Using Unique assignment method in a web experiment
The Unique assignment method provides a quick and easy way to automatically assign guests to variants in every classic A/B experiment. It prioritizes speed and quantity over precision, and is especially useful when running multiple tests simultaneously. This method ensures that guests are randomly assigned to variants independent of their assignments in other experiments. The Unique assignment method is the default option for classic A/B experiments in Sitecore Personalize. Alternatively, if you want to apply more control and precision to guest assignment, you can use the Universal assignment method.
An experiment that runs with optimized testing prevents you from selecting the Unique and Universal assignment methods or adjusting how you allocate traffic. This option will automatically allocate 100% of the traffic to your experiment, and dynamically assign guests to the highest performing variants.
When using the Unique assignment method, you need to set the traffic allocation by specifying the proportion of guests entering the experiment. Allocating 100% includes all website traffic, while a lower percentage, like 10%, means only a fraction of guest traffic is participating in the experiment. After the percentage is set, you can assign traffic among the variants. This ensures that of those who enter the experiment, a certain portion of guests will be randomly assigned to the control, and the remainder will be assigned to the other variants. For example, in an experiment aimed at testing ad carousel effectiveness, 80% of the eligible guests would see the current version featuring a three-card ad carousel (Control), and the remaining 20% would see another version with a five-card ad carousel (Variant).
To illustrate how a guest is assigned through this method, consider a website running three different experiments, A, B, and C, each using the Unique assignment method. As shown in the following diagram, each experiment has two variants, consisting of a control and a variant, each assigned 50% of the allocated traffic for that experiment. This equal distribution means that each variant has the same probability of being shown to guests.
With the Unique assignment method, an eligible guest visiting the website is independently and randomly assigned to a unique variant in each experiment. More importantly, a guest's random assignment in one experiment does not affect their assignment in the others. As a result, the same guest might be randomly assigned to the control of Experiment A and the variants of Experiments B and C, independently. The Unique assignment method ensures that a guest's assignment to a variant in each experiment is isolated. It allows you to run multiple tests without mixing the variables and results between experiments.
Example: Randomizing guests in simultaneous experiments
Let's look at an example of how the Unique assignment method works for two experiments, one to test newsletter sign-ups and another to test Black Friday conversions.
The first experiment includes three variants: the original page currently in use (Control), a variant featuring a pop-up window (Variant 1), and another variant with a top bar banner (Variant 2). The second experiment includes two variants: the original Black Friday page currently in use (Control) and a variant featuring an alert bar (Variant 1).
The following tables give an overview of the variants in each experiment, including examples of how two guests might be assigned.
Experiment 1: Newsletter sign-ups
Variant |
Feature to test |
Variant traffic assignment |
Guest assignment example |
---|---|---|---|
Control |
Original page |
34% |
Guest 2 |
Variant 1 |
Pop-up window |
33% | |
Variant 2 |
Top bar banner |
33% |
Guest 1 |
Experiment 2: Black Friday sales
Variant |
Feature to test |
Variant traffic assignment |
Guest assignment example |
---|---|---|---|
Control |
Original page |
50% |
Guest 1 |
Variant 1 |
Alert bar |
50% |
Guest 2 |
Due to unique assignment, Guests 1 and 2 both receive different assignments and variant combinations across the two experiments. Guest 1 is randomly assigned to the variant that features a pop-up window (Variant 2) in the newsletter experiment, and to the original page (Control) in the Black Friday experiment. Guest 2 is randomly assigned to the original page (Control) in the newsletter experiment, and to the variant that features an alert bar (Variant 1) in the Black Friday experiment. This example demonstrates how you can use the Unique assignment method to consistently and independently randomize a guest's assignment to a variant in each experiment.