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How to analyze paying users. Part 3. Re-payments and conversion.

This article is the last in our series of "How to analyze paying users’'. Take a look to know more about re-payments and the time of conversion into paying user.

Previous parts:

Part 1. RFM analysis, whales, and dolphins.
Part 2. The structure of the revenue by time.

Today we are going to talk about re-payments and about the time of conversion into payment. What questions should you be asking yourself?

On what day, the user makes the first payment? The second? The third?
Understanding of user payment behavior will allow you to better plan monetization of your product. This is the usage of the traditional product analytics algorithm:

  1. Identity the pattern of user behavior;
  2. Find the users whose behavior initially corresponds to this pattern;
  3. Offer these users to take next action from the pattern. For you it looks like a logical continuation of the pattern, and for the user - as a targeted offer at the right time.

Knowing when the user makes the next payment, you can get ahead of it and offer the desired campaign (or targeted offer) at the right time. 

For example, let’s use the report “‘Period until payments” by devtodev analytical system. Choose the time of the very first payment (you can also choose the second, the third or all payments, regardless of the serial number), set the period of users registration, and look at the distribution of time of their first payment. 

We see that the bulk of the first payments is made in the first days, even at the day of registration of the user. This means that we can safely propose any campaign from the very beginning of user's staying in the app, even from the first day.
However, offering to buy something, for example, immediately after passing the tutorial would not be quite right - the user decides that the game is based only on donations and, quite likely to immediately leave the application. This is why we ask ourselves the following question.

At what level the user makes the first purchase? And the second? And the third?
Again, we turn to the report "Period until payments" and choose the distribution not by days, but by levels.

Aha! Here is the peak on the fourth and fifth levels.
Thus, we have identified a pattern - paying users primarily make the first purchase on the day of registration at the achievement of levels 4-5.
In the future, we will be able to build into the project (be it a game, or say, a training service) a targeted offer corresponding to this pattern. And it will help us to increase the conversion into paying user on the first day.

How many users make one payment? How many users make repeated payments? How are the sums distributed between the first and repeated payments?
Let's begin with a little advice. Advice: par with metrics Paying Users (amount of paying users) and Paying Share (proportion of paying users among active audience) pay attention to New Paying Users metric, that shows the number of users that made their first payment during the period analyzed. Without the first payment, there would be no repeated ones.

Speaking about the importance of re-payments. Tapjoy company reviewed the applications that made a million dollars and  identified a few common signs.
The first sign: 84% of applications, in which at least 1,000 users made at least three payments during the first 90 days from the date of the first entry, overcame the barrier of $ 1 million.

To help you better understand the distribution of paying users by the number of payments made by them, we, here at devtodev, developed a report  "Users by transactions". It allows you to see how many users made one transaction, two transactions and so on.

The second sign, identified by Tapjoy:
If at least 35% of users made the first payment, and then made the second and the third payments, then the application is likely to make a million dollars.

The first payment is usually small in size. You may see this in the report "Paying users activity":

Users who make their first payment, only want to test the benefit of the paid use of your product and are not ready to immediately spend large sums. In addition, the first payment is also a reason to link the card to the account, if it wasn't previously linked.
But the main monetary weight is contained in the re-payments.

So to have financial success, your product must meet the following conditions:

  1. The user should come to making the first payment by himself, however, you may help him by revealing the pattern and making an offer at the right time.
  2. Do not require a large first payment from the user. The first payment is usually small in size.
  3. The user should feel the return on investment, then he would be pleased with the first purchase and willing to pay more.
  4. Your product should allow a user to pay as much as he wants. Each user, whether he is a non-paying or a whale (the user with the highest check) must enjoy the use of your product.
  5. Long-term retention is the key to a good monetization. If the user is non-paying, the longer he is with you, the higher the probability of the payment is. If the user is paying, the longer he is with you, the more he might pay.

At this, series of articles on the analysis of paying users is completed.
Analyze the behavior of those who are paying  you, understand where your money comes from and increase your cash flow.

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