What is the difference between an experimentation platform and an analytics platform?
An analytics platform records what users did — it observes behaviour passively and surfaces correlations. An experimentation platform establishes why something happened by assigning users to controlled groups and measuring the causal effect of a specific change. You need both, but they answer different questions. Analytics tells you where to look; experimentation tells you what to do about it.
Should we build an experimentation platform or buy one?
The right answer depends on your experiment volume, the sensitivity of your user data, and how tightly the platform needs to integrate with your existing data warehouse and feature-flag infrastructure. Off-the-shelf platforms get you running quickly and suit teams with standard web or mobile surfaces. A custom-built or heavily configured platform makes sense when your data cannot leave your own infrastructure, when you need experiment logic embedded deep in a backend service, or when vendor pricing becomes prohibitive at high traffic volumes. Netguru helps you evaluate both paths honestly before recommending one.
How long does it take to run a first experiment?
With an existing data pipeline and a clear hypothesis, a first experiment can be live within a few weeks. The longer work is building the foundations that make subsequent experiments trustworthy and fast: metric definitions, guardrail metric sets, sample ratio mismatch detection, and a governance process. Teams that invest in those foundations run experiments at a much higher cadence within three to six months.
What does governance look like in practice?
Governance means having a consistent, documented process for every experiment — from hypothesis sign-off through to the deploy decision record. In practice it includes: a shared metric taxonomy so teams measure the same things the same way, a pre-registration step that locks the hypothesis before data is collected, a review gate that checks for sample ratio mismatch and guardrail violations before results are read, and a decision log that records what shipped and why. Without governance, experiment results accumulate but institutional learning does not.
How does Netguru fit into an existing stack?
We work with what you already have. If you use a data warehouse such as BigQuery or Snowflake, we build the experiment assignment and analysis layer on top of it rather than alongside it. If you already have a feature-flag tool, we assess whether it can serve as the assignment layer or whether a dedicated assignment service is needed. Our role is to close the gaps between your existing tools and a trustworthy end-to-end experimentation workflow — not to replace your stack.
What is feature flagging and why is it part of experimentation?
A feature flag is a configuration switch that controls whether a user sees a new behaviour in your product. In an experimentation context, the flag is what enforces the controlled assignment: users in the treatment group have the flag on, users in the control group have it off. The same flag also gives you a kill switch if a live experiment causes unexpected harm, and it supports progressive rollouts — gradually increasing the percentage of users who see a change before committing to a full release. Experimentation without feature flagging forces you to deploy code to run a test, which is slower and riskier.