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Main Metrics. Paying Conversion
Here's how you need to calculate the paying conversion and how it affects your revenue.
Published
10.06.2019
|
devtodev

 

While discussing one or another project, we quite often use the term - conversion. This index is always paid much attention to at the stage of the project analysis, and every developer tries to maximize this metric.

Conversion is a percentage of users who have committed any targeted action: pressing the “Register” button at the landing page, clicking the advertising link or, for example, making a payment.

Let's look into the last case in more detail, as it is the most important one for a project. Usually, by saying conversion, we mean paying conversion specifically.

Before deriving the formula, let’s talk about its key element. This index is calculated for a cohort, which is a certain group of users who have installed the app during the certain period.

So, paying conversion is a percentage of users who have made payments out of all users who installed the app.

Paying Conversion = Paying Users/New Users

Conversion is directly proportional to the revenue. By increasing conversion, you increase the number of users who make payments, which leads to increasing the revenue.

Revenue = New Users*Conversion*ARPPU

  New Users Conversion ARPPU Revenue
Before 1000 10% 5$ 500$
After 1000 15% 5$ 750$ (+250)

However, if the conversion increases because of the decrease of  the average receipt or the product's price, the result may turn out to be negative.

  New Users Conversion ARPPU Revenue
Before 1000 10% 5$ 500$
After 1000 15% 3$ 450$ (-50)

That is why you should take care of other financial indexes while experimenting with conversion.

You need to pay attention to them especially when providing discounts for the products. Not always lower prices lead to lower income, but it's still necessary to control them.

In addition to promotions and discounts, conversion can be affected by: product’s  design and its individual elements, headlines, texts, marketing proposals, simplicity of the interface, timely offers, competent CTA-elements, positive feedback about the product, and much more.

These points are very individual in every project; what multiplies the conversion for one product may not affect the conversion in another one. However, you still need to experiment to find out what will work  for your game.

You can analyze your conversion even in more detail if you know these additional approaches.

Separating payment types

To get a better understanding of users behavior, you need to calculate the conversion for the first payment and repeated payments separately as opposed to calculating it for all the payments at once. In this case, repeated payment conversion is the percentage of users who have made more than one payment during the analyzed period.

You can also divide the repeated payments by their number: 2nd, the 3rd and so on, and then you’ll be able to calculate the conversion for each of them separately.

It's important to ensure that your users continue making purchases after they made their first payments, as often the repeated payments bring the most part of the revenue.

Tracking time before the first payment

It's also important to understand when exactly your users start paying – immediately on their first day or after some time after they installed the app, when they get the feel of the game.

When you know that, you can find patterns in user behavior, affect it, and plan various marketing activities, increasing the probability of making payments.

In addition, if your game or app have levels or stages, it's worth considering the moment of the first (or any other) payment in the context of these stages. This division can be relevant for games with levels, as well as various educational or fitness apps where the user's progress can be divided into separate stages.

Using cohort analysis to study the metrics by days

Conversion is calculated for cohorts, and you can use this aspect to track its changes over time for a specific group of users, as well as to compare different cohorts with each other.

Date
Cohort's Size

 

Days from Install
1 2 3 4 5 6 7
25.01.2017 977 16% 13% 11% 5% 3% 1% 2%
26.01.2017 946 17% 13% 10% 6% 2% 2%  
27.01.2017 945 17% 14% 11% 5% 2%    
28.01.2017 1029 18% 14% 9% 6%      
29.01.2017 953 18% 13% 10%        
30.01.2017 995 20% 13%          
31.01.2017 972 17%            

Such analysis is very relevant while conducting  experiments – it's possible to estimate how the changes affect the conversion of cohorts that were formed before and after these changes.

There's one more metric, which is similar to conversion, but has a different meaning – Paying Share.

Paying share = Paying users / Active users

This metric differs from Conversion by its denominator, which is calculated from the entire active audience and is not tied to the installation date, nor does it require cohort analysis.

In addition to the fact that Conversion affects app's revenue, it's also an indicator of user's interest. Therefore, it is useful to study their behavior and needs in order to provide relevant offers, develop and improve the product. And remember, while tracking how all these changes affect the percentage of paying users, you should not forget to check other equally important financial metrics.

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