Google Ads Machine Learning Prediction System: Digital Marketing Analysis Combining Supervised Regression and Unsupervised Clustering

This project was developed by RafaelGallo, aiming to solve the lead identification problem in digital marketing by combining supervised regression and unsupervised clustering techniques. Its core value lies in using these two machine learning paradigms to mine Google Ads data from different perspectives, providing data support for marketing budget planning, precise targeting, and strategy optimization. It is a typical application case of data science in the digital marketing field.

The digital marketing field has long faced a core challenge: how to identify high-value leads from the massive data in Google Ads? The platform generates hundreds of millions of ad impressions, clicks, and conversion data daily. Marketers need to understand data patterns to predict leads, optimize delivery strategies, and improve return on investment (ROI). This project was created to address this problem.

Feature Engineering: Covers ad dimensions (campaign type, keyword matching, etc.), user dimensions (geographic location, device type, etc.), performance dimensions (CTR, CVR, etc.), and time dimensions (day of the week, seasonality, etc.). Raw data needs to be converted into numerical representations and integrated with business knowledge. Model Selection: Considers linear regression variants (strong interpretability), tree ensemble methods (captures non-linear interactions), and neural networks (excellent performance in high-dimensional spaces). Evaluation metrics include RMSE, MAE, MAPE, which need to align with business objectives.

Business Value: Reveals high-value user profiles, ad campaign grouping, conversion path patterns, and anomaly detection. Common Algorithms: K-Means (efficient for large-scale data), hierarchical clustering (provides hierarchical relationships), DBSCAN (identifies clusters of arbitrary shapes), Gaussian Mixture Model (soft clustering). Results need to be business-interpretable, such as "Weekend mobile users with high clicks but low conversions".

1. Clustering Guided Regression: Model by groups after clustering users/campaigns to improve prediction accuracy (e.g., B2B/B2C grouping);
2. Regression Validates Clustering: Identify samples with large prediction errors through residual analysis and adjust clustering strategies;
3. Hierarchical Strategy Formulation: Increase budget for high-potential clusters, optimize landing pages for low-conversion clusters, and conduct small-scale tests for new clusters.

• Data Quality First: Resolve issues like tracking gaps and attribution disputes, and establish reliable data processes;
• Feature Timeliness: Retrain models regularly to adapt to environmental changes;
• A/B Test Validation: Confirm the business value of the model through controlled experiments;
• Interpretability Requirements: Use techniques like SHAP/LIME to enhance model credibility.

This project demonstrates the practical application of data science in digital marketing. By using supervised regression to predict lead quantity and unsupervised clustering to discover segments, the combination of both provides comprehensive support for marketing decisions. For data scientists and marketing analysts, it is a valuable reference case for applying machine learning to business problems.