You spend a lot of time and energy on building a great application, but do you know how it’s actually being used? Using a product analytics tool lets you gain visibility into what your users find helpful so that you can prioritize feature development and optimize customer experience. In this episode PostHog CTO Tim Glaser shares his experience building an open source product analytics platform to make it easier and more accessible to understand your product. He shares the story of how and why PostHog was created, how to incorporate it into your projects, the benefits of providing it as open source, and how it is implemented. If you are tired of fighting with your user analytics tools, or unwilling to entrust your data to a third party, then have a listen and then test out PostHog for yourself.
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- Your host as usual is Tobias Macey and today I’m interviewing Tim Glaser about PostHog, an open source platform for product analytics
- How did you get introduced to Python?
- Can you start by describing what PostHog is and what motivated you to build it?
- What are the goals of PostHog and who are the target audience?
- In the description of PostHog it mentions being a product focused analytics platform, as opposed to session based. What are the meaningful differences between the two?
- Customer analytics is a rather crowded market, with a large number of both commercial and open source offerings (e.g. Google Analytics, Heap, Matomo, Snowplow, etc.). How does PostHog fit in that landscape and what are the differentiating factors that would lead someone to select it over the alternativs?
- For anyone interested in using PostHog, do you offer a migration path from other platforms?
- necessary features for a customer analytics tool
- privacy and security issues around analytics
- How is PostHog implemented and how has its design evolved since you first began building it?
- reason for choosing Python
- benefits of Django
- thoughts on introducing Channels
- option to include it as a pluggable Django app
- integration points
- data lake integration
- challenges of providing understandable statistics and exposing options for detailed analysis
- Having data about how users are interacting with your site or application is interesting, but how does it help in determining the useful actions to drive success?
- business model and project governance
- What are the most complex, complicated, or misunderstood aspects of building a product analytics platform?
- What have you found to be the most interesting, unexpected, or challenging lessons that you have learned in the process of building PostHog?
- When is PostHog the wrong choice?
- What do you have planned for the future of PostHog?
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- DOM == Document Object Model for web pages
- Django Rest Framework
- Kea state management for React.js
- Django Stubs
- Django Channels
- Pluggable Django App
- Data Lake
- Feature Flags
- PostHog Roadmap
- PostHog Employee Handbook
- Matomo (formerly Piwik)