Conversion Rate Optimisation is the fancy name for analysing your website traffic data to find what is (and isn’t) driving conversions (i.e. signups, sales or enquiries), and then implementing visual website design, layout and website changes based purely on the findings in that data, with the goal of increasing conversions.Once the changes have been implemented and have been running for some time it is important to then repeat the analysis stage to determine the impact of the website changes and assess their effectiveness in driving conversions. The result we want to see is improved conversions.This process is then repeated on an ongoing basis to ensure the website components are all working together to maximise conversions.
In order to understand how users are interacting with your site it is imperative that you have proper measuring tools in place, and can track the users who are (and aren’t) converting:We recommend that the following as a minimum (don't worry our team of developers can do all the tricky stuff for you):
Other tracking options include:
Once you have the tracking installed and have collected a suitable amount of data (3 - 4 weeks is ideal) you should be able to use Google Analytics to get a pretty good insight in to your visitors behaviour.Things to look for include:
Conclusions / assumptions are statements that will be implemented and measured as part of the conversion optimisation process.Examples include:
“The landing page has a high bounce rate because the call to action is not clear and lacks visual emphasis. We will lower the landing page bounce rate by re-phrasing the call to action and increasing its visibility”“There are significant drop-offs in the shipping calculation step of the checkout process - this is because the shipping amount is too high . We will see less drop-offs in the checkout funnel by removing shipping costs and promoting ‘free shipping’“People are not filling in the quote form, this is because the form is too long - We will remove the non-critical form fields”
Next step is to update your website with the changes outlined in your conclusions / assumptions. To reduce the impact of external factors (such as seasonal market trends) we recommend split testing the changes so some of your visitors are shown the ‘updated’ version, and some are shown the ‘old’ version during the testing period.
Review your Google Analytics reports. Did your website have more conversions because of your changes? Great! If not, what went wrong? Make sure you use the data to establish your findings - don’t just guess!
Review your Analytics data, establish new conclusions / assumptions and repeat the process.