Paper Key : IRJ************198
Author: Ayush Deshmukh,Prof. Divya Munot
Date Published: 14 Nov 2024
Abstract
In todays digital-driven marketplace, understanding customer engagement is critical for crafting personalized marketing strategies. This study explores the application of the K-Nearest Neighbor (KNN) classification algorithm on a customer engagement dataset to segment customers into distinct categorieshighly engaged, moderately engaged, and inactive. Using engagement metrics, including website visits, email opens, and purchase history, we leverage KNN to classify customers based on their interaction patterns and purchasing behaviors, enabling targeted marketing initiatives aimed at maximizing engagement and retention.The primary challenge in this segmentation process lies in selecting relevant features and optimizing the model to accurately reflect varying engagement levels. To address this, we employ robust data preprocessing and feature engineering techniques to normalize engagement metrics and enhance the predictive capability of KNN. Hyperparameter tuning, specifically optimizing the value of K and the choice of distance metric, is conducted to improve classification accuracy. The models performance is evaluated using accuracy, precision, recall, and F1 scores, with comparative analyses against alternative classification methods such as decision trees and logistic regression to validate KNNs suitability for engagement-based segmentation tasks.future integration of hybrid models to enhance real-time segmentation capabilities. By providing a practical framework for engagement segmentation, this study supports data-driven marketing initiatives and highlights KNNs value in consumer behavior analysis.
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