Section 2: Descriptive Versus Explanatory Analysis; Chapter 2: Key Performance Indicators and Visualizations; KPIs to measure performances of different marketing efforts; Sales revenue; Cost per acquisition (CPA); Digital marketing KPIs; Computing and visualizing KPIs using Python; Aggregate conversion rate; Conversion rates by age; Conversions versus non-conversions; Conversions by age and marital status; Computing and visualizing KPIs using R; Aggregate conversion rate; Conversion rates by age; Conversions versus non-conversions; Conversions by age and marital status; Summary.

This book will be an excellent resource for both Python and R developers and will help them apply data science and machine learning to marketing with real-world data sets. By the end of this book, you will be well equipped with the required knowledge and expertise to draw insights from data and improve your marketing strategies.

• Cover
• Title Page
• Copyright and Credits
• About Packt
• Contributors
• Table of Contents
• Preface
• Section 1: Introduction and Environment Setup
• Chapter 1: Data Science and Marketing
  • Technical requirements
  • Trends in marketing
  • Applications of data science in marketing
    • Descriptive versus explanatory versus predictive analyses
    • Types of learning algorithms
    • Data science workflow
  • Setting up the Python environment
    • Installing the Anaconda distribution
    • A simple logistic regression model in Python
  • Setting up the R environment
    • Installing R and RStudio
    • A simple logistic regression model in R
  • Summary
• Section 2: Descriptive Versus Explanatory Analysis
• Chapter 2: Key Performance Indicators and Visualizations
  • KPIs to measure performances of different marketing efforts
    • Sales revenue
    • Cost per acquisition (CPA)
    • Digital marketing KPIs
  • Computing and visualizing KPIs using Python
    • Aggregate conversion rate
    • Conversion rates by age
    • Conversions versus non-conversions
    • Conversions by age and marital status
  • Computing and visualizing KPIs using R
    • Aggregate conversion rate
    • Conversion rates by age
    • Conversions versus non-conversions
    • Conversions by age and marital status
  • Summary
• Chapter 3: Drivers behind Marketing Engagement
  • Using regression analysis for explanatory analysis
    • Explanatory analysis and regression analysis
    • Logistic regression
  • Regression analysis with Python
    • Data analysis and visualizations
      • Engagement rate
      • Sales channels
      • Total claim amounts
    • Regression analysis
      • Continuous variables
      • Categorical variables
      • Combining continuous and categorical variables
  • Regression analysis with R
    • Data analysis and visualization
      • Engagement rate
      • Sales channels
      • Total claim amounts
    • Regression analysis
      • Continuous variables
      • Categorical variables
      • Combining continuous and categorical variables
  • Summary
• Chapter 4: From Engagement to Conversion
  • Decision trees
    • Logistic regression versus decision trees
    • Growing decision trees
  • Decision trees and interpretations with Python
    • Data analysis and visualization
      • Conversion rate
      • Conversion rates by job
      • Default rates by conversions
      • Bank balances by conversions
      • Conversion rates by number of contacts
    • Encoding categorical variables
      • Encoding months
      • Encoding jobs
      • Encoding marital
      • Encoding the housing and loan variables
    • Building decision trees
    • Interpreting decision trees
  • Decision trees and interpretations with R
    • Data analysis and visualizations
      • Conversion rate
      • Conversion rates by job
      • Default rates by conversions
      • Bank balance by conversions
      • Conversion rates by number of contacts
    • Encoding categorical variables
      • Encoding the month
      • Encoding the job, housing, and marital variables
    • Building decision trees
    • Interpreting decision trees
  • Summary
• Section 3: Product Visibility and Marketing
• Chapter 5: Product Analytics
  • The importance of product analytics
  • Product analytics using Python
    • Time series trends
    • Repeat customers
    • Trending items over time
  • Product analytics using R
    • Time series trends
    • Repeat customers
    • Trending items over time
  • Summary
• Chapter 6: Recommending the Right Products
  • Collaborative filtering and product recommendation
    • Product recommender system
    • Collaborative filtering
  • Building a product recommendation algorithm with Python
    • Data preparation
      • Handling NaNs in the CustomerID field
      • Building a customer-item matrix
    • Collaborative filtering
      • User-based collaborative filtering and recommendations
      • Item-based collaborative filtering and recommendations
  • Building a product recommendation algorithm with R
    • Data preparation
      • Handling NA values in the CustomerID field
      • Building a customer-item matrix
    • Collaborative filtering
      • User-based collaborative filtering and recommendations
      • Item-based collaborative filtering and recommendations
  • Summary
• Section 4: Personalized Marketing
• Chapter 7: Exploratory Analysis for Customer Behavior
  • Customer analytics – understanding customer behavior
    • Customer analytics use cases
      • Sales funnel analytics
      • Customer segmentation
      • Predictive analytics
  • Conducting customer analytics with Python
    • Analytics on engaged customers
      • Overall engagement rate
      • Engagement rates by offer type
      • Engagement rates by offer type and vehicle class
      • Engagement rates by sales channel
      • Engagement rates by sales channel and vehicle size
    • Segmenting customer base
  • Conducting customer analytics with R
    • Analytics on engaged customers
      • Overall engagement rate
      • Engagement rates by offer type
      • Engagement rates by offer type and vehicle class
      • Engagement rates by sales channel
      • Engagement rates by sales channel and vehicle size
    • Segmenting customer base
  • Summary
• Chapter 8: Predicting the Likelihood of Marketing Engagement
  • Predictive analytics in marketing
    • Applications of predictive analytics in marketing
  • Evaluating classification models
  • Predicting the likelihood of marketing engagement with Python
    • Variable encoding
      • Response variable encoding
      • Categorical variable encoding
    • Building predictive models
      • Random forest model
      • Training a random forest model
      • Evaluating a classification model
  • Predicting the likelihood of marketing engagement with R
    • Variable encoding
      • Response variable encoding
      • Categorical variable encoding
    • Building predictive models
      • Random forest model
      • Training a random forest model
      • Evaluating a classification model
  • Summary
• Chapter 9: Customer Lifetime Value
  • CLV
  • Evaluating regression models
  • Predicting the 3 month CLV with Python
    • Data cleanup
    • Data analysis
    • Predicting the 3 month CLV
      • Data preparation
      • Linear regression
      • Evaluating regression model performance
  • Predicting the 3 month CLV with R
    • Data cleanup
    • Data analysis
    • Predicting the 3 month CLV
      • Data preparation
      • Linear regression
      • Evaluating regression model performance
  • Summary
• Chapter 10: Data-Driven Customer Segmentation
  • Customer segmentation
  • Clustering algorithms
  • Segmenting customers with Python
    • Data cleanup
    • k-means clustering
    • Selecting the best number of clusters
    • Interpreting customer segments
  • Segmenting customers with R
    • Data cleanup
    • k-means clustering
    • Selecting the best number of clusters
    • Interpreting customer segments
  • Summary
• Chapter 11: Retaining Customers
  • Customer churn and retention
  • Artificial neural networks
  • Predicting customer churn with Python
    • Data analysis and preparation
    • ANN with Keras
    • Model evaluations
  • Predicting customer churn with R
    • Data analysis and preparation
    • ANN with Keras
    • Model evaluations
  • Summary
• Section 5: Better Decision Making
• Chapter 12: A/B Testing for Better Marketing Strategy
  • A/B testing for marketing
  • Statistical hypothesis testing
  • Evaluating A/B testing results with Python
    • Data analysis
    • Statistical hypothesis testing
  • Evaluating A/B testing results with R
    • Data analysis
    • Statistical hypothesis testing
  • Summary
• Chapter 13: What's Next?
  • Recap of the topics covered in this book
    • Trends in marketing
    • Data science workflow
    • Machine learning models
  • Real-life data science challenges
    • Challenges in data
    • Challenges in infrastructure
    • Challenges in choosing the right model
  • More machine learning models and packages
  • Summary
• Other Books You May Enjoy
• Index