Hands-On Data Science for Marketing

Improve your marketing strategies with machine learning using Python and R

Yoon Hyup Hwang

• Publisher: Packt Publishing
• Year: 2019
• ISBN:
  9781789348828
• Edition: 1
• Topic: Computer Science
• Subtopic: Data Modelling & Design
• Index: Computer Science

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.

Section 1: Introduction and Environment Setup

This section will introduce you to data science for marketing and setting up Python and R environments for the upcoming projects.

This section consists of the following chapter:

• Chapter 1, Data Science and Marketing

Data Science and Marketing

Welcome to the first chapter of Hands-On Data Science for Marketing! As you may be familiar already, the importance and application of data science in the marketing industry have been rising significantly over the past few years. Yet, marketing data science is a relatively new field and the amount of resources available for education and references lags behind the momentum. However, the amount of data gathered and available to the process has been growing exponentially each year, which opens up even more opportunities to learn and bring insight from the data.

With the growing amount of data and applications of data science in marketing, we can easily find examples of the usage of data science to marketing efforts. Companies are starting to use data science to better understand customer behaviors and identify different customer segments based on their activity patterns. Many organizations also use machine learning to predict future customer behaviors, such as what items are they likely to purchase, which websites are they likely to visit, and who are likely to churn. With endless use cases of data science for marketing, companies of all sizes can benefit from using data science and machine learning for their marketing efforts. After this brief introductory chapter, we will learn about how to apply data science and machine learning for individual marketing tasks.

In this chapter, we will cover the following topics:

• Trends in marketing
• Applications of data science in marketing
• Setting up the Python environment
• Setting up the R environment

Technical requirements

You will require Python and R installed to run most of the code throughout this book, and you can find the installation code at the following link: https://github.com/PacktPublishing/Hands-On-Data-Science-for-Marketing/tree/master/Chapter01.

Trends in marketing

As the amount of data available and gathered increases exponentially every year and access to such valuable datasets becomes easier, data science and machine learning have become an integral part of marketing. The applications of data science in marketing range from building insightful reports and dashboards to utilizing complicated machine learning algorithms to predict customer behaviors or engage customers with the products and contents. The trends in marketing in recent years have been toward more data-driven target marketing. We will discuss some of the trends we see in the marketing industry:

• Rising importance of digital marketing: As people spend more time online than ever before, the importance and effectiveness of digital marketing have been rising with time. Lots of marketing activities are now happening on digital channels, such as search engines, social network, email, and websites. For example, Google Ads helps your brand to get more exposure to potential customers through its search engine, Gmail, or YouTube. You can easily customize your target audience, to whom you want your advertisements to be shown. Facebook and Instagram are two of the well-known social networks, where you can post your advertisements to reach your target customers. In the era of the internet, these marketing channels have become more cost-effective than traditional marketing channels, such as television advertising. The following is an example of different digital marketing channels that Google provides (https://ads.google.com/start/how-it-works/?subid=us-en-ha-g-aw-c-dr_df_1-b_ex_pl!o2~-1072012490-284305340539-kwd-94527731):

• Marketing analytics: Marketing analytics is a way of monitoring and analyzing the performances of marketing efforts. Not only does it help you to understand how much sales or exposure you gain from marketing, but it can also help you gain deeper insights into more individual level patterns and trends. In e-commerce businesses, you can analyze and visualize the different types and segments of customers and which type of customers drives the revenue for your business the most with marketing analytics. In media businesses, with marketing analytics, you can analyze which content attracts the users the most and what the trends in keyword searches are. Marketing analytics also helps you to understand the cost-effectiveness of your marketing campaigns. By looking into the return on investment (ROI), you can further optimize your future marketing campaigns. As the adoption and usage of marketing analytics rise, it is not difficult to find various software products for marketing analytics.
• Personalized and target marketing: With the rising applications of data science and machine learning in marketing, another trend in marketing is individual-level target marketing. Various organizations of different sizes utilize machine learning algorithms to learn from the user history data and apply different and specialized marketing strategies to smaller and more specific subgroups of their user base, which results in lower cost per acquisition and higher return on investment. In retail businesses, many companies implement artificial intelligence and machine learning to predict which customers are more likely to purchase and which items they are going to buy from their stores. Using these predictions, they customize the marketing messages to each of their customers. Many of media businesses also utilize artificial intelligence and machine learning to drive higher engagement from individual users to grow their user base. As these customized and target marketing result in higher ROI, there are many SaaS companies, such as Sailthru and Oracle, that provide platforms for personalized marketing. Sailthru recently published a Retail Personalization Index report, which analyzes how various retail companies use personalized marketing in different marketing channels. In this report, we can find that retail companies, such as Sephora, JustFab, and Walmart, use personalized marketing heavily in their websites, emails, and other marketing channels. This report can be found at this link: https://www.sailthru.com/personalization-index/sailthru100/.

The overall trends in marketing have been toward more data-driven and quantitative approaches. Companies of all sizes have been investing in marketing analytics and technologies more and more. According to the February 2018 CMO survey, the reliance on marketing analytics has gone up from 30% to 42% in the past 5 years. The reliance on marketing analytics is even higher for B2C companies with a 55% increase. Also, the number of firms using quantitative tools to demonstrate the impact of marketing has increased by 28% in the past 5 years. Lastly, the CMO survey suggests that the percentage of companies utilizing artificial intelligence an...