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Rewarded Video

We know that you all dream of building the next iconic game that will be remembered by future generations, you generally spend a lot of time thinking about some of the fundamental questions for a sustainable business.

How much can I spend on acquiring a new player?
What is the potential value of this user versus another one?
When will my players churn and what can I do to prevent it?


And of course, building the models that help to predict the monetization of your game players. We are here to take advantage of the benefits data can bring.

For hyper-casual games, your monetization mostly depends on the revenue which is coming from the rewarded video. The most important issue to be considered here is that the frequency and amount of watched videos are quite effective on your profitability.

As a result of the data analysis, the inferences are as follows;
Frequent session entries and rewarded video watching are more profitable than a single session entry and multiple rewarded video watching. The marginal benefit obtained from rewarded video watching decreases considerably in consecutive video watching.

Based on this finding, we define two types of user segments. One of them is labeled as “Valuable” who logs in to the game frequently and watches an average of 6 rewarded videos in each session. And of course, we have “Not Valuable” who also logs in to the game frequently and watch average 2 rewarded videos in each session. Here we focused on the “Not Valuable”, because it would be much more profitable if they start to watch more rewarded videos.

After that, we have solved out how “Not Valuable” acts in the game. How they play the game and how they will behave in the future. So we start to predict whether new users are “Not Valuable” or “Valuable”. If they are “Not Valuable”, the reward they receive after watching the video is improved.

As a result of this arrangement, the average number of watched rewarded videos increased from 2 to 4. And of course, the total profit from this group increased by 32%.

Player Segmentation

Higher Ad Revenue

Saving Human Resources

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