Machine Learning and Artificial Intelligence in Marketing and Sales: Essential Reference for Practitioners and Data Scientists

Cover
Machine Learning and Artificial Intelligence in Marketing and Sales
Praise for Machine Learning and Artificial Intelligence in Marketing and Sales
Machine Learning and Artificial Intelligence in Marketing and Sales: Essential Reference for Practitioners and Data Scientists
Copyright
Dedication
Table of Contents
List of Figures, Tables and Illustrations
Foreword
Preface
Acknowledgments
Introduction
1. Introduction and Machine Learning Preliminaries: Training and Performance Assessment
	Chapter Outline
	1. Training of Machine Learning Models
		1.1 Regression and Classification Models
		1.2 Cost Functions and Training of Machine Learning Models
		1.3 Maximum Likelihood Estimation
		1.4 Gradient-Based Learning
	2. Performance Assessment for Regression and Classification Tasks
		2.1 Performance Assessment for Regression Models
		2.2 Performance Assessment for Classification
			2.2.1 Percent Correctly Classified (PCC) and Hit Rate
			2.2.2 Confusion Matrix
			2.2.3 Receiver Operating Characteristics (ROC) Curve and Area under the Curve (AUC)
			2.2.4 Cumulative Response Curve and Lift (Gains) Chart
			2.2.5 Gini Coefficient
	Technical Detour 1
	Technical Detour 2
2. Neural Networks in Marketing and Sales
	Chapter Outline
	1. Introduction to Neural Networks
		1.1 Early Evolution
		1.2 The Neural Network Model
			1.2.1 NN for Regression
			1.2.2 NN for Classification
		1.3 Cost Functions and Training of Neural Networks Using Backpropagation
		1.4 Output Nodes
			1.4.1 Linear Activation Function for Continuous Regression Outputs
			1.4.2 Sigmoid Activation Function for Binary Outputs
			1.4.3 Softmax Activation Function for Multiclass Outputs
	2. Feature Importance Measurement and Visualization
		2.1 Neural Interpretation Diagram (NID)
		2.2 Profile Method for Sensitivity Analysis
		2.3 Feature Importance Based on Connection Weights
		2.4 Randomization Approach for Weight and Input Variable Significance
		2.5 Feature Importance Based on Partial Derivatives
	3. Applications of Neural Networks to Sales and Marketing
	4. Case Studies
		Case Study 1: Churn Prediction
		Case Study 2: Rent Value Prediction
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		Linear Activation Function for Continuous Regression Outputs
		Sigmoid Activations Function for Binary Outputs
		Softmax Activation Function for Multi-class Outputs
3. Overfitting and Regularization in Machine Learning Models
	Chapter Outline
	1.  Hyperparameters, Overfitting, Bias-variance Tradeoff, and Cross-validation
		1.1 Hyperparameters
		1.2 Overfitting
		1.3 Bias-variance Tradeoff
		1.4 Cross-validation
	2. Regularization and Weight Decay
		2.1 L2 Regularization
		2.2 L1 Regularization
		2.3 L1 and L2 Regularization as Constrained Optimization Problems
		2.4 Regularization through Input Noise
		2.5 Regularization through Early Stopping
		2.6 Regularization through Sparse Representations
		2.7 Regularization through Bagging and Other Ensemble Methods
	Technical Detour 1
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		Weight Decay in L2 Regularization
		Weight Decay in L1 Regularization
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	Technical Detour 8
4. Support Vector Machines in Marketing and Sales
	Chapter Outline
	1 Introduction to Support Vector Machines
		1.1 Early Evolution
		1.2 Nonlinear Classification Using SVM
	2 Separating Hyperplanes
	3 Role of Kernels in Machine Learning
		3.1 Kernels as Measures of Similarity
		3.2 Nonlinear Maps and Kernels
		3.3 Kernel Trick
	4 Optimal Separating Hyperplane
		4.1 Margin between Two Classes
		4.2 Maximal Margin Classification and Optimal Separating Hyperplane
	5 Support Vector Classifier and SVM
	6 Applications of SVM in Marketing and Sales
	7 Case Studies
		Case Study 1: Consumer Choice Modeling
		Case Study 2: Rent Value vs Location
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	Illustration 3
	Illustration 4
	Illustration 5
5. Random Forest, Bagging, and Boosting of Decision Trees
	Chapter Outline
	1. Early Evolution of Decision Trees: AID, THAID, CHAID
	2. Classification and Regression Trees (CART)
		2.1 Regression Trees
			2.1.1 Greedy Algorithm
			2.1.2 Cost Complexity Pruning
		2.2 Classification Trees
	3. Decision Trees and Segmentation
	4. Bootstrapping, Bagging, and Boosting
		4.1 Bootstrapping
		4.2 Bagging
		4.3 Boosting
	5. Random Forest
	6. Applications of Random Forests and Decision Trees in Marketing and Sales
	7. Case Studies
		Case Study 1: Caravan Insurance
		Case Study 2: Wine Quality
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References
Index