Scenarios and Best Practices

Example of game analysis

Monetization

The goal is to have paying users who will return to the game as often as possible. To do this, we must analyze Retention. We will analyze it a little bit later, but for now let's see what we have with the Conversion to the first payment. The Conversion to payments report will help us with this.

We can compare the conversion to the first payment by applying to the report the segments of users who pass the tutorial and those who missed it. It is possible that users whom we brought to the store during the tutorial pay us more and more often than users who refused to study.

Segmentation can be applied to all reports, see the data in the context of various events and in the context of users. Using user segmentation allows you to get more insights from reports.

Let's see how quickly users begin to pay: at the first day or a few days after installation when they better understand the game. So we can determine which day it is worth planning marketing activities to increase the likelihood of payment.

The graph shows that the majority of the first payments are made in the early days, which means we can show users a special offer almost immediately.

It is clear with the first payment, but it’s also important to ensure that users who make the first payment do not stop there. Let's see if the users continue to make subsequent payments, because often repeated payments bring more income, which we could see in the table above. In the same Conversion to payments report, we analyze the conversion to subsequent payments:

After that, we should look at the structure of paying users to evaluate how often, how long and how much users have paid us, we can see it in the RFM analysis report.

• The segment Can’t Lose Them. These customers made the biggest purchases, and made them often. But they haven’t returned for a long time. Try to win them back by offering them renewals or newer offers, features. There is a chance to lose them to competition, try talking to them.

• Loyal Customers. Customers who spend often good amounts of money. They are responsive to promo activities. Try to upsell higher value products. Ask for reviews and think how you can engage them.

• Сhampions. Customers who bought recently, buy often and spend the most money. Try to reward them. These customers can be early adopters for new offers and features. They will promote your app.

• At Risk. These customers spent big sums of money and made purchases often but a long time ago. You need to bring them back. Try to reconnect with customers by sending them personalized push notifications, offering renewals, and providing helpful resources.

• Need Attention. Customers in this segment have above average recency, frequency and monetary values. They may not have made purchases very recently though. Try making time limited offers, recommend based on past purchases, and reactivate them.

• Potential Loyalists. These are recent customers who spent a good amount and made purchases more than once. Try offering membership or loyalty programs, and recommend other products.

• Hibernating. Last purchase by these customers was a long time ago. They are low spenders and have a low number of orders. Try offering them other relevant products and special discounts. You can also promote new features and modes.

• About To Sleep. Customers have below average recency, frequency and monetary values. You will lose them if they remain not reactivated. Try sharing valuable resources, recommend popular products, renewals at discount, and reconnect with them.

• Promising. These customers are recent payers but they haven’t spent much. Try making time limited offers and persuade them to use the app more frequently.

• New Payers. These users have just converted to payers. Try engaging them to use the app more frequently and give them bigger discounts.

Now let's look at the structure of our audience in the Gross structure report, segmenting it into beginners and oldies. Our audience is divided into six segments by the registration time:

It can be seen that we are dominated by old users. Perhaps, it’s worth attracting new traffic to our game. With an increase in traffic, the number of paying audience also increase.

If there were more new users, it would be worth thinking about retaining new users.

We figured out the structure of the audience. Let's see what happens with the dynamics of paying users:

The graphs show that users who register 1 months ago and earlier pay less and less. This may indicate an oversupply of currency. You can also notice the uneven distribution of income from beginners, most likely, at the peak there was a campaign for beginners. Perhaps it is worth concentrating marketing communications specifically on new users.

And at what level do users start paying us? Let's go back to the Conversion to payments report and see at what level the user most often makes the first and the second purchases:

Thanks to this information, we understand when to motivate the user to buy.

Let’s evaluate how the game economy is balanced at each level. Let's go to the Currency balances by level report, it will show the average and total accumulations, purchases, expenses and earnings for each game’s level.

Now let's see what we have with retention:

We will check the traffic to see if we buy that users that we need.The Basic Metrics will help us with the traffic analysis.

The report shows that traffic from google adwords has a downward trend line, on the contrary, organics are growing. Users from different sources perceive our game differently.

It is also worth to segment reports when analyzing by platform, traffic sources, country, version and other criteria, as the behavior of different categories of users may differ.

We will check what is our game’s retention, whether it’s always been at this level. Let’s go to the Engagement dashboard or Basic Metrics.

We see that retention has begun to decline, but maybe the problem is not in all versions of the game. Cohort analysis and App versions analysis will help with this. In the App versions analysis, we will see the audience, the distribution of users across all versions of the game, how they go through the tutorial, users’ deduction and the amount of payments by game’s version.

In the Cohort analysis we compare the behavior of different cohorts with different registration dates. Let's see how cohort / segment retention rates change, what is the average check and other metrics:

With what retention do we have problems?

If the problem is with short-term retention, the Tutorial analysis reports will help us.

How are things going with medium-term retention? Let’s take a look at the report on levels’ difficulty. We should go to the Game structure report:

What about long-term retention? First, let’s take a look at the Cohort analysis report:

We can also build a sequence of users’ actions, for example to see what they do after a push about a new event in the game. To do this, go to the User flow report and set the event from which we want to build the analysis:

To analyze whether users enter events, watch ads, interact with other users, add friends and etc., you should go to the Custom events report:

If you have a match-3 game, you will find some additional tips on analyzing the game in this article.