warm Introduction Offer Rate.
- Activation Rate: Activation Rate measures how many users reach a meaningful engagement milestone. Higher activation rates expand the pool of users eligible and likely to be prompted for warm introductions, influencing the future Warm Introduction Offer Rate.
- Net Promoter Score: Net Promoter Score gauges customer willingness to recommend the product. High NPS signals a ready base of advocates who are more likely to accept a warm introduction request, thereby forecasting increases in the Offer Rate.
- Deal Velocity: Deal Velocity reflects the speed at which sales progress. Efficient, fast-moving deals often create positive momentum and engagement, increasing the likelihood that customers will be receptive to warm introduction prompts in the near future.
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: Referral Prompt Acceptance Rate measures how often users accept referral prompts, which is a downstream outcome of being offered a warm introduction. A high Warm Introduction Offer Rate is likely to lead to higher Acceptance Rates, confirming the effectiveness of upstream referral activation.
- Referral Invitation Rate: Referral Invitation Rate quantifies how many users actually send referral invitations after being offered. This metric validates whether Warm Introduction Offer Rate translates into real referral actions, amplifying the business impact.
- Referral Readiness Score: Referral Readiness Score predicts which users are likely to refer. Insights from actual Warm Introduction Offer Rates can recalibrate the predictive model, ensuring future readiness scores are aligned with observed behavior.
- New Users from Referrals: New Users from Referrals tracks the acquisition resulting from successful warm introductions. Increases in Warm Introduction Offer Rate should eventually result in more new users via referrals, confirming the lagging impact.
- Referral Engagement Rate: Referral Engagement Rate measures how referred contacts interact with referral invitations, reflecting the downstream effectiveness of warm introduction offers in generating engagement and potential conversions.