Makers everywhere get better at building products because of PostHog
Q4 2023 Goals
- Project "NoteForce Exploration 3000" (Michael, Thomas)
- Pair up with Team Monitoring to complete projects Data Exploration, Notebooks and PostHog 3000.
- Release new navigation and insight editor.
- Work more HogQL prompts into the interface.
- Build metrics into the system, introduce Metric Mondays.
- Project "Finish the darned Query Engine" (Tom, Marius)
- Finish insight HogQL conversion.
- Support non-event data sources on HogQL insights (BI tools for data warehouse).
- Build tools to measure HogQL query performance, and catch regressions before they hit users.
Who are we building for?
- 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.
- Product engineer
- Limited focus:
- Growth engineer
- Not a focus but should be usable by:
- Everyone in the product team (less technical PMs, designers)
- 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
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
You can find out more about the features we own here
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 creating cohorts from results.
- Concept: Universal search for people / recordings / cohorts / events / groups
- feat(product-analytics): Data Exploration view
- RFC: Data exploration