How to create landing page tests that increase conversions

If you’re running an online marketing campaign, you probably want the same thing everybody else does: better performance.

One of the most useful tools in the campaign performance improvement toolbox is landing page optimization (LPO) tests, where you design two different versions of the same page and test them against one another to see which performs best.

But how do you know what to test?

Today, we’ll help you identify what to test to enhance your landing pages, along with real-life examples of businesses that have successfully optimized theirs.

What are landing page optimization tests?

Landing page optimization tests (LPO tests) are tests that you run on a landing page, where you ‘split’ inbound visitors between the original page (control) and a variation to which you’ve applied specific changes. 

An example of a split test using heatmaps to see how users interact with a slightly different page

If the changed version drives more conversions, you can roll out that change for all visitors. From there, you can continue testing other elements to further boost conversions.

How to form LPO test hypotheses

The strongest LPO hypotheses are based on data, rather than hunches or guesswork. 

Here’s how you can incorporate data into your hypotheses:

• Data-driven CRO and split tests usually start with a metric-based observation, such as the average time visitors spend on your page, bounce rate, or number of partial form completions

• Advanced techniques and CRO tools go more in-depth by integrating on-site tracking. This is where you can use tools that collect quantitative and qualitative data, like heatmaps and recorded visitor sessions, as the basis for your hypotheses. You can use this data to refine your user experience, tweak button placements, adjust headline copy, and make plenty of other changes to improve your conversion rate.

Heatmaps show you information like scroll depth and where users focus their attention

Example #1: using heatmaps to inform LPO hypotheses and tests

Sometimes, even small changes to your landing page design can make a big difference in how users engage with your content. 

Ocado is one of the UK’s leading online grocers. Its approach to improving the Christmas promotional page, ‘Christmas Wondermarket,’ provides a great example of using data-driven insights to make impactful changes.

Step 1: analyze user behavior with heatmaps

Heatmaps are a powerful tool for identifying where users are clicking, scrolling, or exiting on your page. 

In Ocado’s case, a zone-based heatmap analysis showed that

• 0.76% of desktop users and 1.24% of mobile users were clicking on the large, unclickable hero image on the page

• Mobile users were particularly frustrated, with many exiting the page without scrolling further

• Scroll depth was low across all devices, indicating that users weren’t engaging with the content below the fold

Heatmaps for Ocado’s desktop and mobile users

This analysis revealed that the hero image was a potential obstacle to user engagement, especially for mobile visitors.

Step 2: hypothesize changes to test

Based on these insights, the team hypothesized that removing the hero image would

• Bring product tiles above the fold, making them more accessible to users, and

• Reduce user frustration and improve overall page engagement

Step 3: test your hypothesis with split testing

To validate this hypothesis, the team ran a test with a variation of the page that removed the large hero banner.

Ocado’s test pages with and without a hero image

The results showed that removing the hero image had no negative impact on order conversion rates. Additionally, secondary metrics, such as engagement with product tiles, improved significantly.

Step 4: implement and iterate

With the test results confirming the hypothesis, Ocado simplified the page by replacing the large hero image with a smaller one and removing category tiles to focus on product tiles.

These changes improved product visibility and helped users quickly find what they were looking for, leading to a better user experience and higher engagement.

Example #2: analyzing the user journey to inform hypotheses and LPO tests

Clarins, a leading luxury skincare and cosmetics brand, demonstrates how understanding the customer journey can help you optimize landing pages for better engagement and conversions.

Step 1: identify friction points in the user journey

When Clarins launched a new product line of Multi-Active day and night moisturizers, the product page wasn’t converting as expected. 

Using Contentsquare’s Journey Analysis tool, the team identified a critical friction point: users frequently navigated between the product listing pages (PLPs) and product detail pages (PDPs). 

Example of Contentsquare’s Journey Analysis tool

The hypothesized reason why?

The product imagery for the day and night moisturizers looked too similar, and users were struggling to distinguish between each product. This difficulty made users bounce before making a purchase.

Step 2: hypothesize changes to test

To resolve this issue, the team hypothesized that clearer visual cues could help users differentiate the products. 

Step 3: test your hypothesis with split testing

Clarins tested adding ‘sun’ and ‘moon’ icons to the product tiles to visually indicate which cream was for day and which was for night.

The team hypothesized that different icons would help users distinguish between the two products

Step 4: implement and iterate

After implementing the changes, Clarins saw a noticeable increase in conversions on the Moisturizers PLP. 

This small but impactful change helped users make faster decisions and improved the shopping experience. 

Confirm your hypotheses with surveys

If your hypothesis is based on tracking data, you’ll have to infer a ‘best guess’ no matter how accurate and intuitive your landing page tracking data is. 

But if you crowdsource your hypotheses and pair it with tracking data, you’re likely to get insights that will lead to more impactful tests right away.

Adding surveys to your LPO toolkit gives you a stronger foundation because your hypotheses come directly from users and customers.

Contentsquare Surveys let you hear directly from your users so you can base your split tests on concrete data rather than guesswork 

Plus, the request for customer feedback might help your users stay engaged and feel appreciated.

Run successful LPO tests with quantitative and qualitative data

Collecting both qualitative and quantitative data is key to running successful LPO tests:

• Use Session Replay to watch renderings of users interacting with your landing pages

• Use Heatmaps to see where users click and scroll and what they ignore

• Use Journey Analysis to identify pages with high drop-off rates

• Use Surveys and other voice of customer tools to gather direct feedback from users

Then, use the data to inform your hypothesis and run successful split tests.

FAQs on LPO tests