A/B & multivariate testing

Test changes with confidence and understand what genuinely improves performance before rolling them out more widely.

What it is

A/B testing compares two versions of a website, landing page or customer experience to understand which performs better. Multivariate testing goes further, testing multiple changes at the same time to identify which combination has the greatest impact.

But running a test is the easy part. Good experimentation depends on clear hypotheses, reliable measurement, enough data to produce meaningful results and a testing framework that avoids calling winners too early.

When experimentation is connected to analytics and the wider measurement stack, the results can do more than improve a single page. They can inform conversion rate optimisation, customer experience and broader marketing decisions.

Why it matters

Small changes can have a meaningful impact on conversion, but without testing it is difficult to separate genuine improvement from normal variation in performance.

A/B and multivariate testing give organisations a structured way to understand what is actually driving results before changes are rolled out more widely.

This becomes particularly valuable as marketing becomes more automated. Experimentation provides an independent way to test decisions, challenge assumptions and understand whether changes are genuinely improving customer and business outcomes.

Done properly, experimentation can also strengthen attribution, incrementality and optimisation by adding another source of evidence to the wider measurement approach.

What Louder does

Select and implement experimentation platforms that fit your technology stack.
Design statistically robust experiments, including sample size and power calculations.
Implement client-side and server-side testing with accurate measurement.
Integrate experimentation into your wider measurement and analytics environment.
Establish testing roadmaps, governance and knowledge repositories that build organisational learning.

Common challenges

Not enough traffic - Tests are run without the sample size needed to produce reliable results.
Poor experiment design - Unclear hypotheses or success metrics make it difficult to know what the results actually mean.
Measurement inconsistencies - Testing platforms and analytics tools report different outcomes, reducing confidence in the result.
Scaling results too quickly - Changes are rolled out before there is enough evidence that the improvement is genuine.