Product-led growth data guide for growth engineers

A structured reading path from event tracking to churn prediction. 10 articles that help you build the data infrastructure behind product-led growth.

Step 1: Fundamentals

Field-level event tracking showing changed data values

How event tracking captures product usage signals

Event tracking is the foundation of any PLG data stack. Understand how your app already captures the behavioral signals that power lead scoring and segmentation.

User list segmented by behavioral data and click events

Behavioral data types that drive product-led growth

Growth loops depend on behavioral data: logins, feature adoption, usage patterns. Know what your product database already captures before adding new instrumentation.

Real-time bidirectional sync between two connected apps

Why growth loops break when data arrives late

A PQL signal that arrives a day late is a missed conversion. Understand how real-time sync keeps growth tools current without building streaming infrastructure.

Step 2: Building Skills

User list divided into product-led growth segments

Segmenting users by product behavior for PLG motions

Growth engineers segment by what users do, not who they are. Learn to build product-usage segments that identify expansion candidates and churn risks.

Product-qualified leads highlighted in customer scoring list

Identifying product-qualified leads from usage data

PQLs convert at 5-6x the rate of MQLs. Learn which product usage thresholds signal buying intent and how to surface them in your CRM automatically.

Data activation flow from database to marketing tool

Getting product signals into CRM, email, and growth tools

Product data sitting in your database doesn't drive growth. Data activation puts usage signals where your sales and marketing teams can act on them.

Step 3: Advanced Strategy

Lead scoring list with high-scoring leads highlighted

Scoring leads with behavioral and product usage data

Lead scoring models fail when the data they need is scattered across tools. Understand how to combine demographic, behavioral, and billing data into a single score.

Customer list with propensity scores and risk indicators

Predicting conversion and expansion with propensity models

Propensity models predict which users will convert, expand, or churn. Learn the four model types and why most fail before the math even starts.

Customer list with high-churn risk row highlighted in red

Identifying at-risk accounts before they leave

Churn signals are simple: declining logins, failed payments, rising support tickets. The hard part is getting those signals into one place so you can act on them.

Customer list ranked by lifetime value with top customers highlighted

Measuring customer lifetime value for growth engineering

CLV tells you which growth motions actually work. Understand how to calculate it from billing and usage data synced across your tools.