Data-driven personalized marketing strategies
Table Of Contents
1. Introduction
1.1 Background
1.2 Motivation
1.3 Objectives
2. Literature Review
2.1 Traditional vs. personalized marketing
2.2 Data-driven marketing techniques
2.3 Customer segmentation methods
3. Data Collection and Preprocessing
3.1 Sources of customer data
3.2 Data cleaning and preprocessing
4. Customer Segmentation
4.1 Clustering algorithms for segmentation
4.2 Feature selection and extraction
5. Predictive Modeling for Personalization
5.1 Machine learning algorithms for prediction
5.2 Model evaluation and validation
Thesis Abstract
This project aims to explore the application of data-driven techniques in developing personalized marketing strategies. Traditional mass marketing approaches are being replaced by personalized marketing strategies that leverage customer data to deliver targeted and relevant content. This project will investigate the use of machine learning algorithms, customer segmentation techniques, and predictive modeling to create personalized marketing campaigns. The goal is to improve customer engagement, conversion rates, and overall marketing effectiveness.
Thesis Overview
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