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 an experience, while multivariate testing looks at the impact of multiple changes at once. Both help teams understand what works based on evidence rather than assumptions.

Good experimentation depends on reliable measurement, enough data and a clear testing process. It also needs to connect with the rest of your measurement stack so results can inform broader decisions.

Why it matters

As marketing becomes more automated, experimentation gives organisations an independent way to test decisions before they’re scaled.

Well-designed experiments can improve customer experiences, increase conversion performance and reduce the risk of acting on assumptions.

They can also strengthen broader measurement by providing evidence to support attribution, incrementality and optimisation.

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.