Data Analytics and Applications in the Fashion Industry

Build a Smart and Data-driven Fashion Company!

How to apply data analytics in real-life

How data can help a fashion business

Industry-specific application of data analytics principles

Fashion analytics

Product recommendations

Consumer-driven marketing

Digital & web analytics

Integrated demand forecasting

Supply chain analytics for fashion companies

Store localization, clustering, and in-store optimization

Pricing optimization

AI for uncovering fashion trends

How to build a dashboard for a fashion company in Tableau

No prior experience is required. We will start from the very basics

You'll need to install Tableau

Business analytics and AI are two of the hottest topics in the fashion industry.

Not only that: the global brands that rely on intelligent data collection and processing have a strong competitive advantage over the ones that are data blind.

And this shouldn't come as a surprise, right

We live in the 2020s.

Today, in almost all industries, the most successful businesses leverage user data to extract meaningful insights and tailor their products to satisfy user wants and needs.

Netflix recommends to us the movies and TV series we want to see next.

Instagram knows which photos we want in our feed.

So. naturally . fashion brands would want to know which clothes we want to wear, what impact the discounts have on us, and how likely it is that we'll return after our first purchase.

Top executives understand long-term gains in the fashion industry aren't about one-off transactions. Instead, successful brands want to win us over for the long run. The best way to do that is by employing a strategy centered around hyper-personalization. This means leveraging analytics, data science, and AI to deliver a first-rate experience that will make us a repeated customer.

Pretending that current fashion trends are the same as they were a decade ago is as detntal as operating without leveraging insights from data. The best brands will surpass you because they will:

Price items correctly

Know when to discount an item

Recommend the right items

Excel at engaging customers online

Stock the right styles

Be able to choose the right colors, fabrics, and sizes

Supply stores on and efficiently

The goal of this course is to help you learn about analytics in the fashion industry. We want to help you understand the ways in which different types of analysis can be applied in the fashion world and why that would be helpful in practice.

To provide invaluable insights that correspond to the best practices in the industry, we partnered with an experienced executive who's worked with some of the biggest brands in the industry. His current work contract doesn't allow us to share more info. However, his working title as Director of Data and Analytics for one of the biggest companies in the industry speaks volumes of his expertise of the topics we'll cover together.

This course is an invaluable opportunity for anyone who works in fashion or who wants to work in fashion and become a high-level executive. Moreover, the course can also be useful to data practitioners who would like to specialize/get hired in the fashion industry. Some of the interesting topics we will cover are:

Product recommendations

Consumer-driven marketing

Digital & web analytics

Integrated demand forecasting

Supply chain analytics for fashion companies

Store localization, clustering, and in-store optimization

Pricing optimization, and

AI for uncovering fashion trends

This course offers tremendous upside for the you will dedicate to it. Not only can it be career-chag if you work in fashion, but it can also inspire you to transform your business if you're a fashion entrepreneur who wants to succeed in the years to come.

People who want to work in the fashion industry

Students of data analytics

People who want to work in the field of data analytics and want to learn how to apply their knowledge in practice

Aspiring data analysts

People who want to build a modern fashion brand

Fashion executives eager to incorporate data-driven decision making in their business



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