Intelligent Customer Segmentation: A Data- Driven Framework for Targeted Advertising and Digital Marketing Analytics

Abstract

ABSTRACT: This paper suggests a smart, data-driven customer segmentation model to promote the use of targeted advertising and digital marketing in the current online markets. The framework combines machine learning algorithms, such as Principal Component Analysis (PCA) to reduce dimensionalities, K-Means and DBSCAN to perform unsupervised clustering and Gradient Boosting Machines (GBM) to predict segment-level responses, to identify meaningful groups of consumers using behavioral, demographic, and psychographic variables. The model was tested on a dataset of 50,000 records of customers in a digital retail setting. The methodology starts with data preprocessing, engineering of features, and reduction of multicollinearity with the help of PCA, which has retained 92 percent of the variance in 15 transformed components. The quality of segmentation was compared between K-Means and DBSCAN, and the former (k=5) has shown a better performance by the Silhouette Score (0.71) and Davies-Bouldin Index (0.42). The five customer groups formed thereafter showed some observable behavioral differences in the frequency of purchasing, the browsing behavior, responding to the campaigns, and expenditure. A GBM classifier to assess the effect of marketing was also trained to predict the likelihood of ad- clicks in clusters, with an F1-score of 0.87 and AUC of 0.93. Findings have shown that the use of campaigning methods where the segments were identified and applied significantly increased the click through rate (CTR) and conversion rate by 34% and 27% respectively relative to the campaigning methods that were not segmented. The most uplifted segments were high-value and high-engagement, which validates the use of machine learning-based segmentation. All in all, the suggested framework exhibits a high potential of facilitating individual tailored digital marketing approaches, better resource allocation, and market intelligence in the data-filled Internet ecosystems.