A self-paced practical course for product managers, data engineers, analysts and developers building the analytics foundation of a SaaS product. Seven lessons cover defining a metrics strategy, writing a tracking plan, instrumenting events in client and server code, building collection pipelines, modelling product data in a warehouse, calculating core SaaS metrics, and running experiments with privacy and governance built in. Each lesson ends with an exercise using your own product or a sample application.
What you will learn
- Define a product metrics strategy and north star metric
- Write and maintain an event tracking plan
- Instrument reliable client and server events
- Build event collection and loading pipelines
- Model users, accounts, sessions and events in a warehouse
- Calculate activation, retention, churn and revenue metrics
- Run experiments and handle consent and privacy correctly
Lessons
- Metrics Strategy
- The Tracking Plan
- Instrumentation
- Collection Pipelines
- Warehouse Modelling for Product Data
- Core SaaS Metrics
- Experimentation, Privacy and Governance
Self-paced course from Tech Skills Library. Educational content only; it is not an accredited qualification, licence or credential.
Lessons
Metrics Strategy
The Tracking Plan
A tracking plan defines which events and properties to collect, with consistent names and definitions. Event design Use an object-action naming convention, such as “Project Created” or “Invite Sent”. Track meaningful actions, not every click. Properties Attach context: plan type, feature, source, and identifiers for user and account. Identity Define how anonymous visitors are linked […]
Instrumentation
Client-side tracking Captures interface interactions but can be blocked by browsers or lost when users leave quickly. Server-side tracking More reliable for important events such as payments, subscription changes and core actions. Prefer server-side for business-critical events. Implementation practices Wrap tracking calls in a shared library enforcing the plan. Validate event schemas in development and […]
Collection Pipelines
Options Use a customer data platform, an open-source event collector or a custom endpoint that writes to a queue and object storage. Pipeline steps Receive events, validate schemas, enrich with context such as geography or account, deduplicate, and load into the warehouse. Reliability Buffer events in queues, retry failures and route invalid events to a […]
Warehouse Modelling for Product Data
Core models Events: one row per event. Users and accounts with attributes and plan history. Sessions derived from events. Daily user and account activity tables. Identity stitching Map anonymous IDs to user IDs so pre-sign-up activity joins post-sign-up activity. Subscription data Model subscription periods with start, end, plan and monthly recurring revenue. Performance Partition event […]
Core SaaS Metrics
Activation Percentage of new users who reach a defined value moment within a time window. Retention Cohort retention shows the share of users or accounts still active in later periods. Analyse by sign-up cohort. Churn Logo churn counts lost customers; revenue churn measures lost recurring revenue. Net revenue retention includes expansion. Revenue metrics Monthly recurring […]
Experimentation, Privacy and Governance
Experiments Define a hypothesis, primary metric, guardrail metrics, sample size and duration before starting. Randomise assignment consistently and analyse results with appropriate statistical methods. Avoid stopping early when results look favourable. Privacy Collect only data you need, respect consent choices, honour deletion requests, avoid sending personal data to tools that do not need it and […]