This article helps you:
Understand the cumulative exposures graph in Amplitude Experiment
Gain a deeper understanding of analyzing cumulative exposure results with examples
The cumulative exposures graph details the number of users who are exposed to your experiment over time. The x-axis displays the date when the user was first exposed to your experiment; the y-axis displays a cumulative, running total of the number of users exposed to the experiment.
Each user is only counted once, unless they're exposed to more than one experiment variant; in that case, they count once for each variant they see.
This article covers cumulative exposure results with:
Be sure to check out other help center articles on interpreting cumulative exposure results with:
In the graph below, each line represents a single variant. March 20 is the first day of the experiment, with 158 users triggering the exposure event for the control variant. A day later, a total of 314 users have been exposed to the control variant. That number is the sum of exposures on March 20 and March 21.
This is a very standard cumulative exposure graph with an increasing slope.
Mathematically speaking, the slope of each line is the change in the y-axis divided by the change in the x-axis:
1∆y / ∆x = (cumulative users exposed as of day T1 — cumulative users exposed as of day T0) / (number of days elapsed between T0 and T1) = Number of new users exposed to the experiment, per day, from day T0 to day T1.
What are some other things we can say about this graph?
Often, changing the x-axis to an hourly setting, as opposed to daily, offers new ways of understanding your chart:
Here, the trend is still fairly linear. But since this is an hourly graph, from 9 PM to about 5 AM, almost no additional users get exposed to the experiment. This is probably when people are sleeping, so it stands to reason they aren't using the product. This is something we couldn’t have seen in the daily version of the graph.
This is a more extreme example. Here, the exposures look like a step function. In this case, it could be that the users who have already been exposed to your experiment at least once are evaluating the feature flag again during these “flat” time periods.
Sometimes, an experiment’s cumulative exposures can start out strong but then slow down over time.
When this experiment launched, each variant was exposed to about 280 new users each day. But toward the end, those exposure rates were down to about 40 new users per variant, per day.
The cumulative exposures can flatten out over time when you target a static cohort, for example, one that doesn't grow or shrink on its own.
For example, imagine a static cohort with 100 members. On the first day, 40 of those users saw your experiment. That leaves only 60 more users eligible users to include in the future. With each passing day, there are fewer and fewer users who can enter into the experiment in the first place, and the slope of your cumulative exposures graph inevitably flattens.
If you’re using a static cohort in an experiment, consider rethinking how you’re using the duration estimator. Instead of solving for the sample size, you should ask what level of lift you can reasonably detect with this fixed sample size.
Whenever you use a cohort in this way, ask yourself whether the cohort is actually representative of a larger population that would show a similar lift if more users were exposed to the winning variant. You can’t assume this; doing so would be like running an experiment in one country and then assuming you’ll see the same impact in any other country.
Bear in mind that just because the cumulative exposures graph has flattened out doesn't mean that the experiment has a limited impact. It all depends on the specifics of your users’ behavior.
Seeing this kind of graph has serious implications to the duration your experiment needs to run. The standard method of calculating the duration of an experiment is to use a sample size calculator and divide the estimated number of samples by the average traffic per day. Here, that’s not the case. Generally, you’ll need to run the experiment for longer than expected, since the denominator was overestimated.
November 6th, 2024
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