A/B testing

A/B testing isn’t the goal, it’s your evidence. Use A/B testing to validate your customer understanding, your segment choices, and measure the effectiveness of your personalizations against defined goals.

A/B testing essentials

Variation and control
A/B testing allows you to determine what impact your personalizations are having on your site. That’s why you need a variant and a control, with randomized allocation of visitors to each group based on traffic allocation.

Test meaningful hypotheses
You want experiences with meaningful impact. That means using data and insights to create experiences that can change visitor behavior.

Adopt industry-standard practices
Our experience in A/B testing shows that the best results are obtained by restricting the number of variations within an experience, using adequate sample sizes and concentrating on observed trends and not day-to-day fluctuations.

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Optimize your conversion rates

 
 

Optimize key landing page performance
Use templates to quickly alter key messages on select pages to improve performance, without involving IT teams.

Make your homepage more compelling
Use recommendations and social proof so your customers quickly find the products they’ll buy.

CRO your developers will love
Build compelling experiences using our command line interface, native integration with node package manager and server-side capabilities for increased efficiency.

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Prove the value of your efforts

Validate visitor behavior
Draw on quantitative and qualitative insights to create experiences which influence visitors' journeys across your entire digital presence.

Test targeted segments
Craft experiences to target underperforming customer groups or reach out to specific visitor segments, and make sure you’re having an impact.

Understand customer opinions
Test experiences and use surveys to gather first-hand feedback from your visitors, gaining a deep understanding of their preferences and opinions.

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Proven impact. Results you can trust.

Real science
Utilize Qubit’s best-in-class Bayesian statistics engine to understand when an A/B or multivariate test really is having an effect, minimizing poor decisions caused by false positives.

Revenue impact
Accurately assess the impact of your personalized experiences by tracking them against revenue per visitor and revenue per converter.

Smart goals
Set up early indicator metrics such as email sign-up, add-to-basket, and content views, to measure progress throughout the lifetime of a personalization.

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Most winning A/B test results are illusory

Badly performed A/B tests can produce winning results which are more likely to be false than true. Read this report to arm yourself with the concepts you need to cut through the misinformation and deliver meaningful testing.

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