Charts are available on paid plans.
RevenueCat charts allow you to understand your user base with key subscription specific metrics, filters, and segments. All charts are generated from the current snapshot of purchase receipts saved in RevenueCat and work independently from any in-app usage.
This means that your charts are always up-to-date, without having to rely on any client-side event logging. However, since receipt files are dynamic, this means even historical data may change from day-to-day if for example a user was refunded. This also means that data in RevenueCat may be different than other systems tracking similar metrics - see the section on Differences In Data for more information.
Charts show production data only
Due to the limitations of the sandbox environments, charts are only displayed for production transaction data.
The Monthly Recurring Revenue (MRR) chart is the benchmark of the size and growth of your subscriber base and your business.
The calculation takes revenue amortized to a 28-day period (not including any platform fees or taxes). This means an annual subscription purchased for $100 will show as $7.67 MRR (100/365*28), and a weekly subscription purchased for $2.99 will show as $11.96 MRR (2.99/7*28).
The Revenue chart shows the gross revenue (not including taxes and VAT) generated by subscription and non-subscription purchases during a given period. This means a yearly subscription purchased for $100 will count all $100 toward revenue for that day.
The chart breaks down proceeds after app store fees (either 70% or 85%) and can be useful for forecasting cashflow and store payouts.
The New Purchases & Trials chart shows the number of new purchases (e.g. trial starts, initial purchases, and non-subscriptions) during a given period.
Note that it is possible for a single user to start multiple subscriptions in the same period, if for example they start a trial for a monthly subscription then switch to an annual subscription.
This chart is useful for tracking the acceleration (is the number of new users increasing every week?) and is powerful when combined with filters and segments to understand what is driving new subscriptions.
The Active Subscriptions & Trials chart shows the total number of active subscriptions, including trials, during a given period. Note that it is possible for a single user to have multiple subscriptions. A subscription is considered active as long as the user has access, regardless of whether the subscription will renew or not.
Usually this chart is highly correlated to MRR, and when combined with filters and segments can help understand the long-term value of certain user groups.
The Trial Start Rate chart shows the number of users who have started a trial within 7-days from when they were first seen. This means there may be a higher volume of trial starts shown in the New Purchases & Trials chart - but since most users start a trial within 7-days of installing this can be an important metric in understanding conversion rates. Note that because this chart takes into account events that happen 7-days after a user installs, they'll be no data unless you extend the date range beyond the past 7-days
Requires at least seven days of data
Since this chart is based on the number of users that start a trial within the first 7-days, it needs at least seven full days worth of data before the first data point will populate.
Filters don't alter install counts
Since most filters can't apply to installs, adding filters to the Trial Start rate chart will not affect the install count.
You should expect the Install count in RevenueCat to be different than the download numbers provided by Apple/Google. However, if things seem drastically off, make sure you're identifying users correctly in RevenueCat.
The Trial Conversion chart shows just that: the percentage of free trials that convert to paid subscriptions. The calculation is (number of trials converted to paid) / (number of trials eligible to convert) * 100. Note that this calculation doesn't consider users who never start a trial (trial start rate).
Trial conversion rates are an important metric for understanding your new user flow and the growth of your subscriber base.
Churn is the percentage of paid subscriptions that were lost during a given period. The calculation is (number of subscriptions expired during period) / (number of paid subscriptions at the start of the period) * 100. Note that a subscription isn't counted as expired until the user no longer has access. This means users that turn off auto-renew aren't counted as "churned" until their subscription expires. If your app offers multiple subscriptions, it's possible to have "negative churn" if users are subscribing to multiple products - this is known as expansion.
Churn can be relatively high for mobile apps when compared to web SaaS products. Like trial conversion, churn is another metric you should continuously monitor and work to improve. By combining churn and trial conversion you can start to make longer-term forecasts of the lifetime value of your subscribers.
Auto Renew Status shows a snapshot of the active subscriptions today, segmented by whether they are set to renew or not. This chart isn't plotted over a timeseries since it is independent of when the user purchased the subscription.
This chart can be especially helpful when filtered to only show subscriptions with an 'annual' duration, as you can start to forecast long-term churn before the subscription actually expires.
The Cohorts table lets you see when in their lifecycle subscribers are churning. The Initial Cohort shows the number of new subscribers that were added in the month. As you move left to right you'll see how much of the initial cohort remained subscribed in the subsequent months.
The cohorts table can help you understand if there's a certain point where the majority of your subscribers drop off. If it's early on, try to offer more value for new users or offer a longer trial.
Note that churns are not counted until the subscription is expired.
Requires two complete months of data
Since this chart is based on monthly cohorts, it needs at least two full months of data before it will populate.
All charts can be filtered, while revenue and subscriber charts can be filtered and/or segmented.
Filters allow you to limit the charts to only include data that matches one or more attributes. This is useful when you want to check the performance of a specific property, such as a certain country or product identifier.
Segments allow you to break down the chart totals into underlying data segments. This is useful for comparing the performance of specific properties, such as monthly vs. annual subscriptions.
The month that the user was first seen by RevenueCat (segment option only).
First Purchase Month
The month that the first purchase (incl. free trials) was recorded for the user (segment option only).
The store that processed the purchase. Either App Store, Play Store, Stripe, or Mac App Store.
The duration of the normal subscription period (not trial or intro period).
The different apps you have access to in RevenueCat.
The product identifier set in the store.
The entitlement identifier set in RevenueCat.
The offering identifier set in RevenueCat.
Is Trial Period
Whether a subscription is in a free trial or paid period.
Whether a subscription in an initial paid period or a renewal. Free trials that convert to paid will always be in a renewal period.
The device locale that was recorded with the purchase. May be unknown.
Apple Search Ads Campaign
If you're collecting Apple Search Ads Attribution, the specific campaign that drove the install (iOS only).
Apple Search Ads Ad Group
If you're collecting Apple Search Ads Attribution, the specific ad group that drove the install (iOS only).
Save views as bookmarks
Filters and segments are set as URL parameters so views can be saved as bookmarks for quick access later.
The underlying chart data can be exported in .csv format by clicking the Export CSV button.
Choose the time scale for the x-axis of the charts. Use a day timescale to see the most granular level of data and lower resolutions like month to spot longer term trends.
Depending on the chart and segments, it may be more useful to view the data as a bar chart.
With a small number of segments, it may be preferable to compare the data side-by-side instead of stacked.
The Zero y-axis toggle can be turned on to spot subtle differences in more granular data.
All of the charts in RevenueCat are generated from a snapshot of the current state of all receipts from your app. This is different than other analytics systems that may use event based metrics and counters to track values. This also means that data for any historical point in time may change if a receipt file is added or updated.
When comparing data in RevenueCat to other systems, keep in mind any reporting differences between the systems such as timezones, calculations, and whether the underlying source data is the same.
All charts are displayed in UTC time.
Due to the limitations of the sandbox environments, charts can only display production transaction data.
- Learn how to view the purchase history of a specific user and grant them promotional access via the Customer View
Updated 2 months ago