Here are some recurring themes I hear from customers who want more than PostHog offers.

I recently sat down with a customer who migrated from PostHog to Statsig. We’ve had a lot of conversations about their needs over the past few months, and some of their criticisms of PostHog struck me.

Author's note: I’m an Account Executive at Statsig, and a big part of my job involves listening to and understanding the needs of our potential customers on a micro level.

The reason my customer’s complaints about PostHog struck me is because they’re common: I’ve heard them enough times that I can give a product demo specifically tailored to addressing complaints about PostHog experimentation.

For the sake of others who may be in the same situation, I’ve compiled these common PostHog issues, along with Statsig’s solutions for them.

Common frustrations with PostHog

My actual notes from calls with PostHog users

Of all the frustrations I hear from PostHog users, these are the most common threads:

Limited experimentation features

PostHog often gets described to me as a good “zero to one” tool but lacks any sort of advanced experimentation features. When customers have a high experimentation velocity and want to build some kind of standardization around it, things start to get challenging.

It’s hard for people who don’t have deep statistical knowledge to glean insights and analyze results effectively, and essential tools like confidence intervals and minimum detectable effect are either newly added or outright missing.

At the end of the day, it can be difficult to get basic insights like “Should we ship this?” or “Which variant is performing the best?”

Manual workarounds

Teams tend to have to perform significant manual work, like defining and tracking metrics from scratch for each experiment.

Users across teams (especially non-data scientists) might not know the ins and outs of metrics available in PostHog, let alone how they’re set up and used. People find themselves creating dashboards and cheat sheets of their key metrics that everyone needs to reference in order to set up or interpret experiments.

There’s also not a standardized metric collection or metrics library.

One recurring theme in chats with ex-PostHog customers is that they want the ability to define a set of metrics—say revenue metrics, for instance—and then run experiments against those metrics or use them as a guardrail in new experimentation.

Restricted metrics view

As of the last time I spoke to someone about this, users are limited to viewing only four or so metrics per experiment, which requires users to do a lot of analysis outside PostHog’s experimentation platform.

For instance, one person I spoke to couldn’t bring data into PostHog from an e-commerce platform and had to use a proxy metric and then match it with e-commerce metrics offline in their data warehouse.

Black box nature

People seem to share the sentiment that PostHog can be a “black box,” offering limited transparency into the deeper data that would enable them to optimize experimentation or feature flags more effectively.

Integration limitations

PostHog seems to struggle to fully integrate with external data sources like Stripe, meaning critical metrics like total revenue can’t be tracked directly, requiring offline workarounds.

Statsig versus Posthog, head to head

Wondering how Statsig stacks up against Posthog in a feature comparison? We've got the data.

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Statsig’s advantages

Based on what I hear from talking to people about experimentation, these are the specific ways that Statsig wins—according to them.

Advanced experimentation features

Statsig supports a more robust experimentation platform with confidence intervals, minimum detectable effect, statistical power, and advanced statistical corrections like Bonferroni, providing a more comprehensive analysis.

Standardized metrics

Unlike PostHog, Statsig allows users to create standardized metrics libraries, which can be reused across experiments, eliminating the need for manual setup and ensuring consistency.

This means no more creating new metrics from scratch every time! 😓

Unlimited metrics and deep analysis

Statsig enables users to track unlimited metrics per experiment and dive deep into customized analysis using funnels, retention, user journeys, and more.

Transparency and flexibility

Statsig offers a fully transparent and customizable experimentation system, empowering engineers and data teams to tweak and view results in real time without the feeling of a "black box."

Data warehouse integration

Statsig offers Warehouse Native solutions, which seamlessly integrate with data warehouses like BigQuery, allowing direct access to critical business metrics such as revenue—all with no need for offline analysis.

Closing thoughts

Migrating to Statsig has helped many teams overcome common frustrations with PostHog, giving them a more robust and transparent experimentation platform and fewer things to worry about.

If any of these resonate with you, or if you’re currently struggling with the limitations of your existing platform, please don’t hesitate to book time on my calendar.

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