5 A/B Testing Trends You Shouldn’t Ignore

A/B testing remains one of the best ways to make data-driven decisions for increasing conversions on your site. Most marketers employ a few different types of A/B testing strategies to create the best possible versions of landing pages in terms of conversion performance. 

The conversion optimization landscape is constantly undergoing rapid change, so you have to leverage new technologies to improve user engagement, reduce bounce rates, and acquire more leads and customers.

What’s more, due to growing competition, businesses often struggle to generate highly relevant leads. In this sense, A/B testing also offers value to marketers seeking to find solutions to the following challenges:

• Understanding the needs of your target market, audience pain points and behaviors.
• Increasing time on site, as more attention minutes correlated with more interaction.
• Reducing bounce rates, as bounces lead to higher costs per customer acquisition on ad platforms.
• Improving the ROI of marketing campaigns across all channels.
• Consistently updating website design to improve user experience (UX).
• Optimizing published prices after carefully considering the current price trends and their impact on conversions.
• Making low-risk, data-driven business strategy decisions.

We can confidently say that A/B testing is something that no businesses should ignore. If you are a new business just starting with A/B testing or a major player in the market, you must understand some of the latest trends that are changing A/B testing processes.

Here are the top five A/B testing trends that you shouldn’t ignore.

Cross-Organization Testing

For many companies, A/B testing remains the conversion optimizer’s job. However, we are gradually witnessing a trend where more and more companies are adopting testing in their cultures. Every department – not just the marketing, design or sales departments – is leveraging testing to offer the best product or service experience.

 Nowadays, testing starts even before the main product is launched in the market. The pre-launch testing phase is the first hurdle to jump. After that, feature testing, experience testing, price testing, marketing testing, creative testing, channel testing, and all sorts of other testing are rigorously applied to keep the brand at the forefront. 

The sole emphasis is laid upon customer experience because UX leads to loyalty, and loyalty leads to retention. Companies with the highest retention rates are the ones that succeed in the long run.

Server-Side A/B Testing on Redshift

While it’s easiest to use a SaaS tool to serve up page variants for testing, this means that the tests take place on the end user’s browser instead of your servers, so if you go this route, the experiments will be slower to load, and you won’t be able to track results using your own systems.

This is why many conversion rate optimization pros nowadays prefer running server-side A/B tests and logging results using Amazon Redshift, one of the fastest cloud data warehouses. If you know what you’re doing, you can calculate the significance of your A/B tests in Redshift with whatever user-defined query functions make sense in your situation. 

In the same way that A/B tests are necessary for landing pages when you run Google Ads campaign or Amazon Ads campaign, using server-side A/B testing integrated with a cloud data warehouse helps you to ensure the statistical significance of your A/B test results, so you can select a winning design with greater confidence.

For many business situations, this level of experimenting and analytics will be overkill. On the other hand, if you run a relatively high volume of tests on many landing pages at once, and if you segment large amounts of audience interaction data according to campaign and channel parameters, then it is definitely worth looking into.

Personalization Testing

It’s almost 2021. You simply can’t ignore personalization and its benefits any longers. When you run A/B tests that incorporate dynamic personalization, you serve different versions of your web pages tailored to the individual needs of your audience members. 

If it’s done well, personalization is always able to deliver the most relevant experience to visitors. Here are some of the ways you can leverage personalization in A/B testing:

• Combine offline and online data to create omnichannel personalization to connect with the audiences.
• Use machine learning to use signals from people’s browsing histories to draw conclusions about their preferences and intent.
• Add segmentation based on geography, demographics, psychographics, and behavioral actions.

Device and Geography Testing

Apple postponed the IDFA opt-in until early next year. IDFA, or the Identifier for Advertisers, is a random device identifier that tracks data for advertisers. 

When IDFA opt-in will come into play, advertisers will have to ask for user permission before they can start tracking the data. This is an excellent opportunity for marketers to test new and innovative A/B testing models based on visitor devices and geo-locations, as compared to the traditional cookie-based tracking model. 

CRO Still Rules the Chart

A/B testing remains a widely used tactic for conversion rate optimization. 

The State of A/B Testing report by Invespcro suggests that around 58% of companies are using A/B testing to increase the flow of conversions, while another 35% of companies plan to use it in the coming months. 

Although A/B testing is just a complement of conversion rate optimization, marketers use the term as a synonym for CRO. Companies have understood the relevance of making data-driven decisions, and hence both qualitative and quantitative data is gathered via A/B testing to optimize the conversion funnel.

Final Thoughts

Anything that is not measured can’t be optimized. Campaigns that aren’t optimized leads to lower returns on investment. No matter whether your campaigns are generating profits or running on losses, A/B testing should be a part of your strategy for every campaign.  

A/B testing does not apply only to landing pages. You can implement multivariate tests for any marketing and advertising campaigns. You never know when the smallest tweak will generate the highest returns.