How To Set Up Analytics for Web and Mobile Products - Issue 259

Build analytics that scale with your product - a practical guide for product analysts

This publication is for product analysts, digital analysts, marketing analysts, and other growth-focused roles responsible for setting up analytics for web and mobile products, particularly those working with event-based tracking.

This is a follow-up to my (now very old) Introduction to Event-Based Analytics guide, where I did my best to squeeze in 11 weeks of digital analytics course into a single short newsletter.

Today, I want to take it a step further and share my framework and recommendations on how to set up analytics that will scale up, allowing better event tracking for faster product testing and iterations.

Here are a few “groundbreaking” truths about setting up and maintaining event-based analytics:

You need a dedicated owner for event setup and maintenance

While maintaining events requires cross-functional effort (product, analytics, development, and QA), the ownership of the tracking setup and the responsibility for updating the taxonomy, data catalog, and documentation should be assigned to a single role. This role can sit within either the product or data team.

Distributing this ownership across PMs, analysts, or multiple teams slows everyone down, leads to overlapping responsibilities, redundant workflows, and ultimately introduces more gaps in your analytics.

The more experienced this person is, the higher the ROI of your analytics setup. Especially in the early stages, you want someone who understands how to report on the Signup-to-Paid rate, replicate it in a funnel, and knows how to link the conversion to one user, not simply divide the 2 events in the formula.

📌 Related: I’ve been training 3 standout analysts over the past year in event data setup, kicking off taxonomies, and event data maintenance - happy to share introductions if you’re looking for someone!

Event-based analytics ownership is still new and underrated

If your event tracking isn’t trusted, most of your analytics efforts will fail.

Teams are often quick to point out discrepancies for the same metrics between tools or data sources, but reluctant to invest in product analytics.

We refer to this role as a product analyst or digital analyst. This unicorn role requires product intuition, an understanding of the product development lifecycle, knowledge of data and data architecture, and most importantly, strong analytical thinking. This person needs to: