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

by Yoon Hyup Hwang

Optimize your marketing strategies through analytics and machine learning 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. 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.

Genres: Science

464 pages, Paperback

Published March 29, 2019

Reviews

[Walter Ullon · Rating 3 out of 5]

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...

[Enrique · Rating 5 out of 5]

Easy book. Python made simple for marketing.

He use some outdated code, but only requiere to do update with numpy.

Nice selection of datasets and methods, Machine Learning methods good applied.

The only problem is the lack of in depth explanation, you need to look other resources to understand most difficult concepts like random forest.

Besides that it’s an excellent primer.

[Anu · Rating 5 out of 5]

5 star

[Relax, you're doing fine · Rating 4 out of 5]

I recommend you to learn basic R or Python before reading this book. It's better if you have to learn some basic data-science models.

This book is suitable for the intermediate level.

The author will guide you to apply data science in various situations from analyzing a product portfolio to A/B testing verification. Quite many topics but not is very deep :)

[Firdaws · Rating 5 out of 5]

Got moved to the marketing analytics section where I am interning at. This book was recommended to me . It helped ease me into the department.My fellow intern and I highly rate it