{"id":116080,"date":"2023-12-07T10:12:48","date_gmt":"2023-12-07T10:12:48","guid":{"rendered":"https:\/\/staging.wingify.com\/blog\/dynamic-allocation-sequential-testing\/"},"modified":"2026-09-15T13:38:39","modified_gmt":"2026-09-15T08:08:39","slug":"dynamic-allocation-sequential-testing","status":"publish","type":"post","link":"https:\/\/wingify.com\/blog\/dynamic-allocation-sequential-testing\/","title":{"rendered":"How to Better Handle Collateral Effects of Experimentation: Dynamic Allocation vs Sequential Testing"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\"><span style=\"font-weight: 400\">When talking about web experimentation, the topics that often come up are learning and earning. However, it\u2019s important to remember that a big part of experimentation is encountering risks and losses. Although losses can be a touchy topic, it&#8217;s important to talk about and destigmatize failed tests in experimentation because it encourages problem-solving, thinking outside of your comfort zone and finding ways to mitigate risk.&nbsp;<\/span><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><span style=\"font-weight: 400\">Therefore, we will take a look at the shortcomings of classic hypothesis testing and look into other options. Basic <\/span><a href=\"https:\/\/wingify.com\/blog\/formulate-ab-test-hypothesis\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400\">hypothesis<\/span><\/a><span style=\"font-weight: 400\"> testing follows a rigid protocol:&nbsp;<\/span><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><span style=\"font-weight: 400\">Creating the variation according to the hypothesis<\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400\">Waiting a given amount of time\u00a0<\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400\">Analyzing the result<\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400\">Decision-making (implementing the variant, keeping the original, or proposing a new variant)<\/span><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><span style=\"font-weight: 400\">This rigid protocol and simple approach to testing doesn\u2019t say anything about how to handle losses. This raises the question of <\/span><i><span style=\"font-weight: 400\">what happens if something goes wrong?<\/span><\/i><span style=\"font-weight: 400\"> Additionally, the classic statistical tools used for analysis are not meant to be used before the end of the experiment.<\/span><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><span style=\"font-weight: 400\">If we consider a very general rule of thumb, let\u2019s say that out of every 10 experiments, 8 will be <\/span><a href=\"https:\/\/wingify.com\/blog\/inconclusive-ab-test-results\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400\">neutral<\/span><\/a><span style=\"font-weight: 400\"> (show no real difference), one will be positive, and one will be negative. Practicing classic hypothesis testing suggests that you just accept that as a collateral effect of the optimization process hoping to even it out in the long term. It may feel like crossing a street blindfolded.<\/span><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><span style=\"font-weight: 400\">For many, that may not cut it. Let\u2019s take a look at two approaches that try to better handle this problem:&nbsp;<\/span><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><b>Dynamic allocation<\/b><span style=\"font-weight: 400\"> \u2013 also known as \u201c<\/span><a href=\"https:\/\/wingify.com\/glossary\/multi-armed-bandit\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400\">Multi Armed Bandit<\/span><\/a><span style=\"font-weight: 400\">\u201d (MAB). This is where traffic allocation changes for each variation according to their performance, implicitly lowering the losses.<\/span><\/li>\n\n\n\n<li><b>Sequential testing <\/b><span style=\"font-weight: 400\">\u2013 a method that allows you to stop a test as soon as possible, given a risk aversion threshold.<\/span><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><span style=\"font-weight: 400\">These approaches are statistically sound but they come with their assumptions. We will go through their pros and cons within the context of web optimization.<\/span><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><span style=\"font-weight: 400\">First, we\u2019ll look into the classic version of these two techniques and their properties and give tips on how to mitigate some of their problems and risks. Then, we\u2019ll finish this article with some general advice on which techniques to use depending on the context of the experiment.<\/span><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Dynamic allocation (DA)<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><span style=\"font-weight: 400\">Dynamic allocation&#8217;s main idea is to use statistical formulas that modify the amount of visitors exposed to a variation depending on the variation\u2019s performance.&nbsp;<\/span><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><span style=\"font-weight: 400\">This means a poor-performing variation will end up having little traffic which can be seen as a way to save conversions while still searching for the best-performing variation. Formulas ensure the best compromise between avoiding loss and finding the real best-performing variation. However, this implies a lot of assumptions that are not always met and that make DA a risky option.&nbsp;<\/span><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><span style=\"font-weight: 400\">There are two main concerns, both of which are linked to the time aspect of the experimentation process:&nbsp;<\/span><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><span style=\"font-weight: 400\">The DA formula does not take time into account\u00a0<\/span><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><span style=\"font-weight: 400\">If there is a noticeable delay between the variation exposure and the conversion, the algorithm may go wrong resulting in a visitor being considered a \u2018failure\u2019 until they convert. <\/span><b>This means that the time between a visit and a conversion will be falsely counted as a failure.<\/b><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><span style=\"font-weight: 400\">As a result, the DA will use the wrong conversion information in its formula so that any variation gaining traffic will automatically see a (false) performance drop because it will detect a growing number of non-converting visitors. As a result, traffic to that variation will be reduced.&nbsp;&nbsp;<\/span><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><span style=\"font-weight: 400\">The reverse may also be true: a variation with decreasing traffic will no longer have any new visitors while existing visitors of this variation could eventually convert. In that sense, results would indicate a (false) rise in conversions even when there are no new visitors, which would be highly misleading.<\/span><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><span style=\"font-weight: 400\">DA gained popularity within the advertising industry where the delay between an ad exposure and its potential conversion (a click) is short. That\u2019s why it works perfectly well in this context. <\/span><b>The use of Dynamic Allocation in CRO must be done in a low conversion delay context only.<\/b><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><span style=\"font-weight: 400\">In other words, <\/span><b>DA should only be used in scenarios where visitors convert quickly<\/b><span style=\"font-weight: 400\">. It&#8217;s not recommended for e-commerce except for short-term campaigns such as flash sales or when there\u2019s not enough traffic for a classic AB test. It can also be used if the conversion goal is clicking on an ad on a media website.<\/span><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><span style=\"font-weight: 400\">DA and the different days of the week\u00a0<\/span><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><span style=\"font-weight: 400\">It\u2019s very common to see different visitor behavior depending on the day of the week. Typically, customers may behave differently on weekends than during weekdays.&nbsp;&nbsp;<\/span><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><span style=\"font-weight: 400\">With DA, you may be sampling days unevenly, implicitly giving more weight on some days for some variations. However, you should weigh each day the same because, in reality, you have the same amount of weekdays. You should only <\/span><b>use Dynamic Allocation if you know that the optimized KPI is not sensitive to fluctuations during the week<\/b><span style=\"font-weight: 400\">.<\/span><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><span style=\"font-weight: 400\">The conclusion is that DA should be considered only when you expect too few total visitors for classic <\/span><a href=\"https:\/\/wingify.com\/web-experimentation\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400\">A\/B testing<\/span><\/a><span style=\"font-weight: 400\">. Another requirement is that the KPI under experimentation needs a very short conversion time and no dependence on the day of the week. Taking all this into account: <\/span><b>Dynamic Allocation should not be used as a way to secure conversions.<\/b><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Sequential Testing (ST)<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/wingify.com\/blog\/sequential-testing\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400\">Sequential Testing<\/span><\/a><span style=\"font-weight: 400\"> is when a specific statistical formula is used enabling you to stop an experiment. This will depend on the performance of variations with given guarantees on the risk of false positives.&nbsp;<\/span><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><b>The Sequential Testing approach is designed to secure conversions by stopping a variation as soon as its underperformance is statistically proven.<\/b><span style=\"font-weight: 400\">&nbsp;<\/span><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><span style=\"font-weight: 400\">However, it still has some limitations. When it comes to effect size estimation, the effect size may be wrong in two senses:&nbsp;<\/span><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><span style=\"font-weight: 400\">Bad variations will be seen as worse than they really are. It\u2019s not a problem in CRO because the false positive risk is still guaranteed. This means that in the worst-case scenario, you will discard not a strictly losing variation but maybe just an even one, which still makes sense in CRO.<\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400\">Good variations will be seen as better than they really are. It may be a problem in CRO since not all winning variations are useful for business. The effect size estimation is key to business decision-making. This can easily be mitigated by using sequential testing to stop losing variations only. Winning variations, for their part, should be continued until the planned end of the experiment, ensuring both correct effect size estimation and an even sampling for each day of the week.<\/span><span style=\"font-weight: 400\"><br><br><\/span><b>It\u2019s important to note that not all CRO software use this hybrid approach. <\/b><span style=\"font-weight: 400\">Most of them use ST to stop both winning and losing variations, which is wrong as we\u2019ve just seen.<\/span><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><span style=\"font-weight: 400\">As we\u2019ve seen, by stopping a losing variation in the middle of the week, there\u2019s a risk you may be discarding a possible winning variation.&nbsp;<\/span><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><span style=\"font-weight: 400\">However, to actually have a winning variation after ST has shown that it&#8217;s underperforming, this variation will need to perform so well that it becomes even with the reference. Then, it would also have to perform so well that it outperforms the reference and all that would need to happen in a few days. <\/span><i><span style=\"font-weight: 400\">This scenario is highly unlikely.<\/span><\/i><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><span style=\"font-weight: 400\">Therefore, <\/span><b>it\u2019s safe to stop a losing variation with Sequential Testing<\/b><span style=\"font-weight: 400\">, even if all weekdays haven\u2019t been evenly sampled.<\/span><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>The best of both worlds in CRO&nbsp;<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><span style=\"font-weight: 400\">Dynamic Allocation is the best approach to experimentation instead of static allocation when you expect a small volume of traffic. It should be used only in the context of \u2018short delay KPI\u2019 and with no known weekday effect (for example: flash sales). However, it\u2019s not a way to mitigate risk in a CRO strategy.<\/span><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><span style=\"font-weight: 400\">To be able to run experiments with all the needed guarantees, <\/span><b>you need a hybrid system using Sequential Testing to stop losing variations and a classic method to stop a winning variation.<\/b><span style=\"font-weight: 400\"> This method will allow you to have the best of both worlds.<\/span>\u00a0<\/p>\n","protected":false},"excerpt":{"rendered":"<p>When talking about web experimentation, the topics that often come up are learning and earning. However, it\u2019s important to remember that a big part of experimentation is encountering risks and losses. Although losses can be a touchy topic, it&#8217;s important to talk about and destigmatize failed tests in experimentation because it encourages problem-solving, thinking outside&#8230;<\/p>\n","protected":false},"author":1432,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"post_read_time":0,"footnotes":""},"categories":[10676,1865],"tags":[],"feature":[],"industry-type":[],"product":[],"role":[],"region":[],"class_list":["post-116080","post","type-post","status-publish","format-standard","hentry","category-a-b-testing","category-website-optimization"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>How to Better Handle Collateral Effects of Experimentation: Dynamic Allocation vs Sequential Testing - Wingify Blog<\/title>\n<meta name=\"description\" content=\"We explore sequential testing and dynamic allocation in CRO and which technique to use depending on the context of the experiment.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/wingify.com\/blog\/dynamic-allocation-sequential-testing\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How to Better Handle Collateral Effects of Experimentation: Dynamic Allocation vs Sequential Testing - 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