Does an online store with One Step Checkout sell more or not?<\/h2>\n
After two years of collecting and processing A\/B test data from 50 online stores across different industries, we can base our answer not just on an intuition that logic might suggest, but on reliable data. And the answer is no. Having a one-step or multi-step checkout does not directly affect an online store\u2019s conversions.<\/p>\n
Many of us will have seen specific cases where a checkout change improved an eCommerce store\u2019s sales conversion, but can we attribute those improvements solely to the change of system?<\/p>\n
In one of our recent projects, which involved a change in the opposite direction to these trends (from one-step to multi-step), the result was a slight increase in conversion. If we only looked at the surface, we could draw the wrong conclusion. In this specific case, the conversion increase was due to other improvements related to rate and discount assignment, as well as other functional and usability improvements.<\/p>\n
Sometimes, this is where the main problem lies:<\/p>\n
We tend to compare processes under unequal conditions<\/strong><\/p>\n Why? <\/strong><\/p>\n Because you can\u2019t compare different audiences, different time periods or different campaigns. If we compare under unequal conditions, we\u2019ll always find examples that tip the scales one way or the other, which can be used at will to claim the absolute truth.<\/p>\n Let\u2019s return to logic and math, where we feel most comfortable because there\u2019s data, not hypotheses. It\u2019s also where we can see, under equal conditions, that the numbers never lie.<\/p>\n NOTE: This data comes from a sample of 50 stores divided into 4 industries. The stores have different traffic and revenue volumes to provide maximum reliability and breadth of data, but they don\u2019t necessarily reflect the overall online conversion rates of each market sector, nor is that the aim of this study.<\/em><\/p>\n Here\u2019s a simple exercise for anyone with curiosity and time: have your store run an A\/B test on your visitors without changing absolutely anything for either group. In other words, just assign 50% to one group and the other 50% to another. After a few weeks, go into Analytics and pull some data from this split, and you\u2019ll be surprised by the result.<\/p>\n Mathematically, this is easy to explain: if your target audience as a whole has a given statistical behavior, and you study just half of that same audience under the same circumstances, the statistical result of its behavior must logically be identical. So it\u2019s normal for both groups to show very similar results.<\/p>\n At this point, what the study set out to show was that with the same target audience and the same time period, splitting users into 2 groups of 50% each with a different checkout, while keeping the same user form fields and payment and shipping options, the conversion results of both groups would be very similar, with no clear winner as the system that converts more and better.<\/p>\n As mentioned earlier, the study was carried out with a sample of 50 eCommerce stores from each of the industries listed, using data from January 2023 to October 2024. All stores had a responsive design and split their users 50\/50 between the two checkout processes. The result is the average conversion of each group in each industry.<\/p>\n So now, let\u2019s get to the numbers:<\/p>\n As the chart shows (setting aside the conversion differences between industries, which aren\u2019t the subject of this study), conversion rates are very similar within each industry. One Step Checkout has a slight edge in Food & beverage, while the opposite is true in the other industries studied, but the differences are always minor.<\/p>\n And what happens if we split these numbers by device?<\/p>\n (OSC = One Step Checkout, MSC = Multi Step Checkout)<\/em><\/p>\n As expected, when we split the results by device type, they are once again very similar to each other. This makes sense given the premise mentioned earlier about splitting statistical results under equal conditions.<\/p>\n It is clear that the checkout process itself does not necessarily influence our users\u2019 purchase decision, as long as it is optimized and works properly. On our blog you will find several tips on how to improve and optimize a checkout process that may be useful.<\/p>\n In short, there are several factors that can directly influence users\u2019 purchase decisions, such as:<\/p>\n\n
Let\u2019s compare on equal terms: the numbers don\u2019t lie<\/h2>\n
The truth about one-page checkout: it converts the same<\/h2>\n
![\"Conversions](\"https:\/\/trilogi.com\/blog\/wp-content\/uploads\/sites\/2\/img_checkout_1-retoc.png\")
<\/p>\n![\"eCommerce](\"https:\/\/trilogi.com\/blog\/wp-content\/uploads\/sites\/2\/img_checkout_2_retoc.png\")
<\/p>\nSo which checkout process should I choose for my online store?<\/h2>\n