User Churn Prediction

The text discusses the utilization of modern data warehousing and machine learning models to predict user churn in online apps. It emphasizes the importance of retention as a business metric and the benefits of using machine learning for user churn prediction. The approach involves dataset preparation, SQL-based model training, and leveraging BigQuery ML for model training and predictions. It highlights the key steps and considerations in preparing the dataset, model training, performance evaluation, and practical use of predictions to enhance user retention strategies. The comprehensive text provides practical insights and recommended reads to further understand the concept.

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Modern data warehousing and Machine Learning

Photo by Martin Adams on Unsplash

No doubt, user retention is a crucial performance metric for many companies and online apps. We will discuss how we can use built-in data warehouse machine learning capabilities to run propensity models on user behaviour data to determine the likelihood of user churn.

Key Takeaways

• Utilize data warehouse machine learning capabilities for user churn prediction
• Train models using standard SQL in modern data warehouses
• Understand and analyze user behavior to improve retention
• Use ML model insights to tailor user experiences and target relevant information

Modern Data Warehousing

Modern data warehouses offer useful features and ML model support that differentiate them from other data platform types.

Practical Solutions

• Utilize Binary Logistic Regression for fast model training
• Employ tools like BigQuery ML to democratize machine learning operations
• Use standard SQL for dataset preparation and model training

Dataset Preparation and Model Training

Perform exploratory data analysis on user behavior data to understand the user journey better.

Practical Steps

1. Analyze and preprocess raw event data from Firebase or Google Analytics
2. Create a training dataset for ML model with categorical and behavioral attributes
3. Train and evaluate ML models using tools like BigQuery ML

Model Training and Classification

Choose suitable model types such as Logistic Regression for fast training and utilize performance metrics to evaluate the models.

Model Types in BigQuery ML

• BOOSTED_TREE_CLASSIFIER
• Neural Networks
• AutoML Tables
• Logistic Regression

Using Predictions

Employ prediction data to understand user behavior, identify potential churn, and improve customer engagement.

Application of Predictive Data

• Identify user propensity to churn and tailor user experiences accordingly
• Automate customer engagement and manage interactions across all customer journey stages
• Use predictions to retarget users and enhance overall user retention

Conclusion

AI Solutions for User Churn Prediction can revolutionize your company’s approach to user retention and customer engagement. Explore the potential of AI for your business and stay ahead in the competitive landscape.

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List of Useful Links:

AI Lab in Telegram @aiscrumbot – free consultation

User Churn Prediction

Towards Data Science – Medium

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