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Hands-On Data Science for Marketing
Hands-On Data Science for Marketing

Hands-On Data Science for Marketing: Improve your marketing strategies with machine learning using Python and R

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Profile Icon Yoon Hyup Hwang
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NZ$71.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.1 (14 Ratings)
Paperback Mar 2019 464 pages 1st Edition
eBook
NZ$39.99 NZ$57.99
Paperback
NZ$71.99
Subscription
Free Trial
Arrow left icon
Profile Icon Yoon Hyup Hwang
Arrow right icon
NZ$71.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.1 (14 Ratings)
Paperback Mar 2019 464 pages 1st Edition
eBook
NZ$39.99 NZ$57.99
Paperback
NZ$71.99
Subscription
Free Trial
eBook
NZ$39.99 NZ$57.99
Paperback
NZ$71.99
Subscription
Free Trial

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Hands-On Data Science for Marketing

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:

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Key benefits

  • 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

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.

Who is this book 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.

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
Estimated delivery fee Deliver to New Zealand

Standard delivery 10 - 13 business days

NZ$20.95

Premium delivery 5 - 8 business days

NZ$74.95
(Includes tracking information)

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Mar 29, 2019
Length: 464 pages
Edition : 1st
Language : English
ISBN-13 : 9781789346343
Category :
Languages :
Concepts :
Tools :

What do you get with Print?

Product feature icon Instant access to your digital eBook copy whilst your Print order is Shipped
Product feature icon Paperback book shipped to your preferred address
Product feature icon Download this book in EPUB and PDF formats
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want
Product feature icon AI Assistant (beta) to help accelerate your learning
Estimated delivery fee Deliver to New Zealand

Standard delivery 10 - 13 business days

NZ$20.95

Premium delivery 5 - 8 business days

NZ$74.95
(Includes tracking information)

Product Details

Publication date : Mar 29, 2019
Length: 464 pages
Edition : 1st
Language : English
ISBN-13 : 9781789346343
Category :
Languages :
Concepts :
Tools :

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Frequently bought together


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Total NZ$ 193.97
Data Science for Marketing Analytics
NZ$56.99
Hands-On Data Science for Marketing
NZ$71.99
Data Science for Marketing Analytics
NZ$64.99
Total NZ$ 193.97 Stars icon

Table of Contents

19 Chapters
Section 1: Introduction and Environment Setup Chevron down icon Chevron up icon
Data Science and Marketing Chevron down icon Chevron up icon
Section 2: Descriptive Versus Explanatory Analysis Chevron down icon Chevron up icon
Key Performance Indicators and Visualizations Chevron down icon Chevron up icon
Drivers behind Marketing Engagement Chevron down icon Chevron up icon
From Engagement to Conversion Chevron down icon Chevron up icon
Section 3: Product Visibility and Marketing Chevron down icon Chevron up icon
Product Analytics Chevron down icon Chevron up icon
Recommending the Right Products Chevron down icon Chevron up icon
Section 4: Personalized Marketing Chevron down icon Chevron up icon
Exploratory Analysis for Customer Behavior Chevron down icon Chevron up icon
Predicting the Likelihood of Marketing Engagement Chevron down icon Chevron up icon
Customer Lifetime Value Chevron down icon Chevron up icon
Data-Driven Customer Segmentation Chevron down icon Chevron up icon
Retaining Customers Chevron down icon Chevron up icon
Section 5: Better Decision Making Chevron down icon Chevron up icon
A/B Testing for Better Marketing Strategy Chevron down icon Chevron up icon
What's Next? Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.1
(14 Ratings)
5 star 64.3%
4 star 7.1%
3 star 7.1%
2 star 14.3%
1 star 7.1%
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Ramakant Feb 06, 2023
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
The author does a comprehensive job of building knowledge frameworks to explain how analytics can be used for marketing datasets. Bonus stars on providing code for both R and Python. Covers a lot of ground that will be useful to brands operating in the retail, D2C space. Was personally looking to study the theory of attribution models and market mix models which was not sufficiently detailed in this edition. Hope the authors can address this in their subsequent revisions.
Amazon Verified review Amazon
Claudia Jan 29, 2023
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
This book comes paired with a GitHub repo with all the code. However, the code depends on the data which is nowhere to be found in the repo. I have no interest in going to 70 different links, downloading the data, and putting it in the right folder per chapter just to follow along. Huge waste of time, if I could give this a negative rating I would. Save yourself hours and go find another option. I genuinely cannot believe this was ever approved for publishing in this manner
Amazon Verified review Amazon
WU. Jun 23, 2022
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
Easily a book that could have been a five-star contender, unfortunately, there are some shortcomings.Overall, the text is a fine (but basic) introduction to data science through applications of common marketing analytics to problems such as: engagement, conversion, retention, churn, recommendations, forecasting, segmentation, experimentation, lifetime value, etc.What it does well is that it provides exposure to a wide range of applications of data science techniques to some pretty good datasets that somewhat replicate what you'd see out in the wild. The author also shows some data manipulation and aggregation techniques that I was not familiar with, like the use of "Groupers" combined with timestamp resampling inside of "group_by" operations. His notebooks are all neatly organized in the book's repo and the code is very readable.From here on out, the book is a bit of a letdown. The most salient of its shortcomings involves the depth (or lack thereof) of its exposition when it comes to the technique or algorithm being discussed. There are a couple of simple formulas here and there and very short explanations of what the algorithms are doing behind the scenes. I guess that's ok if it is meant to be an intro and not a full-fledged encyclopedia, however, I cannot overlook the sidestepping of common pitfalls and best-practices when it comes to leveraging these techniques in other places.For instance, in chapter 6 (collaborative filtering) the author walks the reader through the application of user-user and item-item collaborative filtering using unary matrices and cosine distances, without mention of assumptions made of the data (you need a lot), on the shortcoming of the technique (collaborative filtering doesn't work well for new users or new items), why you'd want one vs. the other (user-user CF is much more computationally expensive than item-item since one is more likely to have more users than items), or why you'd use cosine distance vs. Pearson (mean centering), etc. This is pretty much the case in each section.There are some editing quibbles as well. Chapters 5, 7, and 8 are pretty much rehashed from earlier chapters, so there's no reason why the material couldn't have been consolidated.Also, I wonder what the folks at Packt were thinking in packing essentially the same book twice into the same print - one for R and one for Python. If you are a Python user, then half of the book is dead-weight (the R chapters and vice-versa). Why not have "Hands-On Data Science for Marketing with Python" and "Hands-On Data Science for Marketing with R"?I have no issues recommending this book to students in an introductory class as long as there's some adult supervision. I'd be slightly concerned that the exposition nonchalance might lead some practitioners into thinking data science is just a couple of one-liners and boilerplate API calls...
Amazon Verified review Amazon
Byung O. Mar 29, 2022
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
Perhaps I am a bit bias because I've read other Data Science books but it lacks color, page design, and overall the descriptions are not robust. It feels like a compilation of low quality posts from Towards Data Science. It seems Python and R portions should two different books, it's way too redundant. Not sure whether if I should return this or just power through the book.
Amazon Verified review Amazon
Alex Rubio Oct 27, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Muy interesante todos los proyectos que se pueden hacer en el area de marketing, mos ejemlos muy claros para diferentes tipos de problemas reales.I refommend it.
Amazon Verified review Amazon
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