Chapter 1, Data Science and Marketing, covers the basics of how data science is used for marketing. It will briefly introduce frequently used data science and machine learning techniques and how those techniques are applied when it comes to creating better marketing strategies. It also covers how to set up your Python and R environments for upcoming projects.
Chapter 2, Key Performance Indicators and Visualizations, goes over some of the key performance indicators (KPIs) to track in marketing. This chapter discusses how Python and R can be used to compute such KPIs and how to build visualizations of those KPIs.
Chapter 3, Drivers behind Marketing Engagement, demonstrates how to use regression analysis to understand what drives engagement from customers. This chapter covers how to fit linear regression models in Python and R and how to extract the intercept and coefficients from a model. With the insights gathered from regression analysis, we will examine how we can potentially improve a marketing strategy for a higher engagement rate.
Chapter 4, From Engagement to Conversion, discusses how to use different machine learning models to understand what drives conversion. This chapter introduces you to how to build decision tree models in Python and R, as well as how to interpret the results and extract the drivers behind the conversions.
Chapter 5, Product Analytics, guides you through exploratory product analysis. This chapter walks you through various data aggregation and analysis methods in Python and R to obtain further insights into the trends and patterns in products.
Chapter 6, Recommending the Right Products, covers how to improve product visibility and recommend the right products that individual customers are most likely to purchase. It discusses how to use the collaborative filtering algorithm in Python and R in order to build a recommendation model. Then, it covers how these recommendations can be used for marketing campaigns.
Chapter 7, Exploratory Analysis for Customer Behavior, dives deeper into data. This chapter discusses various metrics that can be used to analyze how customers behave and interact with the product. Using Python and R, this chapter broadens your knowledge to encompass data visualization and different charting techniques.
Chapter 8, Predicting the Likelihood of Marketing Engagement, discusses how to build a machine learning model to predict the likelihood of customer engagement. This chapter covers how to train machine learning algorithms using Python and R. It then discusses how to evaluate the performance of the model and how these models can be used to achieve better target marketing.
Chapter 9, Customer Lifetime Value, covers how to get the lifetime value of individual customers. This chapter discusses how to build regression models using Python and R and how to evaluate them. It also covers how the computed customer lifetime value can be used for building better marketing strategies.
Chapter 10, Data-Driven Customer Segmentation, dives into segmenting the customer base using a data-driven approach. This chapter introduces clustering algorithms to build different customer segments from data using Python and R.
Chapter 11, Retaining Customers, discusses how to predict the likelihood of customer churn and focuses on building classification models using Python and R and how to evaluate their performances. This chapter will cover how to build an artificial neural network (ANN) model, which is the backbone of deep learning, in Python and R using the keras library.
Chapter 12, A/B Testing for Better Marketing Strategy, introduces a data-driven approach to making better decisions on marketing strategies. This chapter discusses the concept of A/B testing and how to implement and evaluate it using Python and R. It then discusses the real-life applications and benefits of A/B testing in relation to better marketing strategies.
Chapter 13, What's Next?, summarizes what has been discussed in this book, as well as real-life challenges in using data science for marketing. This chapter also introduces other data science and machine learning packages and libraries, as well as other machine learning algorithms that can be used for your future data science projects.
