Push Notification Optimization for Apps Using Machine Learning

2021.01.13 (Wed)
Use Case
Machine Learning Data Science Game

This article has been translated from Japanese into English using DeepL.

Are you just sending them?

As we all know, push notification feature is an effective way to increase user engagement in smartphone applications and games.
It has been shown that users who don't receive push notifications from the start are more likely to leave than those who do, and that users who open multiple push notifications per day have higher retention rates than those who don't.
However, if you keep sending push notifications to your users, you may end up churning them instead of increasing their engagement.

Optimizing with Machine Learning

In order to avoid such a worst-case scenario, the ideal push notification is to deliver effective content to the right user at the ideal time.
The solution to achieve this is to optimize push notifications using machine learning.
In recent years, predictive analytics based on machine learning has started to be used in various fields. Application development is no exception. Machine learning data analysis can help you personalize your push notifications and extract maximum value from your users.
Photo by Jamie Street on Unsplash

Machine Learning Modeling with VARISTA

Normally, building a machine learning model like the one above requires programming skills and the manpower to implement it.
By using VARISTA, a non-coding tool, the cost of developing these models can be greatly reduced.
By learning the log data of push notifications that have been sent to users, it is possible to build a model that predicts which users will open push notifications when they are sent.
It is also possible to analyze factors that affect the open rate at that time.

Use of predictive models

By applying the predictive model built by VARISTA on whether users will open push notifications or not, it is possible to verify at what time of the day the open rate is high, what kind of content will attract paying users, and which notification is more effective.
By switching to data-driven decision making, where directors and marketers used to make decisions based on their senses, it is possible to improve both efficiency and de-personalize.


We have seen that by using machine learning in app development, it is possible to operate more efficiently than ever before.
In the future, more and more tasks will be automated using predictions from machine learning models.
How about taking up the challenge now, anticipating the long-term effects?

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