What is conversion rate optimisation (CRO)?
Conversion rate optimisation (CRO), or conversion optimisation, is the process for increasing the percentage of visitors to a website that convert into customers, or more generally, take any desired action on a webpage. It is commonly referred to as CRO.
How does conversion rate optimisation testing work?
Conversion rate optimisation involves several different parts and includes more than just improving your website’s conversion rate. After all, conversion rates are just one metric and the CRO testing process (applied effectively) will allow you to not only increase the proportion of visitors who convert, but also customer value and retention. Ultimately, a CRO strategy helps you to drive revenue and efficiency, increase market share, customer satisfaction and reduce return rates.
The purpose of conversation rate optimisation is to develop data driven decisions based on your customers to generate growth.
The A/B testing process is the method of getting data driven decisions within your conversion rate optimization strategy and must include the following steps:
- Data analysis
- Hypothesis development
- Creating the challlenger
- A/B testing
- Post test analysis
The first step is to gather insights from your data. Companies gather information from website data, loyalty programmes, CRM systems, store purchasing and onsite heat mapping. The aim is to discover 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 forms 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 insight as well as the “what” and the “why” behind the things you uncover.
You’ve worked out where you want to test based on the data and what you want to test 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 quantitative data showed that there was a big drop-off of certain types of customers on your payment page and your qualitative 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, clever 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 are 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, copy writing, consumer psychology, neuromarketing and usability.
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 are also a range of A/B testing tools for a range of budgets which will allow you to run you 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 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 important to take a holistic view and look at more than your website conversion rate when conducting post-test analysis.
What to do if an AB test fails:
- Examine the data. Segment the results to better understand what failed and where. It may have “won” for a particular segment or traffic source for example or failed for a specific browser/OS indicating there may have been a bug. However be careful about sample size if you segment your data.
- Look into whether the test “failed” due to execution. For example, if you’re added an element to a page, was the creative striking enough? Using heat maps would help to identify this or some on-site survey tools will allow you to add test variables to the data collected. This means you can collect some qualitative feedback on your test variations.
- Look into the data to see if there are any potential iterative changes you can make to potentially turn this negative result into a positive one.
- Make sure you have correctly documented the test result, so that when you are looking to implement future tests, you have all the research and results data recorded. This will then feed into future tests. This way your ‘negative’ result isn’t without some positives, as your learnings will feed into future tests.
What to do if an AB test wins:
- If your test is successful, the first thing is start taking steps to put the successful challenger changes onto your live website. In the meantime you can set the challenger to show to 100% of your site’s traffic within the CRO testing tool so that you can reap the rewards straight away.
- From this success, you should look to identify further opportunities to gain more improvements by iterating the successful test.
- Make sure you have correctly documented the test result, so that when you are looking to implement future tests, you have all the research and results data recorded. This will then feed into future tests.
Occasionally, a test won’t generate a positive result or a negative result and will come back as ‘flat’. If this is the case, you should go back to your data to help understand why. Do not see a ‘flat’ result as a failure because after some analysis, it may well have a positive effect in other areas on your site or in your customer journey. Or it might simply have saved future time and resource for your team, developing an idea that wouldn’t have had any impact. Once again, make sure you documented the test result correctly.
Who is conversion rate optimisation (CRO) suitable for?
Basically any business who spends money, time and or resource getting traffic to their website should be spending time converting those potential customers to be effective. It’s not just ecommerce sites that should be doing conversion rate optimization 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).