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Amplitude’s Funnel Analysis chart helps you understand how users are navigating defined paths ("funnels") within your product, and identify potential problem areas where users tend to drop off.
This article will describe how the Metrics Module of the Funnel Analysis chart works, and how you should interpret the data it contains.
Analyzing your funnel analysis data takes place in the screen’s lower panel.
Before you begin
Interpret your Funnel Analysis chart
Interpreting the Funnel Analysis chart is more straightforward than it may at first appear, mostly because you can read through the parameters like a sentence.
For example, the following chart will show you (1) new users who (2) fired these events (3) in this order, (4) within 30 days of their first day as an active user.
All these parameters, as well as many others, can be easily changed to reflect the needs of your analysis.
In the rest of this section, we’ll explain the Metrics Module as it applies to a funnel analysis, what all the parameter options mean, and how you can use them to generate the data you want.
Set your time options
Specifying the time frames of your funnel analysis is straightforward in Amplitude.
- ...completed within: This is where you will set your conversion window, which is the maximum length of time allowed for a converting user to take between entering a funnel and completing it. The default conversion window is one day (in UTC). This means Amplitude will count a user as converted if they complete the funnel within one day of entering it; any longer than that, and the user will not be counted. The minimum conversion window length is one second, and the maximum is 90 days.
NOTE: Customers who have access to the Accounts add-on have the maximum conversion window of 366 days.
- any day: This applies to new user funnels only. If you select any day from the dropdown, the funnel will include new users who have performed the first step of the funnel at any point in the date range selected.
- their first day: If, in a new user funnel, you select their first day instead, this restricts the funnel to users who fire the first event (and thus enter the funnel) on the first day they appear in Amplitude (their new user date).
The default option for a Funnel Analysis chart, the Conversion graph is a bar graph detailing the number of users who have clicked through to each step of the funnel.
In the chart, we see that there were 165,241 users who fired the event 'Search Song or Video' in the last 30 days. Of these, 161,696 fired 'Play Song or Video' within 30 days of searching for a song or video. And 26,423 of the original group of users fired 'Share Song or Video' within 30 days of 'Search Song or Video.'
Not only does the bar graph show the number of users who converted at each step, it also shows the number of users who dropped off at a particular step of the funnel. The former are displayed by the solid regions of the bars, while the latter are represented by the striped areas on top.
The tabular view of the data, which you'll find directly below the chart, offers some additional context:
- Conversion: The percentage of users who successfully completed the entire funnel.
- [Event name]: The number of users who complete that step in the funnel. The first step will always be 100% because a funnel only includes users who fired that first event.
- Average Time: The average time it takes users to move from one event to another event in the funnel, based on the time of users' first conversion.
NOTE: If you've applied a group-by to your funnel chart, the Average Time column will return "N/A," since average and median times will not be computed for daily/weekly/monthly step transitions.
Conversion over time
The Conversion Over Time graph shows conversion rates for users who entered the funnel on a specific date. If, for example, a user enters a funnel on January 1st and then converts in the funnel on January 5th, they will be counted in the bucket for January 1st, since that's when they first entered the funnel.
The percentages seen here are conversions per unique user, per day/week/month. For instance, if a user enters the funnel by firing the first step on both July 1st and July 2nd, and completes the funnel within 30 days of both dates, that user will be counted in the conversion percentages for both July 1st and 2nd.
This graph can also show you the conversion rate between funnel steps. Users do not have to complete the entire funnel to be included in this analysis—instead, they need only complete all the steps up to (and including) the last step you're interested in.
For an example, let's look at this screenshot:
Within this three-step funnel, Amplitude offers you the option to look at conversion rates between steps one and two, or between steps two and three. If you were to select
1: Search Song or Video to 2: Play Song or Video , all users who completed those two steps would be included, regardless of whether or not they completed step three.
If this were a four-step process, conversions from step two to step three would include all users who completed the first three steps of the process, regardless of whether they completed the fourth. Users always must enter the funnel at the first step to be included.
NOTE: Conversion Over Time for new users still counts all active users.
Time to convert
Time to Convert shows you how long your users take to move from one step in your funnel to the next, displaying the data as a histogram.
The horizontal axis is broken into 25 interval buckets. Amplitude automatically chooses a bin size (1 second, 10 seconds, 1 minute, 10 minutes, 1 hour, 1 day, 10 days, or 30 days), depending on which size would give the most standard deviations. The maximum and minimum time to convert is dependent on the conversion window and time range you’ve chosen.
The percentages on the vertical axis represent the ratio of users who converted within a particular interval, relative to the number of all users who converted within the selected time range.
If you need to, you can also create custom bins:
If you create custom bins, the percentages returned will be calculated using only users who fall between the min and max values for your bin.
NOTE: The median bar will only appear if you're looking at the time to convert between two specific steps. Additionally, the median bar will still be calculated based on the full data set, regardless of the bin min and max.
While the default scope of a Time to Convert graph is the entire funnel, you can also limit it to any two consecutive steps in your funnel:
The Frequency chart helps you get a sense of of the number of times users in your funnel fire one event before firing another specific event for the first time. You can choose the two events you want to analyze in the Metrics Module, as shown in the screen shot below.
For example, in the below example we see that 74.3% of users who reach Step 3 of the funnel fire the ‘Download Song or Video’ event only once before purchasing a song or video within a one-day period.
NOTE: For best practices, including tips on instrumentation, please take a look at our How to Analyze A/B Tests Results in Amplitude article.
Also, A/B testing is only available for customers with Growth and Enterprise plans.
In Amplitude, A/B testing lets you compare the funnel conversion performance of two or more user segments against each other. You can view results in terms of improvement—which describes the performance of a segment compared to the baseline—or in terms of statistical significance, which will show you the predicted chance that the segment you're viewing will outperform the baseline.
Amplitude will, by default, use the first segment added to the funnel analysis as the baseline, but you can change this in the where baseline is set to drop-down menu.
A/B Test - Improvement
This chart will display the conversion rate for each segment across all steps in your funnel. In this example, the variant 'Germany' is performing more than 13% better than the baseline, which in this case is a segment of users based in the United States:
You can certainly have more than just one variant in an A/B test, but you can only have one baseline.
A/B Test - Significance
This chart will tell you each variant's predicted chance to outperform the baseline. A high value suggests the variant will convert better than the baseline, while a low value suggests it won't.
If a variant's results have both a) a sample size above 30, and b) a chance to outperform of 97.5% or higher, Amplitude considers them significant.
In our example, we can see the segment of users based in Germany has an excellent chance to outperform the baseline of US-based users:
Get more details on how Amplitude calculates chance to outperform and statistical significance.
The data table below the chart will give you a breakdown of the data. As with all data tables in Amplitude, you can export the data as a CSV file. Here are the columns included:
- Count: The number of users or groups that entered the funnel.
- Converted: The number of users or groups that completed all the steps in the funnel with all conditions met.
- % Conversion: The number of converted users or groups, divided by the number of users or groups that entered the funnel.
- % Improvement over Baseline: This is calculated by the equation (% conversion for that variant - % conversion for the baseline) / (% conversion for the baseline). The percentage in the data table will be green when the value is a positive number.
- % Chance to Outperform: This is the percent probability that your test variant will convert better than the baseline, using a model founded on Bayesian principles. The percentage in the data table will be green when the value is above 51%.
Holding properties constant and session-based funnels
By default, Amplitude does not hold properties constant in a funnel analysis. This means the funnel chart will display the unique count of users who have gone through the funnel once or more—if, for example, the user goes through the entire funnel multiple times, they are only counted once.
So for the following chart, if a user were to convert this funnel ('SearchSong' -> 'PlaySong' -> 'ShareSong') ten times in the last 30 days, they would only show up once.
If, however, you opt to hold properties constant, the funnel chart will display the unique count of user and user/event property pairs that have completed the funnel. If a user goes through the entire funnel X times with Y distinct event property values, the user will be counted Y times.
For example, if a user converts 'SearchSong' -> 'PlaySong' -> 'ShareSong' with ten different 'SongName' property values, they will show up ten times in the chart. Here, 'SongName' is an event property that's been sent for all three events in the funnel. An event property can only be held constant if you have instrumented it for every event in the funnel.
You can use this method to build session-based funnels. To do this, hold constant '[Amplitude] Session ID', as shown here:
A user must complete each step in the funnel with the same session ID in order to be converted. A Funnel Analysis chart with this setup will no longer show unique users, since users can complete the funnel multiple times in different sessions.
Conversion by Event & User Property (broken down by)
You can set up your funnel to break out conversion by event property values at a specific step of the funnel. This helps you understand what property value potentially has the greatest or smallest impact on conversion.
In the example below, we have a three-step funnel: 'Search Song or Video', 'Play Song or Video', and 'Share Song or Video', segmented by Step 2's event property, 'Content_Type':
The graph below shows the conversion distribution of users who fired the 'Play Song or Video' event, broken out by each 'Content_Type' value. Of the possible values, we see that both songs and videos had roughly 42% conversion.
If you choose to break down by a step other than the first, you will also see a segment of users who did not reach that segmented step (the orange shaded segment for 'did not reach step' in this example).
NOTE: If users in your funnel can complete the steps multiple times, then this method will take the first occurrence of each event and bucket the user for the value on that event.
The conversion drivers option allows you to look at the actions taken by users between steps in the funnel. This helps you clearly identify potential drivers of conversion, or of drop-off.