At Making Science, we are evangelists of Conversion Rate Optimization (CRO). That’s why we decided to write this guide. It will be published in several parts over the next few weeks, so keep an eye out for upcoming posts.
The CRO Process
The Conversion Rate Optimization Process is here to really help us unlock the possibilities, the potential, and the opportunities that may exist on our website. Here at Making Science, our CRO Process involves 2 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 nor based on the HIPPO (Highest Paid Person’s Opinion) that many organizations have.
- Ensures that our team steps into the shoes of the user to see how they feel i.e. User Experience (UX) is a key part of the change.
Once we understand the above 2 key areas, we can start to generate ideas about what to experiment with. These strategic ideas help us present recommendations for improving certain elements on your website, on your mobile site, or in your 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. These 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 challenge found. It’s an opportunity to improve.
The formula we use for creating a hypothesis is: “If ____, then ____, because ____.”
Here’s an example of a hypothesis we might form, based on data:
There is a 25% bounce rate on the search results page.
If we change the color of the “Book Now” button to one that stands out more, then people will be more likely to click it, because it is more visible.
Within our testing tools, we create a Test to see if our hypothesis improves the KPIs we are tracking. For example, based on the above hypothesis, we updated the color of the book now button to green to draw more attention to it. Our main 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 out the control, so it was 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. Be sure that 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.com/contact!
Stay tuned for parts 3-6 in the coming weeks. Next week, we’ll be discussing different types of testing (A/B Tests vs. Multivariate Tests).