Hands-On Data Science for Marketing

图书信息

作者	Yoon Hyup Hwang
出版社	Packt Publishing
ISBN	9781789348828
出版时间	2019-03-29
字数	48.7万
分类	进口书,外文原版书,电脑,网络

读书简介

Optimize your marketing strategies through analytics and machine learning Key Features * Understand how data science drives successful marketing campaigns * Use machine learning for better customer engagement, retention, and product recommendations * Extract insights from your data to optimize marketing strategies and increase profitability Book Description Regardless of company size, the adoption of data science and machine learning for marketing has been rising in the industry. With this book, you will learn to implement data science techniques to understand the drivers behind the successes and failures of marketing campaigns. This book is a comprehensive guide to help you understand and predict customer behaviors and create more effectively targeted and personalized marketing strategies. This is a practical guide to performing simple-to-advanced tasks, to extract hidden insights from the data and use them to make smart business decisions. You will understand what drives sales and increases customer engagements for your products. You will learn to implement machine learning to forecast which customers are more likely to engage with the products and have high lifetime value. This book will also show you how to use machine learning techniques to understand different customer segments and recommend the right products for each customer. Apart from learning to gain insights into consumer behavior using exploratory analysis, you will also learn the concept of A/B testing and implement it using Python and R. By the end of this book, you will be experienced enough with various data science and machine learning techniques to run and manage successful marketing campaigns for your business. What you will learn * Learn how to compute and visualize marketing KPIs in Python and R * Master what drives successful marketing campaigns with data science * Use machine learning to predict customer engagement and lifetime value * Make product recommendations that customers are most likely to buy * Learn how to use A/B testing for better marketing decision making * Implement machine learning to understand different customer segments Who this book is for If you are a marketing professional, data scientist, engineer, or a student keen to learn how to apply data science to marketing, this book is what you need! It will be beneficial to have some basic knowledge of either Python or R to work through the examples. This book will also be beneficial for beginners as it covers basic-to-advanced data science concepts and applications in marketing with real-life examples.

目录

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Preface

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

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Conventions used

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Reviews

Section 1: Introduction and Environment Setup

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

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

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

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

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

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

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

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

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

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

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

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

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

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