Conversion rate optimisation

Our process

Data analysis

The first step is to gather insights from your data. Businesses collect information from website data, loyalty programmes, CRM systems, store purchasing and onsite heat mapping. The aim is to determine which parts of the customer journey are working well and which parts aren’t.

From here you’ll need to add a layer of qualitative data to uncover what motivates or causes certain behaviours that you see in your quantitative data. This could take the form of getting customer services feedback from calls to moderated user research (with many other ways in between). By pulling all your data sources together, you’ll be able to triage insights which are supported from several sources, giving more evidence to the idea as well as the “what” and the “why” behind the things you uncover.

Hypothesis development

You’ve worked out where you want to test based on the data and what you want to check based on why visitors may not be taking the desired action, such as usability, trust, persuasiveness, perceived value, or confusion as a few examples. Now you’ll be able to create your hypothesis. A hypothesis is a statement proposing that by changing X to Y, it will cause Z (effect).

For example, if your data showed that there was a significant drop-off with certain types of customers on your payment page and your data showed the reason was that customers were worried about the security of their data. Your hypothesis could be “by adding trust messaging and signals to the payment page we hypothesise that more customers will complete their payment”.

Now you’ll need to prioritise your hypotheses, taking into account the projected impact, the amount of resource to create the test, and the amount of traffic/time calculated to run the test to ensure you get sound results.

Creating the challenger

It’s time to get creative and use psychology techniques, bright copy or design solutions to create your challenger or “variant”. The challenger will be tested against the existing page/element (known as the control) in your A/B test.

There is a range of ways to develop solutions to your hypothesis such as innovation exercises to collaborative sketching workshops with a cross-section of your business, ideally with those who have expertise in design, copywriting, consumer psychology, neuromarketing and usability.

A/B testing

Setting up your A/B test and configuring tracking allows you to A/B test your hypothesis solution (challenger) against your existing concept (control). Probably one of the hardest elements of the process is setting up the right metrics to measure your test.

With many testing tools, it’s possible to include offline conversion data into your tests. Ensure you’ve got the full picture – e.g. if a visitor sees one of your variations and then converts over the phone ensure this is being tracked. You should consider both micro and macro conversion metrics as this will enhance the learning you can take from the test such as changes in user behaviour which can inspire new hypotheses and influence further tests.

You may require some front end/back end development support depending on the test. There is also a range of A/B testing tools for a variety of budgets which will allow you to run your tests such as Optimizely, Qubit and VWO. You’ll also need to ensure you stop the test at the right time to get statistically sound results.

Post test analysis

Once a test has concluded, it is crucial that you examine the resulting data. If a test was successful, is there a follow-up test to make a further improvement? If it failed, what does this tell you and again, is there a follow-up test with this new insight?

Improving the number of people who purchase a product offers little value if it creates a corresponding increase in product returns; it’s essential to take a holistic view and look at more than your website conversion rate when conducting the post-test analysis.

Would it work for your business?

Any business who spends money, time and or resources getting traffic to their website should be spending time converting those potential customers to be effective.

It’s not just e-commerce sites that should be doing conversion rate optimisation but websites where the purpose is lead generation or even online communities. To do A/B testing, you need a minimum of 2000 conversions per month (to run enough statistically significant tests vs the resource required to test effectively).