analytics

This workbook introduces the 'double three-layer' framework for event structures, organized by customer, product, and interaction layers. It's designed to help teams build a lean, analyzable event taxonomy that ties directly to business questions. Essential reading for anyone starting a tracking setup from scratch.

This post lays out a practical naming convention for analytics events using the object-action schema. It emphasizes clarity, casing consistency, and dot/underscore delimiters to reduce mystery events and promote scalability. Great starting point for taxonomy design.

This case study shows how Pipedrive split responsibilities for event instrumentation: PMs define, engineers implement, and analysts advise. It walks through their workflow for designing, documenting, and QA'ing event data. A pragmatic guide for collaborative tracking setups.

This guide explains the purpose and structure of a tracking plan, including what to log, where, and why. It includes tactical steps for plan rollout, collaboration, and long-term maintenance. Useful for teams looking to formalize their tracking strategy.

Paul Koullick argues that naming events well is a UX challenge. He lays out conventions for naming (verb-noun, title case, property bias), and introduces a workflow where every new event is reviewed for clarity and governance. Great for scaling teams.

João Lousada outlines how a data governance strategy prevents analytics drift. He emphasizes the role of casing rules, ownership tags, and source-of-truth documentation in keeping instrumentation aligned with product changes. Great for teams scaling analytics without chaos.