1. Test only one variable at a time (Ceteris Paribus)<\/strong><\/h2>\n\nA\/B testing must adhere to basic scientific principles, especially the ceteris paribus<\/em> concept: all other things remain the same. If you change multiple elements on a landing page simultaneously \u2013 for example, the header text and the color of the call-to-action (CTA) button \u2013 you cannot say with certainty which change actually led to the increase in conversion. Keep tests simple: always test only one variable at a time<\/strong>. <\/p>\n\n<\/div>\n\n2. Prioritize what to test: Start with big changes<\/strong><\/h2>\n\nWhile the previous tip emphasizes testing one variable, it’s important to prioritize<\/strong> what is being tested. Don’t start by fine-tuning the shade of a button. The biggest differences in conversion rates usually come from broader changes, such as: <\/p>\n\n\n- Page structure:<\/strong> Long sales copy vs. short and concise structure.<\/li>\n\n\n\n
- Visual element:<\/strong> Static image vs. short background video.<\/li>\n\n\n\n
- Value proposition:<\/strong> Different headline options and core message formulation.<\/li>\n<\/ul>\n\n
So start by testing major elements and move to small adjustments only after you’ve found the most effective overall structure.<\/p>\n\n
<\/div>\n\n3. Continuous testing is the foundation of conversion optimization<\/strong><\/h2>\n\nEven small changes can have a surprisingly large impact on conversions over time. Conversion optimization is not a one-time project, but an ongoing process. Therefore, test regularly and make changes based on the results. The more tests you run, the less you leave to chance and the more data you gather for future landing pages and marketing efforts. <\/p>\n\n![\"\"](\"https:\/\/varri.net\/wp-content\/uploads\/2025\/03\/chance_vs_knowledge-1024x576.webp\")
<\/a><\/figure>\n\n<\/div>\n\n4. Don’t just focus on clicks: Measure the revenue of the entire sales funnel<\/strong><\/h2>\n\nLanding pages often collect leads, not direct sales. That’s why it’s important to measure conversion beyond just the lead obtained from the page itself. Try to measure how much revenue<\/strong> or profit<\/strong> different landing pages actually generate. <\/p>\n\nSometimes a page that works with a smaller number of leads (lower conversion rate) can generate more money for the company because it collects higher quality leads. Design your tests so that they extend all the way to the end of the sales funnel. <\/p>\n\n
<\/div>\n\n5. Always define a baseline version (Control)<\/strong><\/h2>\n\nAlways set one test version as the control<\/strong> (Control, Baseline), whose conversion rate is known. All new variations are tested against this control. <\/p>\n\nClearly name this master page and always perform single-variable tests on it. This way, you gradually learn which elements improve conversion and can collect effective solutions to create an increasingly better control version. <\/p>\n\n
<\/div>\n\n6. Achieve statistical significance: Don’t rush decisions<\/strong><\/h2>\n\nDon’t make decisions too hastily. Changes must be allowed to take effect long enough until statistical significance<\/strong> is achieved. <\/p>\n\nStatistical significance means that the difference between test versions is not due to chance. Decisions made with too small a number of visitors are worthless. If traffic is low, let the test run for at least a couple of weeks. If traffic is high, you can check the results more often, for example once a week. <\/p>\n\n
<\/div>\n\n7. Segmentation is the enemy of testing: Test the same audience<\/strong><\/h2>\n\nIn regular A\/B testing, it’s vital that the two different versions (A and B) are shown to the same audience<\/strong>.<\/p>\n\nIf you direct different customer segments (e.g., 25-year-old men from the capital region vs. 50-year-old women from Northern Ostrobothnia) to different test versions, the results will not be comparable. You cannot draw conclusions about whether the difference was due to the version or a fundamental difference in the target group. Therefore, make sure that the visitors to versions A and B are a representative sample of the same target group. <\/p>\n\n![\"\"\/](\"https:\/\/static.wixstatic.com\/media\/8d28af_a4a8bc559044454b9a36fb2dbbf93772~mv2.png\/v1\/fill\/w_624,h_351,al_c,lg_1,q_85\/8d28af_a4a8bc559044454b9a36fb2dbbf93772~mv2.webp\")
<\/figure>\n\n
2. Prioritize what to test: Start with big changes<\/strong><\/h2>\n\nWhile the previous tip emphasizes testing one variable, it’s important to prioritize<\/strong> what is being tested. Don’t start by fine-tuning the shade of a button. The biggest differences in conversion rates usually come from broader changes, such as: <\/p>\n\n\n- Page structure:<\/strong> Long sales copy vs. short and concise structure.<\/li>\n\n\n\n
- Visual element:<\/strong> Static image vs. short background video.<\/li>\n\n\n\n
- Value proposition:<\/strong> Different headline options and core message formulation.<\/li>\n<\/ul>\n\n
So start by testing major elements and move to small adjustments only after you’ve found the most effective overall structure.<\/p>\n\n
<\/div>\n\n3. Continuous testing is the foundation of conversion optimization<\/strong><\/h2>\n\nEven small changes can have a surprisingly large impact on conversions over time. Conversion optimization is not a one-time project, but an ongoing process. Therefore, test regularly and make changes based on the results. The more tests you run, the less you leave to chance and the more data you gather for future landing pages and marketing efforts. <\/p>\n\n<\/a><\/figure>\n\n<\/div>\n\n4. Don’t just focus on clicks: Measure the revenue of the entire sales funnel<\/strong><\/h2>\n\nLanding pages often collect leads, not direct sales. That’s why it’s important to measure conversion beyond just the lead obtained from the page itself. Try to measure how much revenue<\/strong> or profit<\/strong> different landing pages actually generate. <\/p>\n\nSometimes a page that works with a smaller number of leads (lower conversion rate) can generate more money for the company because it collects higher quality leads. Design your tests so that they extend all the way to the end of the sales funnel. <\/p>\n\n
<\/div>\n\n5. Always define a baseline version (Control)<\/strong><\/h2>\n\nAlways set one test version as the control<\/strong> (Control, Baseline), whose conversion rate is known. All new variations are tested against this control. <\/p>\n\nClearly name this master page and always perform single-variable tests on it. This way, you gradually learn which elements improve conversion and can collect effective solutions to create an increasingly better control version. <\/p>\n\n
<\/div>\n\n6. Achieve statistical significance: Don’t rush decisions<\/strong><\/h2>\n\nDon’t make decisions too hastily. Changes must be allowed to take effect long enough until statistical significance<\/strong> is achieved. <\/p>\n\nStatistical significance means that the difference between test versions is not due to chance. Decisions made with too small a number of visitors are worthless. If traffic is low, let the test run for at least a couple of weeks. If traffic is high, you can check the results more often, for example once a week. <\/p>\n\n
<\/div>\n\n7. Segmentation is the enemy of testing: Test the same audience<\/strong><\/h2>\n\nIn regular A\/B testing, it’s vital that the two different versions (A and B) are shown to the same audience<\/strong>.<\/p>\n\nIf you direct different customer segments (e.g., 25-year-old men from the capital region vs. 50-year-old women from Northern Ostrobothnia) to different test versions, the results will not be comparable. You cannot draw conclusions about whether the difference was due to the version or a fundamental difference in the target group. Therefore, make sure that the visitors to versions A and B are a representative sample of the same target group. <\/p>\n\n<\/figure>\n\n
- \n
- Page structure:<\/strong> Long sales copy vs. short and concise structure.<\/li>\n\n\n\n
- Visual element:<\/strong> Static image vs. short background video.<\/li>\n\n\n\n
- Value proposition:<\/strong> Different headline options and core message formulation.<\/li>\n<\/ul>\n\n
So start by testing major elements and move to small adjustments only after you’ve found the most effective overall structure.<\/p>\n\n
<\/div>\n\n3. Continuous testing is the foundation of conversion optimization<\/strong><\/h2>\n\n
Even small changes can have a surprisingly large impact on conversions over time. Conversion optimization is not a one-time project, but an ongoing process. Therefore, test regularly and make changes based on the results. The more tests you run, the less you leave to chance and the more data you gather for future landing pages and marketing efforts. <\/p>\n\n
<\/a><\/figure>\n\n<\/div>\n\n4. Don’t just focus on clicks: Measure the revenue of the entire sales funnel<\/strong><\/h2>\n\n
Landing pages often collect leads, not direct sales. That’s why it’s important to measure conversion beyond just the lead obtained from the page itself. Try to measure how much revenue<\/strong> or profit<\/strong> different landing pages actually generate. <\/p>\n\n
Sometimes a page that works with a smaller number of leads (lower conversion rate) can generate more money for the company because it collects higher quality leads. Design your tests so that they extend all the way to the end of the sales funnel. <\/p>\n\n
<\/div>\n\n5. Always define a baseline version (Control)<\/strong><\/h2>\n\n
Always set one test version as the control<\/strong> (Control, Baseline), whose conversion rate is known. All new variations are tested against this control. <\/p>\n\n
Clearly name this master page and always perform single-variable tests on it. This way, you gradually learn which elements improve conversion and can collect effective solutions to create an increasingly better control version. <\/p>\n\n
<\/div>\n\n6. Achieve statistical significance: Don’t rush decisions<\/strong><\/h2>\n\n
Don’t make decisions too hastily. Changes must be allowed to take effect long enough until statistical significance<\/strong> is achieved. <\/p>\n\n
Statistical significance means that the difference between test versions is not due to chance. Decisions made with too small a number of visitors are worthless. If traffic is low, let the test run for at least a couple of weeks. If traffic is high, you can check the results more often, for example once a week. <\/p>\n\n
<\/div>\n\n7. Segmentation is the enemy of testing: Test the same audience<\/strong><\/h2>\n\n
In regular A\/B testing, it’s vital that the two different versions (A and B) are shown to the same audience<\/strong>.<\/p>\n\n
If you direct different customer segments (e.g., 25-year-old men from the capital region vs. 50-year-old women from Northern Ostrobothnia) to different test versions, the results will not be comparable. You cannot draw conclusions about whether the difference was due to the version or a fundamental difference in the target group. Therefore, make sure that the visitors to versions A and B are a representative sample of the same target group. <\/p>\n\n
<\/figure>\n\n - Visual element:<\/strong> Static image vs. short background video.<\/li>\n\n\n\n
![\"\"](\"https:\/\/varri.net\/wp-content\/uploads\/2025\/03\/chance_vs_knowledge.webp\")
<\/a><\/figure>\n\n