Mid Fidelity
Mid Fidelity
Mid Fidelity
Multivariate Test
Overview
A Multivariate Test is a controlled experiment that compares two or more versions of a webpage, product feature or offer to determine which one performs better. This method is crucial for validating which value proposition, pricing model, or call-to-action drives the highest engagement and conversion rates. At Future Foundry, we use Multivariate Tests to eliminate guesswork and make data-backed decisions before scaling a concept. Rather than relying on assumptions, this experiment delivers clear quantitative insights, ensuring that decisions are rooted in actual customer behaviour. It’s especially valuable for digital experiences, marketing campaigns, or refining early-stage product positioning.
A Multivariate Test is a controlled experiment that compares two or more versions of a webpage, product feature or offer to determine which one performs better. This method is crucial for validating which value proposition, pricing model, or call-to-action drives the highest engagement and conversion rates. At Future Foundry, we use Multivariate Tests to eliminate guesswork and make data-backed decisions before scaling a concept. Rather than relying on assumptions, this experiment delivers clear quantitative insights, ensuring that decisions are rooted in actual customer behaviour. It’s especially valuable for digital experiences, marketing campaigns, or refining early-stage product positioning.
A Multivariate Test is a controlled experiment that compares two or more versions of a webpage, product feature or offer to determine which one performs better. This method is crucial for validating which value proposition, pricing model, or call-to-action drives the highest engagement and conversion rates. At Future Foundry, we use Multivariate Tests to eliminate guesswork and make data-backed decisions before scaling a concept. Rather than relying on assumptions, this experiment delivers clear quantitative insights, ensuring that decisions are rooted in actual customer behaviour. It’s especially valuable for digital experiences, marketing campaigns, or refining early-stage product positioning.
Process
We start by identifying the specific customer behaviour we want to improve, such as progressing through a purchase funnel or increasing sign-ups. Next, we create Control A (the original version) and Variant B (a different version with a key change, such as price, messaging, or design). We define a measurable improvement threshold that Variant B must surpass and ensure our sample size is statistically significant. The experiment is then executed by directing 50% of traffic to Control A and 50% to Variant B. Once the test reaches its target audience size, we analyse conversion rates and decide whether Variant B outperformed the control. If so, it replaces Control A, becoming the new standard. If the difference is inconclusive, we refine and run another round of testing.
We start by identifying the specific customer behaviour we want to improve, such as progressing through a purchase funnel or increasing sign-ups. Next, we create Control A (the original version) and Variant B (a different version with a key change, such as price, messaging, or design). We define a measurable improvement threshold that Variant B must surpass and ensure our sample size is statistically significant. The experiment is then executed by directing 50% of traffic to Control A and 50% to Variant B. Once the test reaches its target audience size, we analyse conversion rates and decide whether Variant B outperformed the control. If so, it replaces Control A, becoming the new standard. If the difference is inconclusive, we refine and run another round of testing.
We start by identifying the specific customer behaviour we want to improve, such as progressing through a purchase funnel or increasing sign-ups. Next, we create Control A (the original version) and Variant B (a different version with a key change, such as price, messaging, or design). We define a measurable improvement threshold that Variant B must surpass and ensure our sample size is statistically significant. The experiment is then executed by directing 50% of traffic to Control A and 50% to Variant B. Once the test reaches its target audience size, we analyse conversion rates and decide whether Variant B outperformed the control. If so, it replaces Control A, becoming the new standard. If the difference is inconclusive, we refine and run another round of testing.
Requirements
This experiment requires digital traffic, clear conversion tracking, and an A/B testing tool (Google Optimize, Optimizely, or a similar service). It is most effective when combined with qualitative insights from customer interviews to understand why one version performs better than another.
This experiment requires digital traffic, clear conversion tracking, and an A/B testing tool (Google Optimize, Optimizely, or a similar service). It is most effective when combined with qualitative insights from customer interviews to understand why one version performs better than another.
This experiment requires digital traffic, clear conversion tracking, and an A/B testing tool (Google Optimize, Optimizely, or a similar service). It is most effective when combined with qualitative insights from customer interviews to understand why one version performs better than another.
Discover other experiments
Explore more real-world experiments that have helped teams validate ideas, reduce risk, and accelerate innovation.
