In this article, we will discuss how analytics insights help increase subscription app revenue, the events you need to track to build quality analytics, and metrics that answer the most important questions.
In the fast-paced world of subscription applications, tracking key events is critical to success. By monitoring and analyzing these events, app developers and marketers can gain valuable insights into user behavior, optimize app performance, and increase user engagement.
Whether you are a seasoned app manager or just starting in the subscription app industry, understanding and tracking these events and metrics will enable you to make data-driven decisions and maximize the value of your app.
So let's dive in and discover the key subscription app events and metrics you should be monitoring to unlock your app's full potential!
To further enhance the analysis capabilities, we should define the specific events we need to track and identify the key metrics that are critical to our analysis. By grouping application events based on their associated elements, we can gain a more organized and granular understanding of our data. In addition, we will establish a comprehensive event tracking and data analysis framework to ensure that we have a structured approach to collecting, analyzing, and interpreting the data generated by these events.
1. Users and installs
These events are the most important because they are the foundation of your analytics system. It depends on when we start tracking the user and when our analytics start working.
In Apphud, we calculate all metrics based on the
user_created events related to opening the application. However, in the store analytics, the installs and app unit events are related to downloading the application.
2. Trial events
Most subscription applications offer a trial period for new users. This allows them to showcase the product and convert leads to customers smoothly. Trial events are a useful addition to analytics and ad optimization. The most common trial events to track are: trial started, trial canceled, and trial converted.
Understanding the users’ cohort who did not cancel the trial in the first hour is important for marketing and product development.
Trial value = (Ad spent / # of subscriptions) / (# of subscriptions / # of trials * 100%)
Alternatively, you can send your customer's LTV (or ARPPU) as a trial value. This approach improves the accuracy of your return on ad spend (ROAS).
Understanding this value can help you manage ads more effectively by increasing or decreasing ad bids and budgets.
3. Subscription events
Of course, when we talk about applications with subscriptions, we need to keep track of subscription events. Examples:
subscription_expired. By monitoring subscription events, you can understand user preferences, identify trends, and make decisions to optimize subscription models.
4. Intro/Promo offers
To evaluate offer performance, we need to track offer events. There are 2 types of offers: introductory offers and promotional offers. There is only one difference - an introductory offer is a discount that can only be applied to new subscribers, while a promotional offer can be applied at any time.
5. One-time purchases
In addition to subscriptions, some applications offer one-time purchases. There are 2 types of them: Non-consumable - most often used for lifetime unlocking of premium features, and Consumable - most often there are some items in games that you can buy as much as you want.
6. Paywall events
These events are useful for building funnels and understanding paywall conversions. By knowing how many users successfully convert from paywalls, you can evaluate the effectiveness of your monetization strategies and make necessary adjustments to drive higher conversions.
They are sent manually from the application code.
Refunds can be made at any time during the subscription period, even if a long time has passed since it started. The Platform may decide to refund the user's subscription fee if it considers the user's arguments to be valid. It should also be noted that refunds may be partial or total.
8. Billing issue
On average, only about 16% of renewals are successful after the billing issue. This means that if you ignore and do not track billing issues, you will lose customers.
Also good to know - Apple supports 2 mechanisms for repeated billing, within 16 and 60 days of the scheduled renewal period.
9. Other events
Here are some other events to keep an eye on and why they might be useful to your business.
Apphud tracks even more events. Check it in our documentation.
This is where the fun begins. Here we turn all the events we track into meaningful metrics to drive the growth of our subscription apps. But before we get started, we must always address the questions of what and why we are measuring.
All data must be carefully examined and analyzed in detail. Simply relying on aggregated averages will not provide valuable insights. In addition, it is important to monitor changes in payback over time and identify the specific day when we break even. It is also important to track the growth dynamics of the average revenue within the selected cohort.
When it comes to calculating metrics, there are two main types: cohort metrics and non-cohort metrics.
Cohort metrics divide users into groups based on a specific characteristic and period. For example, you could create a cohort of users who installed the application in March of this year.
The defining characteristic of cohorts is that their size is limited to the selected period. While the metrics for cohort users may change over time, the size of the cohort remains constant. Cohort analysis is necessary to gain important insights; you need to take a magnifying glass to the users you attract and how they behave over time. Application owners who do not use cohorts and focus only on aggregate analysis essentially lose a lot of useful insights.
On the other hand, non-cohort metrics are calculated for all users without any specific grouping or segmentation. These metrics can be calculated for the entire user base without regard to specific cohorts.
It's important to note that the same metrics can be calculated for both cohort and non-cohort scenarios. For example, revenue metrics can be derived from both types of data. Both cohort and non-cohort metrics provide valuable insights into user performance and behavior. The prioritization of metrics should be based on your business goals.
Let's look at specific examples.
Measuring Monthly Recurring Revenue (MRR) and Annual Recurring Revenue (ARR) is no less critical for subscription-based applications. These metrics provide valuable information into the financial health and growth of the business.
MRR helps track the predictable revenue generated each month, providing a clear indication of the app's stability and ability to cover ongoing expenses. It also allows companies to identify trends and make informed decisions about pricing, retention, and expansion strategies.
MRR= number of subscribers * normalized revenue (by month)
This metric is useful for understanding how much money recurring payments bring to the application each month. Non-renewal purchases are not included, only subscriptions.
The dynamic of the new/churn MRR movement is also useful to understand the trend of subscription growth/decline.
ARR provides a broader view of the app's long-term revenue potential. By multiplying MRR by 12, companies can estimate the annual revenue they can expect from their customer base. This metric is beneficial for forecasting and setting growth targets.
Annual Recurring Revenue (ARR) is recurring payments of the year = MRR * 12
Gross Revenue: Gross revenue is the total amount billed to customers for purchasing subscriptions before refunds, taxes, and Apple’s commission. Gross revenue provides an overall measure of a company's financial performance without regard to costs or deductions.
Sales: There is a total amount billed to customers for purchasing in-app purchases. Sales = Gross Revenue - Refunds. The evaluation of this metric depends on the percentage of in-app purchases in your total revenue.
Proceeds: Proceeds refer to the amount of money the app receives after deducting the Store commission and VAT. Proceeds is a clearer metric to evaluate the app's performance.
In summary, while gross revenues represent the total revenues generated by a company, sales focus specifically on revenues generated from in-app purchases. Proceeds, on the other hand, are the final amount of money the subscription business has. In most cases, it is better to look at the Proceeds since this is the money that will go into your hands.
Gross revenue= All revenue from purchases - Deductions
Sales= Gross revenue - Refunds
Proceeds= (Sales - Commission - VAT) * Currency Exchange Rate
Refunds refer to the process of returning money to customers who cancel their subscriptions or request a refund for any reason. The Refund Rate metric measures the percentage of total subscriptions that result in refunds. Monitoring these metrics can provide insight into overall customer satisfaction, product quality, and potential issues that need to be addressed to reduce refund rates and improve user retention.
Refund Rate = Refunded Transactions / All Transactions by the Period * 100%
You cannot ignore the importance of refunds. If the refund rate remains consistently high, reaching 15% or more, the store may take action, including the possibility of suspending your account. Such situations often occur with scammers and fraudulent activity.
ARPU measures the average revenue each user generates, regardless of whether they are paying or non-paying users. It is calculated by dividing the total revenue generated by the number of active users.
ARPPU, on the other hand, focuses on the average revenue generated by paying users only. It provides a more specific view of the revenue generated by those users who are actively paying for the subscription.
ARPAS is an average revenue per active subscription metric. This metric helps optimize an ad campaign's bid by counting only those users who performed the valuable (in terms of monetization) actions within the SKAN attribution time frame.
ARPU = Revenue (Sales or Proceeds) / New users count
ARPPU = Revenue (Sales or Proceeds) / New paying users count
ARPAS = Revenue (Sales or Proceeds) / (New paying + Trial started) users count
Benchmarking ARPU/ARPPU/ARPAS can be challenging as it depends on various factors such as app category, target audience, pricing model, and market conditions. It is recommended to compare these metrics within the same industry or app category to gain meaningful insights.
Measuring these metrics on a cohort basis provides a more granular and accurate understanding of user monetization. It allows you to see how different segments of users contribute to revenue generation over time. We can identify which cohorts have higher levels of engagement and spending, and tailor our strategies accordingly.
Understanding the importance of LTV is critical. It allows us to determine how much revenue we can generate from a user over the long term. This knowledge helps us scale purchases and bid higher in ad auctions.
When we talk about LTV, it can have different meanings depending on the context. Calculating LTV is not a one-size-fits-all approach, and it's important to consider the nuances. For subscription-based models, our focus is on paying users, not all users collectively.
The ratio of LTV to CAC (Customer Acquisition Cost) is a critical factor. It must be greater than one for the acquired users to be profitable. However, it's generally considered favorable if the ratio exceeds 3x.
In the subscription model, we are primarily interested in calculating LTV specifically for paying or trial users. Similarly, CAC/CPA (cost per acquisition) is calculated specifically for these user segments and not for all installations.
ROAS is one of the key ROI metrics for marketing efforts. It measures the revenue generated from advertising compared to the amount spent. ROAS can be calculated either in bulk or per unit, which is useful for understanding and calculating unit economics.
In the context of subscription app analytics, conversions refer to the number of users who complete a desired action, such as signing up for a subscription or upgrading to a higher tier. It measures the effectiveness of your app in converting users into paying customers.
Typically, the biggest drop-off occurs at the Renewal 1 step. It is also useful to look at the change in conversions over time on a line graph.
Funnels in subscription analytics represent a series of steps or events that users go through before reaching a specific goal, such as subscribing to a service. Visualizing the funnel helps analyze the user journey, identify bottlenecks or drop-off points, and optimize the conversion process.
When optimizing a funnel in subscription app analytics, it is important to prioritize certain elements to improve conversion rates. The easiest way is to start with the largest drop-off, but this is not always the right answer. It depends on your goals and the resources available to your team.
According to open source information, the conversion rate for the first renewal of a monthly subscription averages just over 50%, but from the second to the third renewal, for example, it is more than 80%. It is important to ensure that renewing users continue to derive value from the application.
Conversion examples in Apphud
New users -> Trial started -> Trial converted
New users -> Subscription (intro/promo) started
New users -> Non-renewing purchase
The main funnels for trials and subscriptions that Apphud measures are:
In summary, tracking subscription app events is critical to understanding user behavior and optimizing revenue in subscription-based apps. Subscription app revenue analysis provides a deeper understanding of the app's financial performance and enables strategic decision-making. Calculating metrics such as ARPPU, ARPAS, and LTV helps evaluate app profitability and identify areas for improvement.
1) Cohort analysis allows you to compare in detail how different groups of users behave over time.
2) It is important to know the dynamics of the accumulation of the user's average revenue starting from day 0.
3) In the new post-IDFA reality, non-standard metrics such as ARPAS become relevant.
4) It is important to track key conversions by application and the dynamics of change for key revenue-generating cohorts.
5) You need to understand how revenue is generated (new, returns, renewals, refunds, etc.).
Get all-embracing visibility of your app MRR, ARPU, Churn, Refund rate, and 20+ more key revenue metrics in Apphud. From tracking subscription revenue and user churn rate to analyzing subscription conversion and retention, Apphud provides comprehensive analytics tools to help app managers understand and improve their subscription app's performance.
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