PostHog Product Analytics vs. Web Analytics - How to Track Your Entire User Journey
Most brands are using web analytics when they should be using product analytics and it's costing them critical insights about their users.
In this video, JJ Reynolds (founder of Vision Labs) breaks down the real difference between product analytics and web analytics using real-world examples like ChatGPT, Figma, Salesforce, and PostHog. You'll learn why the line between "web" and "product" is blurrier than you think – and why treating your entire user journey as product analytics gives you a massive competitive advantage.
What you'll learn:
✅ The key difference between product analytics and web analytics
✅ Why PLG (product-led growth) companies like ChatGPT and Figma are 100% product analytics questions
✅ How even enterprise and sales-led brands should think about product analytics
✅ Why logged-in vs logged-out is the only real distinction that matters
✅ How to track anonymous users before they ever sign up
✅ A live PostHog demo showing real-time user behavior tracking
Whether you run a SaaS, a services business, or an enterprise product – this video will change how you think about your analytics stack.
🔗 Want us to implement your product analytics? → visionlabs.com/contact
00:00 – Product analytics vs web analytics: the debate
00:27 – Who is JJ Reynolds & Vision Labs
00:39 – Example 1: Is ChatGPT a product or web analytics question?
01:06 – Breaking down ChatGPT's UTM parameters
02:14 – Why most things are actually product analytics
02:33 – Example 2: Vision Labs (sales-led company)
03:08 – Example 3: Figma (PLG company)
03:44 – Example 4: Salesforce (enterprise)
04:19 – Example 5: PostHog – product or web analytics?
05:02 – The framework: how websites are actually structured
06:01 – The only real distinction: logged in vs logged out
06:14 – Why you need to track users before they sign up
06:54 – Live PostHog demo: Vision Labs real data
07:47 – Real-time activity & anonymous user tracking
08:57 – Building cohorts around your content
09:39 – The key insight: treat the entire journey as product analytics
09:53 – Conclusion & next steps