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Advocate Re-Engagement Rate

Definition

Advocate Re-Engagement Rate measures the percentage of previously engaged brand advocates who return and participate in a new activity (e.g., referral, review, or campaign). It helps assess brand loyalty and the strength of your advocacy program.

Description

Advocate Re-Engagement Rate is a unique lens into customer loyalty and brand affinity, showing how often past brand advocates come back to engage again — whether that’s writing another review, making a second referral, or joining a new campaign.

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

  • In B2B, it might reflect customers who’ve previously left a review or joined a webinar, returning for another advocacy opportunity
  • In DTC or community-led models, it tracks repeat referrals, social shares, or UGC contributions
  • In ambassador programs, it surfaces sustained emotional connection and brand love

A high rate signals that your advocacy program is compelling, rewarding, and sticky. A low rate may reflect burnout, lack of re-engagement mechanisms, or insufficient variety in advocacy paths. Segment by advocacy type, tenure, or engagement level to personalize outreach and optimize advocate journeys.

Advocate Re-Engagement Rate informs:

  • Strategic decisions, like designing tiered or ongoing brand ambassador programs
  • Tactical actions, such as sending follow-up rewards or content to previous advocates
  • Operational improvements, including broadening engagement channels or diversifying asks
  • Cross-functional alignment, by syncing community, CS, and marketing teams on loyalty, not just acquisition

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

  • Recency and Quality of Previous Engagement: Users with high past value perception are more likely to return. But if that experience was stale or transactional, reactivation is harder.
  • Triggers for Relevance and FOMO: Advocates return when there’s something new, relevant, or exciting to check out. No fresh value = no reason to come back.
  • Strength of Relationship With Brand or Community: Advocates who feel part of your mission, not just your product, are more likely to re-engage — especially through content or events.

Improvement Tactics & Quick Wins

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

  • If advocate re-engagement is low, segment them and trigger a campaign highlighting new features, use cases, or community wins.
  • Add social proof or customer spotlights in emails (e.g., “Here’s how another power user just leveled up with [feature]”).
  • Run a test offering early access to beta features or exclusive perks, only to former champions — and track return rates.
  • Refine your community and content strategy to stay top-of-mind, not just during product use, but in between.
  • Partner with CS or customer marketing to build an “alumni” re-engagement track, focused on value reminders and reactivation offers.

  • Required Datapoints to calculate the metric


    • List of Past Advocates: Tagged users who’ve engaged in advocacy.
    • Engaged Advocates This Period: Number of those returning to participate again.
    • Time Frame: Re-engagement window (e.g., within 3 or 6 months).
    • Advocacy Types: Clear list of actions considered valid (e.g., review, share, referral).
  • Example to show how the metric is derived


    In Q1:

    • Past Advocates: 250
    • Re-Engaged with new action: 75
    • Formula: 75 ÷ 250 = 30%

Formula

Formula

\[ \mathrm{Advocate\ Re\text{-}Engagement\ Rate} = \left( \frac{\mathrm{Re\text{-}Engaged\ Advocates}}{\mathrm{Past\ Advocates}} \right) \times 100 \]

Data Model Definition

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

cube(`Advocates`, {
  sql: `SELECT * FROM advocates`,

  measures: {
    engagedAdvocatesThisPeriod: {
      sql: `engaged_advocates_this_period`,
      type: `sum`,
      title: `Engaged Advocates This Period`,
      description: `Number of advocates who have re-engaged in the current period.`
    },
    advocateReEngagementRate: {
      sql: `engaged_advocates_this_period / NULLIF(past_advocates, 0)`,
      type: `number`,
      title: `Advocate Re-Engagement Rate`,
      description: `Percentage of past advocates who have re-engaged in the current period.`
    }
  },

  dimensions: {
    id: {
      sql: `id`,
      type: `string`,
      primaryKey: true,
      title: `ID`,
      description: `Unique identifier for each advocate.`
    },
    pastAdvocates: {
      sql: `past_advocates`,
      type: `number`,
      title: `Past Advocates`,
      description: `Number of advocates who have engaged in the past.`
    },
    advocacyType: {
      sql: `advocacy_type`,
      type: `string`,
      title: `Advocacy Type`,
      description: `Type of advocacy action taken by the advocate.`
    },
    engagementTime: {
      sql: `engagement_time`,
      type: `time`,
      title: `Engagement Time`,
      description: `Time when the advocate engaged.`
    }
  }
})

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 New Content or Activities: Without fresh and engaging content, advocates see no reason to return, leading to a decline in re-engagement rates.
    • Poor Previous Experience: If advocates had a negative or underwhelming experience previously, they are less likely to re-engage, fearing a repeat of the same.
    • Over-communication: Bombarding advocates with too many messages or irrelevant content can lead to disengagement and reduced re-engagement rates.
    • Competitive Offers: Advocates may be swayed by more attractive offers from competitors, reducing their likelihood to re-engage with the original brand.
    • Weak Community Engagement: A lack of active community interaction or support can make advocates feel isolated, decreasing their motivation to re-engage.
  • Positive influences


    Factors that push the metric in a favorable direction, supporting growth or improvement.

    • Recency and Quality of Previous Engagement: Advocates who had a high-value perception from their previous engagement are more likely to return, as they anticipate similar or greater value in future interactions.
    • Triggers for Relevance and FOMO: Introducing new, relevant, or exciting activities creates a sense of urgency and curiosity, encouraging advocates to re-engage.
    • Strength of Relationship With Brand or Community: Advocates who feel a strong connection to the brand's mission or community are more inclined to participate in new activities, driven by a sense of belonging and purpose.
    • Personalized Communication: Tailored messages and offers that resonate with advocates' past behaviors and preferences can significantly boost re-engagement rates.
    • Incentive Programs: Offering rewards or recognition for continued participation can motivate advocates to return and engage in new activities.

Involved Roles & Activities


Funnel Stage & Type

  • AAARRR Funnel Stage


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

    Retention
    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.

    • Customer Loyalty: High customer loyalty signals future increases in Advocate Re-Engagement Rate, as loyal customers are more likely to repeatedly participate in advocacy activities.
    • Activation Rate: A higher activation rate means more users are reaching meaningful engagement milestones, which increases the pool of potential future advocates who can be re-engaged.
    • Stickiness Ratio: A high stickiness ratio (DAU/MAU) indicates that users habitually return to the product, forecasting a stronger likelihood of advocate re-engagement in future cycles.
    • Product Qualified Accounts: A greater number of PQAs suggests more accounts are deeply engaged, increasing the base of users likely to become and remain advocates, thus driving future re-engagement.
    • Net Promoter Score: High NPS reflects willingness to recommend the brand, serving as an early indicator of advocates who will likely re-engage in future campaigns or referral activities.
  • Lagging


    These lagging indicators confirm, quantify, or amplify this KPI and help explain the broader business impact on this KPI after the fact.

    • Referral Program Participation Rate: Higher participation in referral programs demonstrates active advocacy, often preceding or coinciding with increases in Advocate Re-Engagement Rate by showing that advocates are engaging in additional activities.
    • Customer Engagement Score: A high engagement score quantifies the depth of user interaction and helps explain increases in Advocate Re-Engagement Rate as advocates return due to positive experiences.
    • Referral Retention Rate: Measures how well referred customers are retained; a high rate shows that advocacy-driven acquisition leads to deeper ongoing engagement and higher re-engagement among existing advocates.
    • Social Shares: Increased social sharing behavior by advocates confirms and amplifies the impact of advocacy, often coinciding with or following spikes in Advocate Re-Engagement Rate.
    • Customer Feedback Retention Score: Indicates how retention is affected by feedback engagement; if advocates who provide feedback are retained, it confirms and quantifies the role of advocacy in long-term engagement and future re-engagement.