Understand how users actually behave on your website or app, then act on it across every team.
At a glance
Product analytics is the discipline of measuring what users do inside a product and using that behaviour to improve metrics which matter: conversion, activation, retention, feature adoption and monetisation. This can take many forms, from finding a UX bug to identifying which features drive long-term engagement with your product.
Historically, most companies need two tools: a session recording tool like Fullstory for qualitative insight and a product analytics tool like Mixpanel or Amplitude for quantitative ones.
However, in 2026, most product analytics tools span both of these categories and may even include other features like onboarding journeys, surveys and experimentation.
Common pitfalls
Engineers ship events, a PM adds more, marketing adds more again and nobody owns the schema. We've seen countless 1000+ event taxonomies where only 100 events are required.
If you don't have common standards across everyone who's creating events, you quickly end up with a mess where no one knows which event to trust.
Without a clear question to answer, analysis goes in circles creating dashboards nobody looks at. On our Product Analytics Training Courses, we focus on knowing what to ask.
The most common challenge we see with product analytics at Metron Growth is that the tools have been in place for a long time with lots of different teams creating events and user traits.
If common standards aren't established and maintained early on, each team does something slightly different. You quickly end up with duplication, inconsistent naming conventions and ultimately data that no one can confidently use.
This is why it's so important to get data governance right from the beginning. Not necessarily a heavy weight process, but enough to introduce consistency across your organisation.
The strategic question
The most important thing for a successful product analytics implementation is starting with the business questions you want to answer. These should tie back to company-level goals like OKRs.
Then establishing common standards and best practises so that each new event builds on what's there already, rather than detracting from it.
Bhavik Patel
Head of Product Analytics
“The team at Metron Growth helped us kick start our Product Analytics roadmap by defining a well thought through strategy that fit the needs of our company… Their work has allowed our product teams to gain a deeper understanding of each of the different product areas and started us on our journey in becoming a truly data-driven product company.”
Product analytics measures how users behave on your website or app so you can improve metrics like conversion, activation, retention and feature adoption. They focus on self-service analytics for rapid feedback loops, so when you have a follow up question you can answer it in minutes without waiting for a data analyst.
We start with the KPIs you need to move and the questions you need to answer: conversion, activation, retention, which features drive upsell etc. Then we work backwards to the events, properties and identity rules that answer those questions. Following the standards we've developed across 100s of real implementations to ensure you get a solid foundation you can build on.
If you already have a tool, you should default to fixing what you have rather than implementing something new. If it's a blank slate, then we'll start with the questions you want to answer and your existing technology stack to figure out which tool will work best for you. There's not one best tool for everyone.
Usually both. For example, in Fullstory we use Fullcapture to get to insights fast then add custom events, page properties and element properties for key events which require structured context.
We start with the business questions your team needs to answer, then work backwards to the events, properties and identity rules that answer those questions. A full taxonomy is usually between 50 and 150 events with consistent naming and typed properties. Usually implemented incrementally in multiple phases. Identity resolution is carefully considered up front on every engagement.
Both. Client-side data is usually the foundation as it's quicker to implement, supports autocapture and enables qualitative insights from session recording. But some events are always lost to ad blockers. So when you need 100% accuracy (e.g. for revenue events) then server side events are essential. Integrating with a CDP like Segment can ensure you have consistent data across product analytics, customer engagement tools and your data warehouse.
Your customer data should be consistent everywhere you analyse it. We're strong advocates for a single source of behavioural data to ensure this consistency. This can mean a CDP like Segment feeding consistent data everywhere. Or, if you don't have a CDP, it can mean syncing the product analytics data to the data warehouse using a product like Fullstory Anywhere Warehouse.
BI tools like Looker, Tableau and Sigma combine a wide variety of sources to provide static, consolidated reporting. This makes them great for things which don't change (e.g. executive dashboards and financial reporting). Product analytics focuses on exploratory behavioural analysis, which is ideal for product, marketing and design teams who need to iterate fast.
Initial implementation usually takes 6-8 weeks. If you have a large product with multiple teams then usually we recommend multiple overlapping phases. If you have an existing broken implementation then we add a 2-4 week audit and planning phase at the beginning.
Get started
30 minutes. No pitch deck.
Prefer email? contact@metrongrowth.com
