Скачать или смотреть Why Demographics Don't Predict Value: CLV Data Exploration

Is "High Income" the best predictor of Customer Lifetime Value?

Welcome to Part 2 of our Hybrid CLV Series. In the previous video, we established the theory: fusing probabilistic BTYD models with Machine Learning.

Now, we open Notebook 1 to start the implementation. We are working with the FinTech Customer LTV Dataset, a synthetic dataset representing a digital wallet platform. In this walkthrough, we build the foundation of our pipeline by ingesting raw data, converting it to Spark for scalability, and performing a deep Exploratory Data Analysis (EDA).

Key Takeaways & Code Walkthrough:
• Scalable Ingestion: How to convert Pandas DataFrames to PySpark for SQL querying.
• The "Strong" Signal: Our correlation matrix reveals that Total_Spent and Frequency are the kings of prediction (r = 0.65+).
• The "Weak" Signal: Boxplot analysis proves that traditional demographics (Income, Location, Age) have almost zero predictive power for this dataset.
• Synthetic Baselines: We discuss the "perfect" 1.00 correlation between Spend and LTV and how we will use this as a baseline for our Hybrid Model.

Up Next in Part 3: Now that we know Transaction History is the key predictor, we will use it to engineer the "Secret Ingredient." In the next video, we calculate P(Alive) using the lifetimes library.

#DataScience #PySpark #EDA #Databricks #CLV #Python #MachineLearning #FinTech