Required Datapoints
- 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)
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.
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:
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:
These are the main factors that directly impact the metric. Understanding these lets you know what levers you can pull to improve the outcome
Actionable ideas to optimize this KPI, from fast, low-effort wins to strategic initiatives that drive measurable impact.
Activities commonly tied to improving or operationalizing this KPI.
| Activity | Description |
|---|---|
| Sales-Assisted PLG | Sales-Assisted PLG centers on leveraging the product experience as the main engine for customer acquisition and expansion, while strategically involving sales teams at pivotal points in the user journey. It helps teams translate strategy into repeatable execution. Relevant KPIs include Product-Engaged Leads (PELs). |
| PQL/PEL Scoring | PQL/PEL Scoring focuses on systematically evaluates users or accounts by analyzing their interactions with a product to assess their likelihood of becoming high-value customers. It turns signals into decisions, interventions, and measurable follow-up. Relevant KPIs include Product-Engaged Leads (PELs). |
| Activation Strategy | Activation Strategy involves designing, implementing, and optimizing strategies to help new users quickly realize value from a product. It gives teams a clear plan for where to focus, how to sequence work, and what to measure. Relevant KPIs include Product-Engaged Leads (PELs). |
| Outbound Prioritization | Outbound Prioritization is the systematic process of evaluating and ranking potential customers or accounts identified through outbound efforts such as cold calls, targeted emails, or social prospecting. It gives teams a clear plan for where to focus, how to sequence work, and what to measure. Relevant KPIs include Product-Engaged Leads (PELs). |
| Behavioral Insights | Behavioral Insights involves systematically collecting, analyzing, and interpreting data on user and customer behaviors across digital touchpoints and sales interactions. It turns signals into decisions, interventions, and measurable follow-up. Relevant KPIs include Product-Engaged Leads (PELs). |
Criteria: 2+ core feature uses + 1 team invite in 7 days
112 users qualified as PELs in February
This KPI is associated with the following stages in the AAARRR (Pirate Metrics) funnel:
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.
This role is directly accountable for the KPI and is expected to drive progress and decisions around it.
These roles contribute directly to performance and typically partner on execution, reporting, or optimization.
These leading indicators influence this KPI and act as early signals that forecast future changes in this KPI.
These lagging indicators confirm, quantify, or amplify this KPI and help explain the broader business impact on this KPI after the fact.
How this KPI is structured in Cube.js, including its key measures, dimensions, and calculation logic for consistent reporting.
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