All Latest 539 A/B Tests

In this experiment, the pricing of three software plans was consistently framed into a more comparable monthly context. Whereas the control version only showed the total prices for each plan, the variation showed both the total and monthly prices. Impact on sales was measured. 

In this experiment, a two plan pricing plan (standalone product with a higher collection set) was tested against a single plan one (only a collection set). Impact on clicks and total sales was measured.

This experiment replaced a visible coupon field with a dynamic text link that would initially hide the form field. Only after clicking the text link would the coupon form field appear. The translation from German is "Do you have a coupon code? Click here to apply". Impact on completed transactions was measured.

In this experiment, the annual plan was standardized and framed in a monthly price context (during the iOS signup flow). This made the annual plan more comparable to the monthly plan price. Impact on overall sales and annual plan sales was measured.

In this experiment, the annual plan was standardized and framed in a monthly price context. This was more comparable with the monthly plan price above. Please also note that the annual billing price was also kept and clearly stated. Impact on overall sales and annual plan sales was measured.

In this experiment, a pricing plan selector was shifted from the very bottom of a long product page towards (almost) the top. Impact on any transactions or sales was measured.

In this experiment, a single focus signup was transformed into a pricing comparison table. Impact on signups was measured.

In this experiment, the variation added a second total price at the bottom of the checkout screen just above the checkout button. The impact on sales was measured.

This experiment tested the order of purchase plans. The control version sorted the purchase options by the least expensive while the variation sorted them by the most expensive first. Impact on sales and revenue was measured.

In this experiment, a 3 plan vs 2 plan pricing page was shown to potential customers. Impact on sales and revenue were measured.

In this experiment, the only change was an added message at the top of the pricing screen, clarifying that there is an active discount on a yearly plan. The discount was already communicated with a strike-through price on the control version as well. The variation simply emphasized this aggressively. 

In this experiment, the B variation property prices were framed using higher and crossed out price points from 12 months ago - achieving a relative discount. A tooltip was also shown which explained the higher price point on hover. The example in the screenshot translates to "2% less compared to 12 months ago". This high-power experiment measured the number of leads that were generated on property (product) screens.

In this experiment on a casting call site, pricing information was shown beside the application button. This change shows the effect of setting a price expectation and being more clear that the application process is not free.

The core hypothesis of this experiment was that by showing clear availabiltiy (in green text) beside each casting call, more users would apply and become premium members. The experiment reports on two metrics: application starts (the first progression metric), and premium membership sales (merasured a few steps further in the funnel).

In this experiment, the order of pricing plans was rearranged as to show the most expensive one first.

In this experiment, all three prices of each plan were shifted higher for greater visibility.

In this experiment, the three pricing plans were condensed into a single recommended plan (annual), with a secondary option to choose the monthly plan.

In this experiment, the $69 payment was explained as $6 per month over 1 year, and the $89 was explained as $4 per month over 2 years. 

In this experiment, 4 things were adjusted in the variation: the highest pricing plan was shifted to the left, it was set as the default one, the recommendation was also adjusted to point to the highest plan, and one benefit from the lowest plan was removed (customer support).

In this experiment, two pricing plans were inverted to show the most expensive plan first (in the variation).