Product-Engaged Leads (PELs) (PEL)¶

Definition¶

Product-Engaged Leads (PELs) are users or accounts that demonstrate meaningful in-product behavior indicating buying intent or readiness for sales outreach. It helps connect product usage signals with sales qualification criteria.

Description¶

Product-Engaged Leads (PELs) are a key indicator of pipeline readiness and in-product intent, reflecting how users or accounts cross usage thresholds that signal qualification and sales readiness.

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

In freemium PLG, it highlights users who hit usage milestones, invite teammates, or activate key features
In sales-assisted trials, it reflects accounts with multi-user activity, repeated logins, or integration setup
In enterprise onboarding, it tracks early-stage usage that correlates with high-likelihood conversion

A rising PEL trend indicates high pipeline potential and engagement-driven qualification. A drop may reveal onboarding gaps or low product motivation. By segmenting by cohort — such as persona, plan tier, signup source, or feature path — you unlock insights to improve scoring models, optimize sales alerts, and align outreach timing.

Product-Engaged Leads (PELs) inform:

Strategic decisions, like pipeline forecasting and sales prioritization
Tactical actions, such as triggering rep outreach, launching nurture tracks, or refining playbooks
Operational improvements, including onboarding flow iteration and product usage tagging
Cross-functional alignment, by connecting signals across product, sales, lifecycle, and RevOps for scalable, intent-based pipeline growth

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

Usage Depth and Frequency: A PEL who logs in once isn’t as hot as one who returns for 3+ sessions or hits specific features.
Drop-Off Timing and Friction: PELs often stall before onboarding completion or at upgrade paywalls.
Lead Source Alignment: Certain acquisition channels (e.g., organic or referral) generate better-fit PELs.

Improvement Tactics & Quick Wins¶

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

If PELs aren’t converting, score them by engagement type and route high-potential ones to sales or nurture flows.
Add triggered lifecycle sequences that mirror user actions (“Noticed you’ve explored X — want to go deeper?”).
Run a test with a 1:1 outreach from a product specialist for top 10% of engaged free users.
Refine your in-product CTAs for trial-to-paid conversion — make the upgrade benefits obvious and timely.
Partner with product to track top pre-conversion actions and double down on those moments.

Required Datapoints to calculate the metric
- Usage Events by Lead or Account (based on PEL criteria)
- Qualified Lead/User IDs
- PEL Definition Rules (set with sales/product)
- Tracking Window (weekly/monthly/quarterly)
Example to show how the metric is derived

Criteria: 2+ core feature uses + 1 team invite in 7 days 112 users qualified as PELs in February

Formula¶

Formula

\[ \mathrm{Product\text{-}Engaged\ Leads\ (PELs)} = \mathrm{Count\ of\ Users\ or\ Accounts\ Meeting\ PEL\ Criteria\ in\ Period} \]

Data Model Definition¶

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

ProductEngagedLeads

cube('ProductEngagedLeads', {
  sql: `SELECT * FROM product_engaged_leads`,

  measures: {
    count: {
      sql: `lead_id`,
      type: 'count',
      title: 'Total Product-Engaged Leads',
      description: 'Counts the total number of product-engaged leads based on defined criteria.'
    }
  },

  dimensions: {
    leadId: {
      sql: `lead_id`,
      type: 'string',
      primaryKey: true,
      title: 'Lead ID',
      description: 'Unique identifier for each lead.'
    },

    accountId: {
      sql: `account_id`,
      type: 'string',
      title: 'Account ID',
      description: 'Unique identifier for each account associated with the lead.'
    },

    eventTime: {
      sql: `event_time`,
      type: 'time',
      title: 'Event Time',
      description: 'Timestamp of the usage event.'
    },

    pelCriteria: {
      sql: `pel_criteria`,
      type: 'string',
      title: 'PEL Criteria',
      description: 'Criteria used to define a product-engaged lead.'
    },

    trackingWindow: {
      sql: `tracking_window`,
      type: 'string',
      title: 'Tracking Window',
      description: 'Time window for tracking product engagement (e.g., weekly, monthly, quarterly).'
    }
  }
});

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.
- Drop-Off Timing and Friction: High drop-off rates during onboarding or at upgrade paywalls negatively impact PELs, as they indicate barriers to engagement.
- Infrequent Product Usage: Low frequency of product usage suggests weak engagement, reducing the likelihood of a lead becoming a PEL.
- Misaligned Lead Sources: Leads from poorly aligned acquisition channels often show lower engagement, negatively affecting PEL conversion.
- Complexity of Product Features: Overly complex features can deter engagement, leading to fewer PELs as users struggle to realize product value.
- Delayed Response Times: Slow response times from support or sales teams can frustrate users, decreasing their likelihood of becoming PELs.
Positive influences

Factors that push the metric in a favorable direction, supporting growth or improvement.
- Usage Depth and Frequency: Higher frequency of product usage and deeper engagement with features correlate with increased PELs, as these behaviors indicate stronger buying intent.
- Lead Source Alignment: PELs originating from organic or referral channels tend to have higher engagement, suggesting these sources align better with product value propositions.
- Feature Adoption: Adoption of key features is a strong indicator of product value realization, leading to higher PEL conversion rates.
- User Onboarding Completion: Successful completion of onboarding processes is positively correlated with PELs, as it reduces friction and enhances user engagement.
- Customer Feedback and Interaction: Active feedback and interaction with customer support or success teams often indicate higher engagement and readiness to convert.

Involved Roles & Activities¶

Involved Roles

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

Data & Analytics
Growth
Product Management (PM)
Product Marketing (PMM)
Revenue Operations
Sales Manager
Activities

Common initiatives or actions associated with this KPI:

Sales-Assisted PLG
PQL/PEL Scoring
Activation Strategy
Outbound Prioritization
Behavioral Insights

Funnel Stage & Type¶

AAARRR Funnel Stage

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

Activation
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 (PQLs) capture early, high-intent product behaviors at the individual level. A surge in PQLs is a strong precursor to future increases in Product-Engaged Leads (PELs), as these users are most likely to become sales-ready accounts through continued engagement.
- Product Qualified Accounts: Product Qualified Accounts (PQAs) represent organizations with multiple users showing meaningful engagement, indicating organizational buying readiness. An increase in PQAs typically forecasts an upcoming rise in PELs, as more accounts reach key in-product milestones signaling sales-readiness.
- Activation Rate: Higher Activation Rates mean more users are experiencing core product value early, which increases the pool of users likely to demonstrate product-engaged behaviors. This directly leads to a greater number of PELs as more users move deeper into the funnel.
- Monthly Active Users: Growth in Monthly Active Users (MAU) expands the top of the engagement funnel. More MAUs translate into more opportunities for users to exhibit product-engaged behaviors, thus increasing the likelihood of future PEL growth.
- Deal Velocity: Fast-moving deals are often driven by leads demonstrating strong product engagement. When deal velocity increases, it typically signals a higher volume of sales-ready leads entering the pipeline—reflecting an underlying growth in PELs driven by in-product activity.
Lagging

These lagging indicators confirm, quantify, or amplify this KPI and help explain the broader business impact on this KPI after the fact.
- Conversion Rate: Conversion Rate quantifies what percentage of engaged leads (including PELs) actually complete desired actions, such as signing up or purchasing. Analyzing conversion alongside PELs reveals how efficiently product engagement translates into revenue or customer acquisition.
- Customer Engagement Score: Customer Engagement Score aggregates post-engagement behaviors (frequency, depth, recency), validating whether PELs continue to show meaningful activity. A high engagement score among PELs confirms the quality of lead qualification and the likelihood of successful sales outreach.
- Trial Sign-Up Rate: Trial Sign-Up Rate measures the inflow of users starting a product trial, helping explain the volume of new PELs. An uptick in trial sign-ups often leads to growth in PELs, while a decline may signal a future dip in product-engaged leads.
- Activation Cohort Retention Rate (Day 7/30): This retention metric shows what proportion of users who reach activation (often a precursor to becoming PELs) continue using the product after 7 or 30 days. High retention in these cohorts validates that PELs are not just one-time engagers but have sustained value realization.
- Percent of Accounts Reaching Product-Qualified Lead (PQL) Status: This metric quantifies the share of accounts that meet PQL criteria, providing a broader context for the volume and rate of PELs generated. A high percentage indicates an effective product-led qualification process feeding into the PEL pipeline.