Fashion analytics with Tableau is all about the exciting world of fashion, as well as the vastly expanding sector of online retail. Data-driven decision-making is crucial in this competitive world, so companies need to make smart decisions and integrate advanced statistical solutions to gain an edge in the market. This course explores a variety of topics in data analytics for fashion, and features a practical use case, where you will analyze fashion retail data and build your own story report in Tableau.

This chapter focuses on the importance of understanding your clients and their needs, as well as creating a seamless customer journey. Here we’ll discuss the main approaches to identifying consumer clusters. You will also gain a better understanding of the main predictors when it comes to consumer scoring, such as preferences, price point, or propensity to buy a product.

Product recommenders are one of the hottest topics in data science and a great way to deliver personalized content, with large-scale platforms such as Amazon and Netflix leading the way. In this section, we’ll discuss the main approaches to product recommendation and the challenges of integrating these solutions for fashion retailers.

This chapter is all about what consumers experience when they’re on an online platform. You will get familiar with the main problems that can be solved with the help of digital analytics with solutions such as Clickstream analytics, attribution models, A/B testing, and then explore the connection between these solutions and data science.

This is a major topic that is becoming even more relevant with the rise of online players in the fashion industry. In this chapter, we provide an overview of advanced supply chain analytics methods, which can be used to forecast replenishment times, stockout conditions, and returns, to name just a few. 

Integrated Demand Forecasting is crucial for optimizing the entire production and supply chain, which makes it one of the key topics in the course. In this chapter, we’ll introduce the complexity of solving problems in this area, as well as advanced statistical solutions that you can adopt when forecasting demand. 

In this section, we’ll cover the important aspect of finding the optimal price for products. We also provide an overview of metrics like price elasticity and discuss machine learning methods, such as logistic regression and neural networks that help us predict the right price for items.  

This chapter is all about finding the right spot to open a new store, implementing efficient space-planning techniques, and, ultimately, creating the best customer journey within a physical store. You will learn about the parameters that need to be optimized when opening a new store, illustrated by examples of how industry leaders are using advanced analytics for store localization and in-store optimization to improve margins. 

We’ll take a look at some of the advancements coming from the world of computer vision and see how they’re employed in the fashion world today. You’ll see what a virtual wardrobe is, as well as an example of a neural network predicting fashion trends.  

This is where you will have the chance to delve into a case study of a company online retailer and analyze data from its customer and a purchase database. You’ll build your own report in Tableau following the consumer journey, starting from Company KPIs, through Customer Journey, and ending with analyzing the Consumer Lifetime Cycle.