Referral Intent Identified in QBRs¶

Definition¶

Referral Intent Identified in QBRs measures the percentage of Quarterly Business Reviews (QBRs) in which customers express interest or willingness to refer your product. It helps track referral readiness and advocacy opportunity among current accounts.

Description¶

Referral Intent Identified in QBRs is a key indicator of strategic relationship strength and advocacy readiness, reflecting how often customers express an interest or willingness to refer during quarterly business reviews (QBRs).

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

In B2B SaaS, it highlights customer confidence and value realization during renewal or expansion conversations
In CS-led orgs, it reflects advocacy moments surfaced during performance reviews
In partner-led motions, it may reveal interest in joining referral or affiliate programs

A high rate suggests customer trust, product satisfaction, and advocacy momentum, while a low rate may indicate missed referral prompts or customer contentment without evangelism. By segmenting by account tier, CSM, or QBR format, you uncover insights to train teams, refine referral CTAs, and identify upsell+referral timing sweet spots.

Referral Intent Identified in QBRs informs:

Strategic decisions, like which accounts to prioritize for referral outreach
Tactical actions, such as adding referral prompts to QBR templates
Operational improvements, including standardizing how referral opportunities are tracked
Cross-functional alignment, connecting CS, product marketing, and lifecycle to drive trusted, high-fit referrals

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

QBR Structure and Listening Skills: Customers often drop referral cues — reps just need to catch them.
CSM Comfort and Initiative: Some reps avoid the ask even when intent is there.
Sentiment and Success Framing: If a customer feels seen and successful, they’re more likely to offer intros.

Improvement Tactics & Quick Wins¶

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

If intent flags are rare, teach reps how to spot buying signals like “I told my team/friend about you.”
Add a “referral readiness” checkbox or sentiment tracker to QBR notes.
Run a campaign offering QBR follow-ups with a referral CTA (“We’d love to meet others like you”).
Refine customer recap decks to include customer quotes and success stats — spark share-worthy pride.
Partner with enablement to role-play referral discovery questions during training.

Required Datapoints to calculate the metric
- Total QBRs Conducted in Time Period
- QBRs Where Referral Intent Was Logged
- CRM or QBR Notes Tagged with Referral Signals
Example to show how the metric is derived

50 QBRs conducted in Q2 17 included referral intent (via CS notes or follow-up actions) Formula: 17 ÷ 50 = 34% Referral Intent Identified in QBRs

Formula¶

Formula

\[ \mathrm{Referral\ Intent\ Identified\ in\ QBRs} = \left( \frac{\mathrm{QBRs\ with\ Referral\ Signals}}{\mathrm{Total\ QBRs\ Conducted}} \right) \times 100 \]

Data Model Definition¶

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

Qbrs

cube(`Qbrs`, {
  sql: `SELECT * FROM qbrs`,

  measures: {
    totalQbrsConducted: {
      sql: `total_qbrs_conducted`,
      type: `sum`,
      title: `Total QBRs Conducted`,
      description: `Total number of Quarterly Business Reviews conducted in the specified time period.`
    },
    qbrsWithReferralIntent: {
      sql: `qbrs_with_referral_intent`,
      type: `sum`,
      title: `QBRs with Referral Intent`,
      description: `Number of QBRs where referral intent was logged.`
    },
    referralIntentPercentage: {
      sql: `100.0 * ${qbrsWithReferralIntent} / NULLIF(${totalQbrsConducted}, 0)`,
      type: `number`,
      title: `Referral Intent Percentage`,
      description: `Percentage of QBRs where customers expressed referral intent.`
    }
  },

  dimensions: {
    id: {
      sql: `id`,
      type: `number`,
      primaryKey: true
    },
    qbrDate: {
      sql: `qbr_date`,
      type: `time`,
      title: `QBR Date`,
      description: `The date when the QBR was conducted.`
    },
    referralSignalTag: {
      sql: `referral_signal_tag`,
      type: `string`,
      title: `Referral Signal Tag`,
      description: `Tags in CRM or QBR notes indicating referral signals.`
    }
  }
});

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.
- CSM Avoidance of Referral Ask: CSMs who avoid asking for referrals can negatively impact Referral Intent.
- Poor QBR Engagement: Low engagement during QBRs can result in missed referral opportunities, reducing Referral Intent.
- Negative Customer Feedback: Negative feedback can decrease the likelihood of customers expressing Referral Intent.
- Inconsistent Product Experience: Inconsistencies in product experience can lead to dissatisfaction, negatively affecting Referral Intent.
- Lack of Customer Success Stories: Without success stories, customers may feel less inclined to refer, reducing Referral Intent.
Positive influences

Factors that push the metric in a favorable direction, supporting growth or improvement.
- QBR Structure and Listening Skills: A well-structured QBR with active listening can capture referral cues, increasing Referral Intent.
- CSM Comfort and Initiative: CSMs who are comfortable and proactive in asking for referrals can boost Referral Intent.
- Sentiment and Success Framing: Positive customer sentiment and framing their success stories can enhance their willingness to refer.
- Customer Satisfaction Score: Higher satisfaction scores often correlate with increased Referral Intent as satisfied customers are more likely to refer.
- Product Usage Frequency: Frequent product usage can lead to higher familiarity and satisfaction, positively impacting Referral Intent.

Involved Roles & Activities¶

Involved Roles

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

Account Management
Customer Advocacy
Customer Success
Customer Lifecycle Management
Product Marketing (PMM)
Activities

Common initiatives or actions associated with this KPI:

QBR Playbooks
Referral Pipeline Sourcing
Customer Marketing
Advocacy Readiness

Funnel Stage & Type¶

AAARRR Funnel Stage

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

Referral
Retention
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 is a forward-looking measure of customer advocacy and satisfaction that predicts future willingness to refer. A high NPS in an account often precedes expressions of referral intent in QBRs, making it a strong early signal.
- Customer Loyalty: Customer loyalty gauges the likelihood of repeat engagement and advocacy, often manifesting as expressed referral intent in strategic conversations like QBRs. High loyalty scores typically foreshadow increased referral readiness.
- Customer Health Score: The Customer Health Score aggregates signals about product usage, satisfaction, and risk, forecasting which accounts are primed for advocacy. Healthy scores indicate accounts that are likely to voice referral intent in QBRs.
- Activation Rate: Accounts with high activation rates are more engaged and realize product value sooner, increasing the probability they will express referral intent during QBRs, as engagement and satisfaction are critical advocacy drivers.
- Product Qualified Accounts: PQAs identify accounts deeply engaged with the product, often correlating with satisfaction and readiness to advocate. PQAs frequently precede and predict referral intent being identified in QBRs.
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 metric shows how many users accept referral prompts when asked, confirming whether referral intent expressed in QBRs translates into concrete action in the referral flow.
- Referral Invitation Rate: Measures actual referral invitations sent, validating and quantifying the downstream impact of referral intent captured in QBRs and the transition from intent to action.
- Referral-Ready Account Rate: Quantifies the share of accounts that internal data deems ready for referral outreach, often based on signals including QBR referral intent, and helps explain broader referral program opportunity.
- Referral Readiness Score: A composite score using engagement, sentiment, and behavioral data to predict referral likelihood; confirms and amplifies the advocacy potential indicated by referral intent in QBRs.
- Referral Discussion Initiation Rate: Measures how often customers initiate referral-related discussions, providing evidence that QBR-expressed intent leads to real advocacy behaviors and quantifies advocacy funnel engagement.