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# How to analyze paying users? Part 1, RFM-analysis

This article starts the whole series of articles devoted to the analysis of paying users.

So, paying users are those guys that bring money to your product. It is very important to know all the nuances of their behavior: what do they pay for, how fast and how much. It is important to know what they feel by doing this, do they get satisfaction from the investments they made into your product. In fact, even in the case of f2p online-game every payment made by gamers is their investment: at the beginning they pay, at the end they get some ROI, which can be denominated in a currency or in the emotions they experience. Therefore, you should perceive users as investors even if they are minority investors.

In order to better understand the behavior, distribution and needs of paying users there exist special analytical methods and reports. Today we will talk about RFM-analysis as one of the basic methods of understanding the structure of your paying audience.

RFM stands for:

R – Recency – how long ago the last purchase was made;

F – Frequency – how often purchases were made;

M – Monetary – the volume of purchases during all the time.

You give three marks that correspond to those parameters to every paying user. As a rule, in theoretical materials a user is assessed with a three-point scale (relatively speaking: good, normal, bad), however in practice we have also faced five- or even ten-point scales in RFM-analysis. To make it easier, let's look at the example of the three-point system:

• R = 1, it has been a long time since a user payed last time;

• R = 2. last payment was made relatively short time ago;

• R = 3, a user has payed recently;

• F = 1, a user pays very rarely;

• F = 2, a user pays with moderate regularity;

• F = 3, a user pays often;

• M= 1, the sum of all payments is small;

• M = 2, a user have payed moderate amount of money to the project;

• M = 3, a user have payed much money.

Of course, the question arises: how to understand in this case long time ago/recently, often/rarely and much/little. It is possible to answer this question in two ways:

1. Expert assessment. No one knows your project better than you. Therefore, define for yourself what is long time ago and recently, what is much and little. Let's say, long time ago is a month and more ago, rarely is once a month or more rarely, little is not more that hundred rubles during the whole payment history.

2. Quantiles and quartiles. Let's recall mathematical statistics. Arrange your users according to one of the parameters (for example, to the sum of payments made during all the time), take, for instance, top-5% of all the users and say that these users payed much. Congratulations, you have already got five-percent quantile of your users' sample. You can also take quartiles (quartile = quantile of 25% -level), and assess first quartile as much, last as little, and what is between them as an average size of a payment. Be that as it may, even when you use quantile and quartile you cannot do without subjective assessment, therefore, look at point 1 again.

Anyway, having spent some time in Excel (or somewhere else), you will give a mark to every paying user for recency, frequency and amount of payments.

Now is the most interesting part.

You can see how those marks are distributed among your paying users and what are the majority of users. This will allow you to segment your paying audience and plan your marketing actions aimed at raising profit.

A simple example:

• Purchased recently, but rarely (or only one payment) – new paying users. What should you do with them? Express your gratitude! Your aim is to stimulate them to make repeated purchases. As many researches show, repeated purchases, their regularity and size raise the app's chances to earn a million of dollars.

• Purchased recently and purchase often – loyal users. They do not need additional stimulation, however you can find a way and thank them for their loyalty (unexpected bonus, surprise, just “thanks” - all these work).

• Purchased often, but long time ago – loyal users on the verge of leaving. In other words, these are the money that right now slip through your fingers. Your aim is to remind them about yourself. Maybe, a simple push-notification will be enough. Maybe, it is worth asking them what has changed and why they are leaving.

• Purchased rarely and long time ago – outflow of users. They didn't become loyal, something in the past prevented them from doing it. You can propose them (and not only them) an action - even if it is not profitable for you - which would stimulate them to make a repeated purchase and to return to the product. Otherwise, at least you can try to find out what they didn't like, and adjust the product based on the feedback.

Imagine the following case.

1. Project X wants to raise its income;

2. They conduct RFM-analysis, and it shows that:

1. the outflow of loyal users is very high;

2. many users make only one purchase.

3. They introduce some triggers to the project that allow to recognize the moment when the stay of a user in a status “one purchase” is too long or when a user that was loyal before stops paying. At these moments users get “an offer that they can't refuse” (special action, big discount, information is delivered by push-notification or pop-up window when logging);

4. The percentage of repeated purchases rises, more loyal users stay with the product;

5. Profit.

Both of discussed examples operate only two parameters: Recency, Frequency.

Adding the Monetary parameter to the report will allow to use volumes of every user's payments in addition.

Besides that, the analysis can be conducted based on the quantity of users or based on money that you get from them.

In addition, it is possible to look at the combination Monetary-Recency (how much do users pay and how long ago did they pay), Monetary-Frequency (how much and how often do users pay).

The easiest way is to analyze paying users in the frames of one parameter is to get distribution of users and their payments depending on time (long time ago – recently), on frequency (often – sometimes – rarely), on amount (much – average – little).

In particular, analysis of paying users according to the size of their payment in f2p-games is usually described with the help of the inhabitants of sea depths:

• Whales – users that bring big sums;

• Dolphins – users that bring average sums;

• Minnows – users that bring small sums.

Here we don't speak about the sums of one payment but rather about general sums accumulated during the whole payment history of a user. The differentiation on big, average and small sums is made based on expert assessment again.

By analyzing the amount of users in every segment and the amount of money that you get from every segment you will be able to understand, which actions are better for raising the profit. To lower prices? To raise prices? To focus on the retention of “whales”?

At our service devtodev.com we have divided users depending on the volume of their payment into five segments additionally defining “grand whales” and “grand dolphins”. In particular, the discussed example shows that the main part of the income is brought by “whales” and “grand dolphins”, therefore, the focus of the marketing forces should be on them.

This is only a part of methods that can be used to analyze paying users. There are many more questions, answers to which will help you to customize your project's monetization better. There are only some of them:

• How fast your users are converted into paying? During the first, the second or the tenth purchase?

• What do users pay for? Why do they become paying after all?

• How much money do you make on the first payments of gamers, how much – on repeated?

• How much money do beginners bring to you, how much do “oldies”?

We will surely tell you about every method in detail in our future articles.

If you don't want to wait, we invite you to the free webinar, which will be held on October, 27th 2015 at 12 p.m CDT.

During the webinar we will tell you about all the methods of paying users' analysis, describe cases devoted to how analytics of paying users helps to raise the income.

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