- This project aims to develop a machine learning model to automate the process of scoring potential customers (lead scoring) in Customer Relationship Management (CRM). The primary goal is to provide a tool that helps businesses better understand the potential of each customer, thereby optimizing outreach strategies and enhancing sales effectiveness.
- This model allows businesses to automate the scoring of lead customers based on various criteria including online behavior, and interactions with campaigns. Below is a flow illustrating how the model operates:
- The model has achieved notable results in classifying and predicting potential customers with a high likelihood of conversion. Below are some charts and tables demonstrating the effectiveness of the model:
| SHAP | Train Accuracy | Train F1-score | Train Gini | Test Accuracy | Test F1-score | Test Gini |
|---|---|---|---|---|---|---|
| CatBoost 0 | 0.8388 | 0.870 | 0.830 | 0.8371 | 0.866 | 0.816 |
| CatBoost 1 | 0.787 | 0.793 | ||||
| LightGBM 0 | 0.8347 | 0.867 | 0.818 | 0.8355 | 0.865 | 0.817 |
| LightGBM 1 | 0.781 | 0.789 |
- This model helps businesses identify and focus resources on potential customers with high conversion potential, thereby optimizing sales and marketing strategies.
Hyperparameters Turning
CatBoost
LightGBM
Score
