Test optimisation is now a central component of successful projects when it comes to continuously improving digital offerings. Many clients come to us because they are unsure how they can systematically test and implement their ideas. With targeted test optimisation, not only individual elements but also entire processes can be optimised in a measurable and sustainable way. The clear structuring of hypotheses and the clean implementation of experiments play a decisive role here.
Why test optimisation is so important
Test optimisation helps to reduce uncertainties and make decisions based on data. Although many companies collect ideas, they often implement them without clear prioritisation. It is crucial to formulate hypotheses and test them in a targeted manner. This creates a comprehensible process that enables continuous improvements.
Example: An online shop wants to increase the number of newsletter registrations. Instead of making several changes at once, he tests the headline, the button text and the placement of the registration form one after the other. This allows him to see exactly which change has the greatest effect.
Another example: A recruiting platform tests different formulations for job adverts. The test optimisation shows that a shorter, concise description generates more applications than a long, detailed text.
Test optimisation is also used in email marketing. Companies test different subject lines to find out which one achieves the highest open rate.
Test optimisation in everyday practice
Collect and prioritise hypotheses
The first step is to collect ideas. Tools such as Google Sheets or Kanban boards are suitable for this. All team members can enter their suggestions. The ideas are then converted into hypotheses. Each hypothesis should be clearly formulated and contain a measurable objective.
Example: „If we make the call-to-action button on the homepage red, the click rate increases by 10 %.“
Another example: „If we reduce the loading time of the product page by 2 seconds, the bounce rate drops by 15 %.“
Hypotheses can also be made in the area of social media. „If we publish the posts at a certain time, the interaction increases by 20 %.“
Implement and measure test variants
After prioritisation, the hypotheses are converted into test variants. It is important to change only one variable per test. This allows the results to be clearly assigned. The test results are then analysed and documented.
Example: An e-commerce shop tests two different product images. The variant with the lifestyle photo achieves a higher conversion rate than the classic product photo.
Another example: An education platform tests two different landing pages. The variant with the clear value proposition generates more registrations than the variant with lots of details.
Test optimisation is also used in content marketing. Companies test different headlines to find out which one achieves the highest click-through rate.
BEST PRACTICE with one customer (name hidden due to NDA contract) A client from the e-commerce sector wanted to improve the conversion rate of its checkout page. Together, we developed hypotheses for various elements such as button colour, text length and form fields. After several test runs, we were able to increase the conversion rate by 18 %. The test optimisation showed that small, targeted changes can have a big effect.
Test optimisation as a continuous process
Test optimisation is not a one-off procedure, but a continuous process. Regular tests and analysing the results ensure that digital offerings are always up to date. It is important to document the results and learn from previous tests.
Example: A software company regularly tests new features and collects feedback from users. Test optimisation helps to continuously improve the functions.
Another example: A financial platform is testing various formulations for its FAQ page. The test optimisation shows that simple, understandable language generates more user loyalty.
Test optimisation is also used in the mobile app sector. Companies test different layouts to find out which offers the best user experience.
My analysis
Test optimisation is an indispensable tool for continuously improving digital offerings. The systematic approach ensures that decisions are made on the basis of data. Many clients report that they not only achieve measurable success through test optimisation, but also gain more certainty when making decisions. iROI coaching supports test optimisation projects and helps to unleash the full potential of A/B testing.
Further links from the text above:
10-point plan: Getting started with A/B testing
How to optimise content with A/B testing
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