Maximize LTV
in free-to-play video games
no matter what
About us

We are incymo, an ML-based company that helps increase the profitability of any free-to-play video games.

For big and well optimized games, we use customized machine learning models trained on a specific genre to personalize in-game offers and get the maximum result.

For small and medium-sized games, we help with general monetization problems based on 100s AB tests and deep data analysis, and at the same time train a deep learning model to reduce the time between launch and the success of the company.

Our mission is to give players the opportunity to enjoy their favorite games more and help released games become more financially successful.

We strive for easy integration with games so that developers do not waste their valuable time. We are deeply immersed in all of our projects to offer only the best solutions.

Stage 1
We find out the needs of the client, study the quality of the player behavior model and the UX/UI of the marketing content of the game. After that, we compile an analytical report with information about the audience and possible improvement of the UX/UI in terms of delivering in-game offers for subsequent use through personalized changes to LTV augmentation algorithms. Please check out some of the examples given below.
Stage 2
After conducting analytics in step 1, we move on to integration and testing. Through the API we pass recommendations with the description of the offer to the client for each user as part of the A/B tests, training our AI and perfecting the result to the specified values.
We only work with anonymized data that holds no value outside the perimeter of the particular game. We do not need to know who your users are; we are only interested in their behavior.
Starting to work
Game Data Access
Users’ behavior and game events
Data analysis
Studying player patterns
Generating insights
When, to whom and in what form to pass the offer
Client decision
Decision to integrate
Setting up API
Setting up event submission
Creating offers
Identifying possible offers
Personalised model
Choosing an offer for the player
A/B tests
Running model-based tests
Comparing control and test groups
Finding out that the group with offers from Incymo has higher LTV
Insights examples
example 1
Showing an expensive offer to players at an early stage of the game (up to level X) increases the probability of churn by Y% (Y large)
example 2
Let us consider a segment of users who play slower than other players but spend a total of more than three hours in the game. In this case, the expectation of revenue from showing an expensive offer is X% higher than that from showing a cheaper one
example 3
If users complete the first X levels of the game in Y hours or less, their LTV will be Z% higher than the average, provided the expensive offers were shown (despite the current logic of offer showing)
Why choose us
If the LTV has grown by 3% or more, we charge 30% of additional revenue growth. Therefore, the client pays only for the result.
In a rare scenario we do not meet our 3% LTV increase goal you do not have to pay us!
We are also ready to discuss the formats of cooperation that are most convenient for our clients. Therefore, the client pays for the result only.
We will happily share our expertise and answer your questions.