Neil KakkarTeam lead
Li Yi Yu
Full Stack Engineer
Makers everywhere get better at building products because of PostHog
Q2 2023 Goals
Objective: Make it easier for engineers to analyze the success of a feature
PostHog’s customers are growth stage startups who don’t really care about specific feature release rules and schedules, but they do care about launching successful features. We want to give them the tools and recommendations to learn faster whether a feature is successful or not.
We aren't yet sure how this looks like, and have opted not to write down concrete features here yet. We'll run experiments, iterate, figure out what sticks, and re-orient if we realise this isn't something people want.
We would have achieved this objective when
- People start their analysing journey with ‘feature success’, rather than an insight/dashboard
- We publish a case study of a customer loving feature success
- This feature itself helps us answer if it's successful
Objective: Launch experiments and feature flags with pricing
The next stage of our feature flags and experiments products is turning them into stand-alone verticals. We are going to launch pricing, which raises the bar for what's acceptable and drives us towards creating a more polished product. This also helps us learn more about what users are really missing, since they'll be more vocal about things they need.
To make this happen, we have four things to focus on:
- Make pricing proposal and support growth team where required with implementation
- Support marketing and website team to make sure our content is world-class
- Make Experiments UI feature complete
- Figure out how a flags-first onboarding flow looks like
We would have achieved this objective when we can create a PostHog account and pay only for flags/experiments.
- Fast, iterative and high output rather not slow and thoughtful - achieving this
- Feedback-driven not spec-driven - we do a decent job at this
- Missionary (we have a clear problem definition and are aligned on how impactful a solution would be) not mercenary - glimpses of this
- Collaborative not lone wolf - glimpses of this
- 20-75 employees
- Series A-D
- Customer type:
- High expectation traits:
- Use the modern data stack
- Frontend uses typescript and react
- API companies
- Shopify stores/no-code companies
- Product-minded front-end engineer
- Growth engineer
- Decision-making seat on product
- Senior engineer
- High expectation traits
- Reads HackerNews
- Educated about the other feature flagging/experimentation tools in the space
- Needs high-reliability and high-performance
- Uses best-in-class tools such as Linear/Figma
- Product Manager
- Backend engineer
Jobs to be done
- Safely rollout frontend features with the least risk
- Persistent feature flags e.g. country/pay gate
- Build/test in production
- Enable beta users to try out experimental features ahead of time
- Test whether a particular feature achieves the desired change in user behavior
You can find out more about the features we own here
Long term vision
Imagine Bob is a product manager, and Alice is an engineer, both of whom love using PostHog.
During their weekly growth review, Posthog shows them that one of their workflows is performing 50% worse than other SaaS companies with a similar flow. They decide to build a new feature together, but they're unsure of the impact, so Bob & Alice decide to gate the feature via a feature flag.
Alice builds the feature and runs the PostHog CLI, automatically converting his feature branch to a feature-flagged version. During creation, he selects the team template they normally use, called "Autorollout based on conversion metric", using the conversion metric that Posthog suggests. The feature progressively rolls out to internal users, then to beta users, then to remaining users. If their conversion metric falls by more than 20% the feature automatically rolls back and alerts their team. Alice requests a feature flag review from Bob.
Bob checks the Posthog UI and because it's such an important feature - adds a safety condition for Sentry errors increasing by 30% and a few counter metrics. This should result in an automatic rollback as well. Bob starts the experiment.
Thankfully, nothing goes wrong when the feature is rolled out. The team is disappointed that the feature doesn't seem to move any of the core company metrics, however. This doesn't fit into either of Alice's or Bob's model, so they dig deeper why this was the case.
Before they even start, PostHog automatically does some impact analysis on their core metrics, and generates some insights into what properties are highly correlated with conversion & which aren't.
As it turns out, people in USA and India love their new feature and show a 40% increase in conversion. Other countries, especially the UK, seem to dislike it so much that it negatively affects conversion. In the end, these forces balance out, leading to similar total conversion rates.
They suspect it might have something to do with their positioning in other countries, so they run a marketing experiment using PostHog, where PostHog automatically generates recommended copy text to try out. It generates 5 variants, and they test these in all countries.
As it turns out, copy wasn't the issue, and there's no significant change here. They watch a few recordings from the experiment to confirm there's nothing off here.
Since it's not a positioning issue, Bob & Alice decide that it makes sense to introduce some personalisation, and let people opt-in to the new feature, and have it on by default for USA and India. They can customise this right from the feature flag, and set this up such that any users who opt-in on their UI automatically get the flag.
PostHog keeps analysing metrics for this flag over time, and notifies Bob and Alice when their customers behaviour change. For example, if the conversion for users in UK has taken a turn for the better, or if enterprise customers have taken a turn for the worse.
Our long term vision is to make all of this possible.
What we're building
Users & recordings linked to feature flags
We want to make it easier for those who use feature flags to get information on users attached to a particular feature flag, and gather more information on those users' experience through session recordings.