In the fast-paced world of e-commerce, understanding what drives customer engagement is key. One steady method merchants can rely on to boost their site's performance is testing. A/B and multivariate testing (MVT) are two essential strategies that offer clear and actionable insights.

The Simplicity of A/B Testing

A/B testing is simple yet powerful. It boils down to comparing two versions of a page to see which performs better. A straightforward setup makes A/B tests appealing. They're particularly suited for:

• Major single-variable changes
• Scenarios requiring distinct decisions on layout or user flow alterations
• Shopify merchants with limited resources

Consider this: You could A/B test the placement of "free shipping" information—next to the add-to-cart button versus in the header bar. Which will drive the most sales? A/B testing is a great method to start with. It is easier to implement and requires less traffic to complete.

Delving Deeper with Multivariate Testing

Once the basics of A/B testing are conquered, multivariate testing (MVT) paves the way for more intricate insights. MVT excels with:

• Refining established designs
• Concurrently testing multiple page elements
• High-traffic sites

Imagine a bustling fashion e-commerce site testing different section orders on their product detail page. They create multiple variations of product feature placement, call-to-action location and reviews. The aim? Find the combination that maximises conversions. Given they have 90% mobile traffic, they need to utilise space in the best way on mobile.

Harnessing Combined Testing Strategies

A/B and MVT are not mutually exclusive. Together, they pioneer deeper insights. Here is how:

Sequential Testing

Start broad with A/B tests, then refine with MVT. It begins with choosing a layout through A/B and ends with honing details. This method is ideal for stores beginning their optimisation journey. Start with broad A/B tests to identify major wins, then fine-tune with multivariate testing. If you're launching a new website design, use A/B testing to select the best layout. Follow up with MVT to refine colour schemes, text, and imagery.

Parallel Testing

Conduct overarching A/B tests while optimising finer elements with MVT. Perfect for updating user journeys while labelling menu elements. This approach is best for merchants wanting rapid, comprehensive updates. It’s perfect for seasonal campaigns when swift page enhancements are crucial.

Run A/B tests on overarching themes—like holiday sales banners—while simultaneously using MVT for specific elements such as button colours and headline fonts. Be aware that the more you test and change simultaneously, the harder it might be to infer a positive effect on a single change. Parallel testing is only recommended to experts.

Hierarchical Testing

Advance from broad A/B tests to detailed multivariate assessments. Think of it as zooming in—first the big picture, then the specifics. Initially, you might A/B test different interface designs, with successive tests drilling down into finer detail based on earlier results.

This method is suited for stores with established user bases looking to sustain growth with layered insights. Begin with A/B testing to refine broadest navigation paths. Use successive MVT to delve into page-level elements. This method ensures consistent, incremental enhancements over time.

Navigating Hypothesis Testing

When implementing A/B tests, we navigate the hypothesis realm. Our initial stance is the "null hypothesis," asserting no difference exists between versions. The core objective is to gather enough evidence to dismiss this assumption if a real variation exists.

Here's the catch—errors can arise:

• Type I Error: Believing a difference exists when it doesn’t. Imagine overvaluing a new headline due to random chance.
• Type II Error: Overlooking a real difference. Like ignoring a headline that genuinely outshines the original due to weak test results.

Set the standard at a 95% confidence level. This standard minimises Type I errors—wrongly claiming a difference—while balancing Type II errors. A 95% confidence means only a 5% chance of mistakenly seeing a difference. This is a common practice in testing for CRO.

Shopify A/B testing apps like Shoplift, often use this confidence level as a default. It’s a balance, offering merchants reliable insights for informed decisions.

Optimise your testing strategy by embracing the strengths of both approaches. With consistent testing, merchants can unlock new potential and ensure website interactions lead to promising results.