Marketing campaigns play a crucial role in promoting products and services, and understanding the behavior of customers is essential for designing effective strategies. In this post, I conduct an Exploratory Data Analysis (EDA) on a marketing campaign dataset. The goal is to gain insights into customer behavior and uncover patterns that can inform marketing strategies.
The dataset we'll be working with is sourced from Kaggle (link: Marketing Campaign Dataset). It provides valuable information about customer interactions with marketing campaigns, offering an opportunity to understand their characteristics, preferences, and shopping behaviors.
In this analysis, I clean the data, reduce its dimensionality with PCA algorithm, cluster similar customers using K-Means algorithm, and identify common characteristics within each cluster.
By the end of this EDA, my goal is to discover actionable insights that can help develop personalized marketing strategies and enhancing customer engagement. Let's delve into the details of the exploratory analysis, and discover the valuable information hidden within this marketing campaign dataset.
