At Making Science, we are evangelists of Conversion Rate Optimization (CRO). That’s why we decided to write this guide. The guide is split into sections and will be released over the next few weeks. Keep an eye out for upcoming posts.

The CRO Process

The Conversion Rate Optimization Process is here to help us unlock our website’s possibilities, potential, and opportunities. Here at Making Science, our CRO Process involves two key steps:

• Ensures that the change you want to make to your website is Analytically driven, i.e. Data-Driven decision-making to ensure we have facts. Data allows us to be sure we are not just making a decision on a gut feeling or based on the HIPPO (Highest Paid Person’s Opinion) many organizations have.

• Ensures that our team steps into the user’s shoes to see how they feel, i.e. User Experience (UX) is a vital part of the change.

Once we understand the above two key areas, we can start to generate ideas about what to examine. These strategic ideas help us present recommendations for improving some aspects of your website, mobile site, or app. When we start experimenting, conversion rate optimization takes place.

Experimentation within Conversion Rate Optimization

As explained, CRO involves generating ideas or recommendations for elements on your site. They are validated through hypotheses. I know what you’re thinking… That seems more related to science than marketing. But we’re here to tell you otherwise!

Hypothesis = A suggested solution for the possibility, the potential problem, or the challenge found. It’s an opportunity to improve. 

The formula for creating a hypothesis is: “If ____, then ____, because ____.”

Here’s an example of a hypothesis we might form based on data:

Opportunity Identified

​​There is a 25% bounce rate on the search results page. 

Hypothesis

If we change the colour of the “Book Now” button to one that stands out more, people will be more likely to click it because it is more visible.

Methodology

Within our testing tools, we create a Test to see if our hypothesis improves the KPIs we track. For example, based on the above theory, we updated the colour of the book now button to green to draw more attention to it. Our primary metric to track was “clicks on book now” compared to the control (see example below). 

Using a testing tool like Google Optimize, for example, you can track which version is more likely to encourage users to click on book now (the metric specified above). In this case, the variant beat the control implemented for 100% of page visitors.

How to get started with Conversion Rate Optimization

Start by ensuring that you understand the basics of conversion rate optimization. Ensure you have a conversion rate optimization tool and a strategy in place. And, always be testing! Testing allows you to learn.

Talking to us at Making Science can also help you get started. Begin improving your conversion rate today by visiting makingscience.co.uk/contact.

Stay tuned for parts 3-6 in the coming weeks. In the following guide, we’ll discuss different types of testing (A/B Tests vs Multivariate Tests).