12.4 million € additional revenue: How to manage predictable growth with behavioral psychology & targeted testing — instead of guessing

It's good that you're here. This webinar is for e-commerce teams who know that more traffic is not the answer in 2026. What you do with the existing traffic is decisive. No product presentation, no theory. Real A/B tests, real figures and a system that has generated over 12.4 million € in additional revenue at MADELEINE.

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Webinar details

Date & time: 02.06. from 11 a.m. to 12 p.m..
It's worth being there live:
Experience an interactive session with live votings in the first 45 minutes and use the
last 15 minutes for your questions and specific tips for your shop.

In this webinar, you'll learn:

  • Why 2 out of 3 shop changes don't bring a sales uplift — and which system that changes
  • How behavioral psychology guides purchasing decisions even before the conscious decision is made (and how to use this in a targeted manner)
  • Which specific A/B tests at MADELEINE generated 12.4 million € in additional revenue (and which did not)
  • How to anchor testing as a scalable system, regardless of budget or team size
  • An insight into the experiences from over 4,000 tests for brands such as s.Oliver, INTERSPORT, Parfumdreams and MADELEINE
Thank you. We'll be in touch soon.
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Speakers

Dennis Herzberger is Senior Manager Marketing & Revenue Growth at LEAP. With over 15 years of experience in CRO, experimentation and funnel optimization, he has helped companies from e-commerce, insurance, fashion and banking to systematically increase conversion potential. His focus is on measures that have a measurable effect and do not just sound strategic.

Massimiliano Bissacco is conversion optimization & UX specialist at LEAP. With over 3 years of experience in data-driven testing, he helps fashion, insurance, travel and e-commerce companies to specifically optimize digital experiences through A/B testing and UX analyses. His focus is on better understanding user behavior and deriving sustainable learnings for new optimization approaches from test results.

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