A Data Analytics Playbook for Retailers and Manufacturers Whose Answer To This Question is "Yes"

If you're an executive leading a big retail company, this book might also spark your interest. By reading it, you will learn what your employees can and should do with the ocean of available pricing data. You will quickly realize that there are so many questions you're asking yourself daily that have all the answers available — you just have to ask. You will learn how to task your data teams and choose better service vendors because you'd know where to look and what aspects of their work you should assess to get maximum performance.

This book will be of no relevance to you if you're a professional data scientist. However, I personally know a bunch of data analysts who have benefited a lot from playing the role of beta readers of this book.

What outcome should you expect after reading it? As a head of eCommerce, you will know how to tackle the uncertainty of demand and how to predict possible outcomes of your price fluctuations. You would be able to build your system for creating customized offers (hopefully, much more relevant and data-driven than your existing one). As a brand account manager, you will learn how to structure your relationships with the sellers in a way that would be the most beneficial for your company and manage to hit your annual profitability goals easily. As a procurement expert, you will learn how to create a mix of suppliers that would minimize your costs, increase your margin, and optimize your logistics (and last but not least, your paperwork associated with order placement).

This is not a book that explains how data is sexy. But this is precisely what you most probably would say after reading it.

So, let's dive in. 

you have been in the retail or manufacturing industry for the last 10 years, you have probably

heard the term "big data analytics" hundreds or even thousands of times. At every trade show, you may have slept through talks about big data or heard this expression again and again in every episode or even a commercial of your favorite eCommerce podcast. You might have even tried to get a taste of this dish, but if you don't have a degree in science or technology, it could have turned out to be not so easy.

But let me tell you that, as someone who has no relevant degrees, built three successful multi-million dollar businesses, and fathered two kids (a boy and a girl), compared to parenting, big data is indeed a piece of cake. I wrote this book to help you handle these modern data science mundane tasks wrapped in fancy buzzword wrapping with the firm hand of a seasoned category manager, brand account manager, or head of e-Commerce. To read this book and find value in it, you don't need a degree in data science. A regular calculus curriculum should cover it all (more or less). You don't need to be exceptionally tech-savvy and have a good grasp of Python or R. If you use old good Excel in your line of work, I believe we can get away with it.

How to calculate price elasticity for certain products? How to establish the break point of price elasticity? Building conditional predictions of price changes in retail and manufacturing. 

How to build profitability-first relationships with the sellers? How to use K-mrans and K-median clustering for offering custom discounts for distributors and customers? 

Check out if any of the use cases are relevant to you, and if you can put the gained knowledge into practice of fuelling the long-term growth of your business

How to plan demand and production using historical data, minimising the standard error and using simple exponential smoothing method. 

How to detect outliers and exclude them from your pricing strategies to make sure your pricing is nor corrupt by the wrong data. 

Become a beta reader and submit your feedback on the early draft.

Get a Honourable Mention. in the book's Credits section. 

Receive your FREE copy as soon as the book is published. 

I've just received the Chapter on Price optimisation and conditional predictions using linear regression. Never thought it could be done in Excel. And that I don't need any technical skills to handle this task. Fabulous!

I'm happy that Heinrich actually included my feedback on the Price Elasticity Chapter in the second version. Was excited to share my personal experience on how we built this framework in our company.

Anthony Castrio

New York, I Worldwide

Jane Drakes

London, We Sell PJs

Sign up now to get your free copy when the book is published. Become a beta reader, and get honourable mention in the Credits.