Product Analytics Team

Product Analytics Team

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

People

What we're building

  • Threshold-based alerting

    Users have asked for a functionality to create threshold-based alerts, e.g. when the conversion rate drops below a certain threshold.

    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

Long dashboard lists will now load 93% faster

If, like us, you have a lot of dashboards (412 in our case) then you may have noticed that viewing the main dashboard page can sometimes take a while to load. No more!

All it took was a little bit of complaining in our internal Slack and suddenly Michael couldn't help himself – he made a few tweaks and, voila, dashboard views now load 93% faster.

Goals

Q4 2024 objectives

  1. Legacy 0x - Thomas owning

    • All insights are query-based
    • rm -rf posthog/queries/
    • Insight query is stored in the URL for instant sharing of unsaved insights
  2. First-class environments - Michael owning

    • Environments are rolled out to everyone
  3. Threshold-based alerts - Anirudh owning

    • All users get to set up alerts on PostHog metrics
    • Alerts are launched with Marketing, and positioned into paid plans
    • We're using alerts ourselves for key trends and funnels
  4. Flexible funnels - Sandy owning

    • UDF funnels rolled out to everyone, unlocking arbitrary complexity
    • Allowing parts of funnels to be unordered. No more separation between regular, unordered, and strict funnels
    • Optional funnel steps
  5. 10x onboarding and ease of discovering features - Anirudh and Sandy owning

    • We run an exercise in implementing PostHog in a hobby project. What stands out as painful or unclear?
  6. User delight 2x - support rotation owning

    • Support is 1 hero + 1 sidekick
    • Every week support folks ship those small requested features (see list of requests so far)
    • Goal: 90% of tickets fulfill the SLA
  7. B2B analytics research - Anna owning

    • Understand the market and user needs better in order to improve our current Group analytics product next quarter

See our internal Q4 planning doc for the discussion that led us here.

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