Thank you: Personalized Next Step
Hypothesis
If we detect the user's OS and show the appropriate action button, then application download will improve because removing the need to choose between platform options reduces post-conversion friction.
Test Results
Key Learning
Problem: Each additional form field adds friction to the thank you, increasing the chance users abandon before completing their submission.
What worked: OS-detecting personalized download CTAs remove decision points post-conversion and improve application download. Removing even small choices at the thank-you moment improves next-step action rates. Validated across 2 related tests. (+7.5% lift)
Takeaway: A meaningful improvement that compounds with other optimizations. Use this win as a foundation for further iteration on adjacent elements.
How to Apply This to Your Site
This experiment demonstrated that thank you: personalized next step can produce a +7.5% improvement in conversions. The test was run on a thank you page page in the e-commerce industry. With 21,910 visitors in the sample, this is a robust result.
Before you test: Consider that personalization tests typically require large sample sizes to detect small effects. Run your test for at least 2 full business cycles to account for weekly traffic patterns.
This result reached 95% statistical confidence, meaning there is a very low probability the observed effect was due to chance. Results at this confidence level are generally considered reliable for making business decisions.
What Was Tested
In this experiment, two app download buttons were tested against a single OS personalized one. In the control, both branded App Store and Google Play buttons were shown statically. Whereas in the variation a single download (stylized consistently with site wide button styles) button was shown depending on the user's operating system. Impact on application download was measured.
Methodology
Build On These Learnings
Save your own experiments, spot winning patterns across your test history, and stop repeating what's already been tried.
Related Experiments
Homepage: Visitor Segmentation by User Type
Problem: Users on the homepage need validation from others before committing — without visible proof of success, they hesitate.
Product-listing: Plans Page: User-Focused Filter Language Increases Plan Selection
Context: Coupon and promo code fields on product-listings can distract users — they leave to hunt for codes, reducing completion rates.
Product: Personalized Next Step
Context: A one-size-fits-all product experience underperforms compared to content tailored to the visitor's context and intent.
General: Personalized Experiences Generate 41% Higher Impact Than Generic
Principle: Personalization — even basic segmentation by traffic source, device, or returning vs new visitor