Features

Identify Churners

The starter kit provides models that identify churners and quantify the level of risk and expected time to churn for every individual. These insights can be used to devise personalized churn treatment strategies.

Determine the Optimal Treatment Type and Time

Many churn analytics solutions provide only the churn risk scores, making it challenging to operationalize. Our starter kit helps you develop treatment evaluation and optimization models that recommend optimal treatment type and time for each user.

Understand the Churn Drivers

The starter kit uses interpretable AI features provided by Vertex AI and custom diagnostic methods to provide advanced insight into user behavior and patterns that precede churn.

Incorporate User-Generated Content

We provide models that help extract useful signals from user-generated content such as customer reviews and call transcripts. These signals help improve the accuracy of churn prediction and determine your optimal churn prevention strategy.

Leverage the Power of AutoML

Most of the models included in the starter kit leverage Vertex AI AutoML services that help reduce the feature engineering and model design effort.

Industries

Our starter kit is created based on our experience with multiple clients from various industries.

Software as a service

Mitigate subscription cancellation or pricing plan downgrading risks in early stages.

Video games

Run targeted campaigns to prevent user engagement decline.

Mobile applications

Improve user retention and engagement through advanced user behavior analytics.

Why Develop Churn Analytics Solutions in Google Cloud

How It Works

The starter kit includes several components for churn risk evaluation, advanced churn behavior analytics and insight, and treatment optimization.

Learn More

Churn analytics in the technology and telecom industries using Google Vertex AI: A reference notebook
In this blog post, we develop a reference churn analytics pipeline that helps to evaluate the churn risk for individual users and recommends personalized churn treatment plans that can be executed by marketing teams.
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Customer churn prevention: A prescriptive solution using deep learning
The ability to identify and interpret churn patterns and prescribe the right treatment is as important as achieving churn prediction accuracy. In this article, we discuss how to build a solution that helps quantify, investigate, and fight customer churn, complaints, and any other issues related to customer dissatisfaction.
Read more
How to build and evaluate a Next Best Action model for customer churn prevention
In this article, we describe the design of the Next Best Action model that we commonly use in practice and elaborate on the methodology for offline efficiency evaluation.
Read more

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