Revenue is a useful way to measure success, but it can sometimes be misleading. It grows when you add new users, even if existing users are churning at an increasing rate. Revenue doesn’t show how much each user makes on average. We can’t say whether the product is getting better or worse without that information. In this article, we’ll explore the top three financial metrics that determine the health of a digital product.
ARPU stands for Average Revenue Per User. It is the average money made per user over a certain period of time. It is usually calculated every month. The formula is simple: total revenue divided by the number of active users. Not just paying users. All of them.
Why is this more important than just making money? Picture two possible services. The first option has higher revenue, but also significantly more users. The second option has less money, but each user brings in twice as much. The second service is more efficient, even though it makes less money. It’s easier for it to scale: each new user contributes more to revenue.
If ARPU is falling, that’s a warning sign. You might have attracted a cheap but solvent audience, or existing users are paying less, or the product has broken down. If your ARPU is going up, you’re doing something right. If you don’t use this measure, you’ll be missing out on a lot of valuable information.
How to measure customer loss using Churn Rate
Churn rate is the percentage of users who stopped using the product over a period of time. This is usually a month. If there were 1,000 active users at the start of the month and 900 were still using it at the end of the month, then the Churn Rate = 10%. As a result, the Health digital product financial metrics.
But there is one problem. What does “stopped using” mean? For one product, it’s not logging into the account for seven days. Another is when there is no action (like clicking, viewing or sending a message). One third of people cancel their subscription. Decide on one meaning in advance and don’t change it. Otherwise, the metric becomes useless.
The number of products being sold is always going to decrease over time. If 10% of users leave every month, the product will lose more than half its audience in a year. For most digital products with a subscription model, a healthy rate is usually around 2–5% per month. If the figure is more than 7%, that is a problem that needs to be looked into.
What should you do when a lot of people are leaving your company? First, you need to know who is leaving. New users are having problems with the sign-up process. People who have used the product for a long time say that it is not worth the money. Users who pay for the service show that they are not happy with the amount they are paying for the quality they are getting. Free users are often not important if they don’t make money or get other users.
LTV and CAC. When a product stops being worth the money it costs, LTV (Lifetime Value) is the amount of money one user will bring in over their whole time using the product. CAC (Customer Acquisition Cost) is the amount it costs to get one paying customer.
The most important rule in the industry: LTV must be at least three times higher than CAC. Why not two? This is because there are operating expenses, taxes, refunds and support costs. A 3:1 ratio makes sure there is enough safety.
Calculating LTV is simple: ARPU divided by Churn Rate. For example: The average revenue per user (ARPU) is $10, and the churn rate (the rate at which users cancel their service) is 10% (0.1). LTV = 10 / 0.1 = $100. If CAC is $25, the LTV/CAC ratio is 4. That’s great. If CAC is $60, the ratio is 1.6. So, you’re breaking even or losing money.
The most common mistake is to calculate CAC for all users, rather than just for paying users. It costs money to acquire free users. But they only pay for themselves if they later become paying users. If your product is supported by advertising (in which case every user generates revenue, even if they don’t pay directly),
Video chat Flirtbee is a great way to try out metrics
Video services are the perfect place to try out metrics. They involve short interaction cycles, fast changes in how users behave, and a lot of competition for people’s attention.
Now, let’s take a look at the Flirtbees service. It makes money by having people pay for video dates, as well as for small purchases like gifts during online chats. When we’re talking about a product like this, the classic ARPU (average revenue per user) doesn’t tell the whole story.
Why? Flirtbees is a video platform that doesn’t cost anything to use. It allows users to create a fun and engaging environment where other users will want to send gifts. Free users here aren’t useless; they’re part of the plan to make money. If you calculate ARPU based only on paying users, you won’t see how free users influence the behaviour of paying ones.
For Flirtbee, the key metric is not only ARPU but also how often a user returns to the site. If a user stops using the service after three video chats, it’s probably because the conversation partners or the matching process are not good enough.
Another detail to note: To work out the number of people leaving, it’s better to look at the number of completed videochats, not the number of days. A user might not log in for three days, then have 20 chats in one evening. The number of days they’ve been active is counted, but in reality, they’re actually active. Flirtbees, like any video service, must choose a definition of churn that matches how users actually behave.
Flirtbees is a good example of this. If you’re selling a service that comes with a gift, it’s good to have a high return rate. But if you’re selling a product with long paid sessions, a high return rate may not be necessary.
The most important thing to remember when using video calls for any digital product is that you can’t just look at one piece of data. A high ARPU can hide a high churn rate. If the current value of the property is lower than expected, the low credit assessment (CAC) is not useful. You need to look at them together, every month, and compare them to the same period last month.
Calculation mistakes that can ruin a business: Health digital product financial metrics
The first mistake is to calculate ARPU only for “active” users, changing the definition to get the result you want. It would be better to include all registered users or everyone who has logged in at least once in the last month.
The second is ignoring cohort analysis. If the overall churn rate is 10%, this can hide the fact that the users who joined in January had a churn rate of 5%, while the users who joined in February had a churn rate of 15%. There are different reasons for this. If you don’t do this, you’ll be trying to solve the wrong problem.
The third is forgetting about time. LTV is calculated using data from the past. But if you changed the price yesterday or added a new feature, past LTV won’t predict the future. You need to wait for a new group of students.
Fourth, you shouldn’t compare yourself to others without considering how they do business. The ARPU and Churn Rate targets for a subscription service and a service with one-time purchases will be different. Try to see how you have changed compared to the person you were in the past.
Health digital product financial metrics: A short checklist for reporting
Calculated ARPU for the month. Included all active users with a clear definition of “activity”.
Calculated the Churn Rate for the same month. Clearly defined what “churned” means.
Calculated LTV = ARPU / Churn Rate. Health digital product financial metrics
Calculated CAC separately for paying users (marketing spend / number of new paying users).
Checked the LTV / CAC ratio. Less than 2 is a stop signal. Less than 1 — stop user acquisition.
Broke down metrics by cohorts (e.g., by week of first visit).
We compared them to the previous month. We explained any changes of more than 10%.
These four metrics (ARPU, Churn Rate, LTV, CAC) won’t automatically make a product successful. But without them, you won’t know why it failed. And that’s the only way to avoid repeating mistakes next time.
