Machine Learning Data to Reduce Churn & Boost Engagement

1) The Differences Between AI, Machine Learning and Predictive Analytics

AI, Machine Learning and Predictive Learning are often put together and sometimes mistaken for each other, but they are three different tools that can help marketers make decisions based on data. To help differentiate between the three, we used the example of how a Roomba works.

2) Segment Audiences Based on Churn Risk to Boost Results

Urban Airship built a powerful machine learning model that uses app data to predict churn, and simplifies the concept into three groups: High, Medium, and Low. Here’s our advice on how to approach each risk group:

3) Segment Your Audience to Send Test Notifications

No one wants to spam their customer, but you also want to make sure that you’re sending the right amount of notifications to get the best engagement. Segmenting your audience by risk can also separate the right audience to test those additional pushes.

4) The Best Time to Send a Notification May Surprise You

When is the best time to send a notification? It depends not only on the brand, but also by user. Sending a message during a time an individual is most likely to engage has a positive impact on direct open rates, and that window of time may be outside daytime hours. For one brand, Urban Airship’s Send Time Optimization data showed that the highest engagement hours were actually the late night hours, with high activity happening even between 1 a.m. and 4 a.m.



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