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A curated collection of validated resources on how tracking systems work, how they break, and how to diagnose issues across marketing platforms.
Created by Barbara Galiza and Timo Dechau of Propel. Last update: September 2026.
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https://amplitude.com/blog/create-tracking-plan
An actionable guide to creating a structured tracking plan, covering how to define events, standardize data collection, and ensure consistent measurement across analytics and marketing platforms.
https://timo.space/blog/how-to-refactor-your-tracking-design
A practical guide to refactoring an existing tracking setup, outlining different strategies to improve event data design while working within real-world constraints like limited resources and legacy systems.
https://dataanalysis.substack.com/p/introduction-to-event-based-analytics
A guide to event-based analytics that explains how to design, structure, and govern event tracking systems to avoid noisy, unreliable data and enable meaningful user behavior analysis.
https://www.021newsletter.com/p/how-to-improve-paid-media-analysis
How to structure campaign taxonomies so paid media can still be analysed after the fact. Group creative by hypothesis before launch, and carry platform IDs in naming conventions rather than names, because names get changed and IDs don't.
https://measureschool.com/tiktok-event-tracking/
A step-by-step guide to implementing TikTok pixel and event tracking through Google Tag Manager, including how to configure standard events, triggers, and dynamic values for more detailed conversion measurement.
https://measureschool.com/facebook-pixel-events/
A beginner-friendly guide to setting up Meta Pixel events on a website, showing how to install the pixel, configure conversion events (such as leads) using URL-based triggers, and validate tracking through Meta's testing tools.
https://www.simoahava.com/analytics/server-side-tagging-google-tag-manager/
An in-depth explanation of server-side tagging in Google Tag Manager, covering how it works, why it represents a major shift in data collection and control, and the trade-offs between improved data governance, performance, and privacy versus added complexity, cost, and transparency concerns.