All Latest 615 A/B Tests
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MOST RECENT TESTS
Test #524 on
Jared.com
by
Craig Kistler
Mar 26, 2024
Mobile
Listing
Craig Tested Pattern #79: Product Highlights On Jared.com
In this experiment, additional (discounted) products were shown at the top of category listing pages with a link to see more such products ("View All Price Drops"). Impact on overall sales was measured.
Test #396 on
Depositphotos.com
by
Gleb Hodorovskiy
Feb 13, 2022
Desktop
Listing
Gleb Tested Pattern #124: Confirmed Selection On Depositphotos.com
In this experiment from Conversionrate.store, the framing of the registration message was changed from a generic account creation one to a specific image selected by the user. The experiment started on a listing page of a stock photography / illustration site. The control showed a more generic message with benefits for signing up and making the purchase. Whereas the variation repeated the actual image that customers clicked on from listing pages - establishing continuity as well as providing a reason for signing up. Impact on sales was measured.
Test #133 on
Bing.com
by
Ronny Kohavi
Dec 13, 2017
Desktop
Mobile
Listing
Ronny Tested Pattern #43: Long Titles On Bing.com
In 2012 a Microsoft employee working on Bing had an idea about changing the way the search engine displayed ad headlines. Developing it wouldn’t require much effort—just a few days of an engineer’s time—but it was one of hundreds of ideas proposed, and the program managers deemed it a low priority. So it languished for more than six months, until an engineer, who saw that the cost of writing the code for it would be small, launched a simple online controlled experiment—an A/B test—to assess its impact. Within hours the new headline variation was producing abnormally high revenue, triggering a “too good to be true” alert.
HBR, September–October 2017 Issue, https://hbr.org/2017/09/the-surprising-power-of-online-experiments
Note: This experiment was a solid success and replicated multiple times over a period of months. It worked at Bing and had a profound influence. The only reason why we atributed a 0.25 point (a "Maybe") was because we don't have the exact sample size and conversion data.