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Customer Segmentation for Marketing Optimization
Project type
Classification
Skills
Exploratory Data Analysis (EDA), Data preprocessing, Customer segmentation methods, Model evaluation, Recommendation formulation, Scenario simulation, Data visualization
Tools
Python, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, Jupyter Notebook, RFM Analysis Tools, Clustering Algorithms (K-Means, Hierarchical Clustering)
Conducted a thorough Exploratory Data Analysis (EDA) to understand customer behavior and patterns, preparing the dataset for segmentation by cleaning, transforming, and visualizing key variables.
Applied advanced segmentation techniques, including RFM (Recency, Frequency, Monetary) analysis and other clustering methods, to categorize customers into distinct groups based on purchase behavior and satisfaction levels. Evaluated and compared model effectiveness, selecting the most appropriate method for meaningful customer differentiation.
Additionally, developed strategies for model maintenance, including simulations to determine the optimal update frequency, ensuring the segmentation model remains accurate and supports the client’s marketing team in targeting high-value customers efficiently over time.
