Last time we were talking about the segmentation of paying users, reminisced RFM-analysis, as well as whales and dolphins.
This time we will also use segmentation, but on an entirely different principle. Have you ever thought about the structure of your revenue? Who brings more money - the new ones or the old ones? What is the ratio of the revenue from the new and the old users, how it changes over time? This is what we are going to talk about.
The structure of the audience as a whole
At first we divide our entire audience (both paying and not paying) into multiple segments of time from the moment of of their registration. How to select segments - the decision is exclusively yours and depends on the nature of your business and the period of engagement in the project.
Anyway, we recommend to go down to 5-7 segments.
1st segment - less than 14 days from the moment of registration;
2nd segment - from 14 to 30 days;
3d segment - from 1 to 2 months;
4th segment - from 2 to 6 months;
5th segment - from 6 months to 1 year;
6th segment - more than a year from the moment of registration.
By selecting custom segments, you may build a report on the structure of your audience at the time of analysis.
What does this report show:
If the newcomers clearly dominate - you have a problem with retention. The project can not retain user for a long term. And that means that you have to either work on retention, or think about monetization of the newcomers (for example, make the application paid one).
If the oldies clearly dominate - this is also not good. Is everything OK with the new registrations? May be it is the time to buy a bit of traffic? Remember that the more users, the more users. And oldies do not go far - sooner or later, the app starts to loose rating.
The next step may be to examine not only the structure of your audience but its dynamics - how this structure changed over time. Usually at this stage the most interesting things show up.
The structure of the paying audience
Let's perform the same manipulations but now only for the paying audience. For example, by report "Users & Gross structure" from devtodev.
This example shows how the stability of the size of your paying audience hides the pitfalls, and the growth of one segment is offset by a decrease of other segments.
We see that the percentage of newcomers (less than 30 days from the moment of registration) is increasing, and the percentage of oldies (6 to 12 months from the moment of registration) decreases. Without the consideration of the structure we would not notice this.
A sign of the healthy application is that the segment of the oldies should be slowly, but growing - more and more users should reach this segment and stay there.
The structure of the revenue
Finally, in a similar manner revenue may be analyzed: by cutting it into segments by the time from the moment of registration of users that make payments.
In the report on the structure of the revenue all distortions for the benefit of oldies and newcomers are usually more vividly pronounced. The fact is that usually (in the projects based on long-term retention) the average check of the newcomers is small, while the average check of the oldies is large enough.
As we see, the revenue in our example has a downward trend (remember that the size of the paying audience in this case was stable). And a decrease in this trend is primarily due to a decrease in revenue from the oldies. Up to the green segment, inclusive, there is some stability, and then decrease occurs.
Our verdict on considered project - the project has problems with payments from users who registered 3 months ago and earlier. It is necessary to optimize the long-term retention of the project so that the natural flow of users in the last segment exceeded the natural outflow.
With the above reports, you will be able to create a mathematical model of predicting your revenue for a few months in advance.
What is needed:
estimate the size of each of the selected segments;
for all segments calculate the probability of transition from the segment N to the segment N+1 (what is the probability of user being active during the month, remains active in the next month?);
calculate the average revenue per user (ARPU) of each segment.
By combining all calculated values in one model, you will be able to model how the structure of your audience and of revenue will change in a month, two, three, six.
Furthermore, this model will allow you to calculate the various experiments with traffic and monetization.
Examples of the questions it will be able to answer:
What if I disconnect the channel of paid traffic and remain only on the virality? How will this affect my revenue in 12 months?
What if I optimize retention (eg, 30 days retention) by 2%, how it will affect the structure of the audience and revenue?
I'm going to make a change in the balance of the game and thus raise the average check of user of 80’s level (which is reached after an average of six months of the game) by 10%. By what percentage my revenue will change?
And so on.
By this article we would like to convey to you one simple idea: it is important to study the structure of your audience and revenue by time from the moment of users registration. This will help you to make more informed and effective decisions, whether it's marketing, monetization or game design.