Data as a Compass: Exploring Statistics, Machine Learning, and Marketing Insights

Over the past years, I’ve had the privilege of applying a wide array of statistical and machine learning methods. These tools have empowered me to uncover insights, optimize campaigns, and drive measurable results in marketing analytics. Here’s a closer look at what I’ve explored:

Statistical Methods

• Linear Regression: Models the relationship between a dependent variable and one or more independent variables by fitting a linear equation.
• Logistic Regression: Used for binary outcomes, it models the probability that a given input belongs to a certain category.
• Ridge Regression: A variation of linear regression that adds L2 regularization to prevent overfitting.
• SARIMAX Forecasting: Seasonal AutoRegressive Integrated Moving Average with eXogenous variables; predicts time series data considering trends, seasonality, and external factors.

Machine Learning Methods

• Random Forest: An ensemble of decision trees that improves prediction accuracy and reduces overfitting.
• Gradient Boosting: Builds models sequentially, where each model corrects errors of the previous one, often used for predictive tasks.
• K-Nearest Neighbors (KNN): Classifies data points based on the majority label of their nearest neighbors.
• Support Vector Machine (SVM): Finds the optimal hyperplane that separates classes in high-dimensional space.

Clustering Methods

• KMeans: Partitions data into clusters based on minimizing variance within each cluster.
• DBSCAN: Density-Based Spatial Clustering of Applications with Noise; identifies clusters of varying shapes and isolates outliers.
• HDBSCAN: Hierarchical DBSCAN; extends DBSCAN by creating a hierarchy of clusters for more flexibility.

Dimensionality Reduction

• PCA: Principal Component Analysis; reduces the number of features while retaining most of the data variance.
• t-SNE: t-Distributed Stochastic Neighbor Embedding; visualizes high-dimensional data in 2D or 3D while preserving local structure.
• UMAP: Uniform Manifold Approximation and Projection; similar to t-SNE but faster and preserves more of the global data structure.

Next Stages: Learning Bayesian Statistics and Marketing Mix Modeling

While these methods have been powerful, I am particularly excited to deepen my expertise in Marketing Mix Modeling (MMM) and Bayesian statistics. These frameworks allow us to model uncertainty, leverage prior knowledge, and make more robust, probabilistic decisions.

My Learning Roadmap

• Dive into Bayesian linear and hierarchical models using PyMC or Robyn.
• Explore probabilistic Marketing Mix Models, combining past data with expert knowledge.
• Apply posterior inference and uncertainty quantification to real-world campaign data.
• Integrate MMM insights into media planning, automated bidding, and ROI optimization.
• Share learnings via case studies, internal guides, and community content.

The more I learn, the more I realize that data is an endless frontier. Every analysis opens new questions, every model offers new perspectives, and every insight propels us forward.