Product Analytics Team

Product Analytics Team

See what we're building
At a recent team offsite in Munich, Germany

People

What we're building

  • Data table exploration

    We're building an insights-like search functionality for everything qualitative. That means people, recordings, cohorts, events, and groups.

    This will enable you to search using simple sentences to easily find anything in PostHog. Additionally, you can trigger context-sensitive actions - such as cr...

    Project updates

    No updates yet. Engineers are currently hard at work, so check back soon!

Roadmap

Here’s what we’re considering building next. Vote for your favorites or share a new idea on GitHub.

Recently shipped

The insight builder for Trends queries now includes a new First time for user option, which enables you to find just the first time that users have performed an event.

There are a couple of obvious scenarios where you may want to zoom in on first time events like this, such as if you want to explore user behaviour on activities they'll do multiple times (like building an insight, or sharing an export). It's also handy if you expect individual users to belong to multiple orgs or accounts.

Goals

Q3 2024 objectives

  1. Rock-solid analytics (Thomas + Julian + Sandy + Anirudh)

    1. Legacy Minus – removing legacy insights code so that we can move fast
      • FilterType gone from the frontend.
      • rm -rf posthog/queries/
      • Experiments ported to HogQL.
      • All the flags from HogQL/querying work.
    2. Tests Plus – shipping fewer bugs in the first place
      • Ensure we test with the feature flags that users actually experience, both in end-to-end and integration tests.
      • When shipping changes to queries, replay old vs. new version on thousands of real queries to check for regressions.
    3. Metrics Plus – catching issues before before users report
      • Analytics performance dashboard in Grafana (query duration, failures, etc.). Paging alerts on critical metrics, e.g. if the number of queries drops rapidly, or failures rise.
      • Analytics experience dashboard in PostHog (time till data available, result freshness across insights and subscriptions, refreshes initiated manually vs. automatically, etc.)
      • Alerts on major Product Analytics errors from Sentry, and us acting on every alert. (Bonus: checking up the Sentry routing rules for the #product-analytics team.)
      • Cohorts dashboard in Grafana (successful vs. failed calculations per day, recalculation backlog). Alerts here too.
    4. Performance Plus - eliminating UX pain via maximum query performance/reliability, based on Metrics Plus data
      • Partial calculation of multi-day time series results
      • …and more – work with Team Query Performance to find the lowest-hanging fruit, similarly to Tim's performance mega issue
    5. Support Plus – sparking joy for users when they’re led to report a bug
      • 1 hero + 1 sidekick
      • Goal: 90% of tickets fulfill the SLA
  2. Answering more product questions, deeper (Thomas + Julian + Sandy + Anirudh)

    1. Growth Plus - increasing ease of onboarding, and subsequent retention
      • Identify growth opportunities working with Anna, our product manager – implement growth optimizations and track their impact whenever possible.
      • Work with Team Growth on optimizing the onboarding experience of Product Analytics.
    2. Analysis Plus - answering more product questions, more deeply
      • Analytics alerts are out to users (implemented with the contributor)
      • “Done for the first time” in Trends, to kill the janky First Time Event Plugin
      • Query in new insight URL for instant insight sharing
      • Optional funnel steps
      • ...and more, based on user feedback - see the most requested features in GitHub
  3. ArtificialHog (Michael + Georgiy) – an LLM-based chat-like interface for answering product questions.

Handbook

Who are we building for?

Personas

  • Primary Personas:
    • Product engineer
      • These are the engineers building the product. Normally full-stack engineers skewing frontend or frontend engineers.
      • Product engineers have more limited time. Need to quickly get high-quality insights to inform what they are building and assess what they've shipped.
    • Product manager (ex-engineer type)
      • Supports the product teams (engineers, PMs, designers) to build the best products. They guide the product roadmap by speaking to customers and diving into the data.
      • Product managers are the power-users of analytics (further evidence in the data analysis of paying users). They have desire and the time to go significantly deeper into the data.
  • Limited focus:
    • Growth engineer
  • Not a focus but should be usable by:
    • Everyone in the product team (less technical PMs, designers)
    • Marketing
    • Leadership team

What types of companies?

The highest-performing product teams building the most loved products at high-growth startups. For more context on the company read about the ideal customer persona.

Jobs to be done

Product analytics is a wide tool which fulfills many job-to-be-done (non-exhaustive list):

  • Monitoring KPIs - how are the specific KPIs (product/team/company) doing? Are there any big changes, is everything going roughly in the right direction?
  • Insights into a new feature you've built - I've created a new feature and I want to make sure that it's being used successfully
  • Watching for errors and debugging - something went wrong (error gets trigger, product regression, drop in conversion), getting told it went wrong, debugging it, shipping a solution and making sure that fixes it
  • Conversion optimization - the growth team is monitoring how particular KPIs are doing, trying to come up with experiments, shipping features to try and improve these
  • Answering product strategy questions - which customers should we focus on, what are our most used/valued features. e.g. should we increase the pricing from X to Y? Which customers should we focus on?

You can broadly group the job-to-be-done of Product Analytics in PostHog as:

  • Creating: You have a specific query/dashboard in mind, you open PostHog to view it. E.g. creating a dashboard to Monitor KPIs, or creating the funnel for your onboarding flow
  • Consuming: you or someone else has made something in Posthog that you refer back to. E.g. Checking the dashboard you made to Monitor KPIs
  • Exploring: you're answering a broader open-ended question. E.g. If you're monitoring your KPIs and you see something not right - you then want to dive into understanding why

Roadmap

3 year goals

  • You can explore data across all insights and dimensions
  • You can trivially share any insight anywhere
  • Onboarding is as easy as a video game
  • Tight integration with developer workflows
  • No more complex than it is today
  • Using PostHog sparks joy
  • We support trillion event querying

Feature ownership

You can find out more about the features we own here