From the course: Deep Learning and Generative AI: Data Prep, Analysis, and Visualization with Python
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Deep learning: Predict customer lifetime value
From the course: Deep Learning and Generative AI: Data Prep, Analysis, and Visualization with Python
Deep learning: Predict customer lifetime value
- [Trainer] As a reminder, the goal of our use case is to predict a customer's potential lifetime value, helping the company prioritize high value customers and optimize marketing strategies. We found during exploratory data analysis that a scatter plot indicated a strong positive correlation between tenure and total revenue, which is how we can define customer lifetime value. But even though we found that strong correlation, creating a Keras model to predict customer lifetime value still has a major benefit. So when it comes to predicting customer lifetime value or CLV, Keras can leverage a wide range of customer data, including demographics, purchasing behavior, service usage patterns and more. It can also uncover and learn patterns in the data, and although our use case is considered not big data, Keras is designed to handle large data sets because it leverages underlying frameworks, like TensorFlow or Theano, which are optimized for high performance and scalability. But more…
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