paper with a drawn graph on a table with two pens and a ruler

Step 1: Analyse

For the analysis, you want to know ‘what’ is happening and then ‘why’ this is happening. To find out the ‘what’, you need to look mainly at the available data and figures. Data on how many people are using your product or service, for example. Data on impact and performance. Mostly a lot of dry numbers and data. This includes data on how a product or service works technically. This data in itself says little, but if you know how to put it in context, it becomes invaluable. That is why it is so important to find out the ‘why’. You can find out ‘why’ by doing qualitative research. This can be done in different ways. From e-mail questionnaires to user research, such as usability testing with people from your target group.

Step 2: Prioritise

Almost everything can be tested. Therefore, it is wise to start in the places where you can win the most. What those places are, you should have discovered in step 1. For digital products, they are often the places that are most used or visited. For physical products, they may be the places that received the most feedback during your qualitative research. When prioritising, it is important not to focus on the quantitative data. You use this data mainly as a compass during your search. The most valuable insights are usually to be found in the qualitative data.

Step 3: Testing

After the completion of steps 1 and 2, the basis is laid for the real work. The actual testing and optimising based on the results. Before you can start, you just need to create a clear test plan. This plan ensures that you have clarity about what you are going to test and for what reason. Only then will you be able to draw conclusions from which possible optimisations can be made.

The basis of each test plan is a clearly defined hypothesis. For example, if you have a high drop off in your checkout process, your hypothesis could be: “Our credibility is questioned by our users because they encounter language errors in the checkout”

A good hypothesis is:

  • Testable and measurable
  • Aims to solve usability or conversion problems
  • Provides market insights

Step 4: process insights and start over

Many tests will probably not produce the results you expected. Does this mean that the test was not successful? On the contrary! They are just as valuable as tests that are successful, because they provide valuable knowledge about your users and give you a new baseline for your next research. After each test, successful or not, move on to the next. An optimisation process is never finished. If you don’t keep improving, you will miss out on new opportunities and eventually fall behind your competition. Therefore, never stop optimising.