Step-by-Step Analysis for Lead Installation Forecasting

Objective

This analysis aims to address the challenges faced by a leading broadband provider in optimizing lead conversion processes. The goal is to:

• Optimize the Lead Funnel:

  • Segment customers to identify high-conversion leads.
  • Allocate marketing budgets efficiently.
  • Prioritize high-potential leads for follow-ups.

• Develop a Predictive Model:

  • Identify features impacting lead conversions.
  • Build models to predict lead installation success.

Overview of Steps

1. Data Preprocessing

• Handled missing values in critical columns like days_to_accept and days_to_install_request.
• Renamed columns for better readability.
• Applied encoding (One-Hot and Label Encoding) for categorical variables.
• Balanced the target variable using SMOTE to handle class imbalance.

2. Exploratory Data Analysis (EDA)

• Visualized data distributions using histograms and countplots.
• Analyzed correlations between numerical variables and the target using heatmaps.
• Highlighted insights like key metrics (marketing_spend_inr, days_to_qualify) that influence conversions.

3. Predictive Modeling

• Built and compared multiple models:

  • Random Forest for robust feature importance.
  • XGBoost for handling complex patterns.
  • Voting Classifier to combine strengths of multiple models.

• Evaluated models using:

  • Classification Reports for precision, recall, and F1-score.
  • ROC-AUC Score to assess overall performance.
  • Confusion Matrices for understanding true/false positives and negatives.

4. Feature Importance and Recommendations

• Identified top features like days_to_install_request and marketing_spend_inr.
• Provided actionable strategies to prioritize resources and improve marketing efficiency.

Tools and Techniques Used

• Libraries: pandas, numpy, seaborn, matplotlib, scikit-learn, XGBoost, imblearn.

Preprocessing:

• Missing value imputation.
• Encoding categorical variables.
• Balancing classes with SMOTE.

Visualization:

• Heatmaps, countplots, histograms, and barplots.

Machine Learning Models:

• Random Forest, XGBoost, and Voting Classifier.

Key Deliverables

• Data Cleaning and EDA:

  • Insights into lead behaviors and operational metrics.

• Feature Importance Analysis:

  • Identification of critical predictors for conversions.

• Predictive Model Performance:

  • Models evaluated on precision, recall, and ROC-AUC score.

• Actionable Recommendations:

  • Strategies for improving lead prioritization and marketing efficiency.