Menu
In paid advertising, assumptions are expensive. What you think will perform and what actually performs are often very different things — and the only way to know for certain is to test.
A/B ad testing setup creates a structured framework for running meaningful experiments within your campaigns. One variable at a time, tested against a clear hypothesis, with the data to draw reliable conclusions. Over time, those learnings compound — building a progressively clearer picture of what resonates with your audience and what drives the best return.
A/B ad testing setup is the process of creating a structured experiment within a paid advertising campaign to compare the performance of two or more variations of a single element — such as a headline, image, CTA or audience segment. The test is configured within the ad platform to split traffic equally between variations and gather statistically meaningful performance data.
You need this when you’re investing in paid search but don’t know whether your account structure, bidding strategy and keyword selection are as efficient as they could be, when a campaign you’ve run for some time has plateaued, or when you’ve taken over an account from a previous agency and want an objective view of what’s working, what isn’t and what’s been missed.
This service includes a structured review of your PPC account or accounts covering strategy, structure, targeting, creative, bidding, tracking and performance. Identifies specific issues and opportunities with clear prioritisation. Delivered as a PPC audit report with an accompanying action plan.
Most marketing companies focus on channels and tactics.
We focus on reaction.
Before selecting platforms, formats, or media spend, we define how your audience thinks, feels, and decides. We use behavioural psychology to understand what will capture attention, build trust, and motivate action — then choose the channels that best support that outcome.
Every channel we use has a clear purpose, a defined role, and a measurable objective. Nothing is done “because it’s popular” or “because it’s expected”.
The result is marketing that feels natural to engage with, works across multiple channels, and is designed to deliver meaningful, long-term results.
Want to see how this approach works in practice?
The configuration of controlled experiments that compare two or more versions of an ad, landing page, audience or campaign setting against each other to determine which performs better — generating reliable data rather than subjective opinion to guide optimisation decisions.
Ad headlines, descriptions, calls to action, images, video creative, landing page design and copy, audience targeting, bidding strategies, ad extensions and campaign settings such as ad scheduling and device targeting can all be tested in controlled experiments.
Google Ads Campaign Experiments (formerly Drafts and Experiments) allows you to split traffic between a control campaign and a variant, with statistical confidence scoring. Alternatively, running parallel campaigns to the same audience with a single variable changed provides comparable data.
Long enough to achieve statistical significance — typically until each variant has at least 100 conversions or 1,000 clicks, whichever comes first. The right duration depends on traffic volume. A test paused too early is worse than no test, because it provides false confidence.
Statistical significance is the threshold at which the difference in performance between variants is unlikely to be due to chance. A 95% confidence level is the standard threshold — it means there is a less than 5% probability that the result occurred by random variation.
No. Testing one variable at a time is the standard approach for clean data. When multiple variables change simultaneously, you can’t know which change caused the performance difference. Multivariate testing (testing combinations) requires much higher traffic volumes to produce reliable results.
Pause it and apply the learnings to subsequent tests. A losing variant still provides useful information about what doesn’t work, which is as valuable as knowing what does. Document test results systematically so learnings inform future creative and copy decisions.
Prioritise tests that address the largest performance gaps and where a change would have the biggest impact. Headline testing in a low-CTR campaign and landing page testing in a high-CTR but low-conversion campaign are typically high-priority starting points.
Yes. Testing the same creative against different audience definitions — different interest categories, lookalike percentage thresholds, demographic segments — identifies which audience characteristics produce the best results.
Ending tests too early based on early results. Performance in the first few days is rarely representative of eventual performance. Applying learnings from underpowered tests propagates bad decisions rather than good ones.