I have been subscribed to 365 Data Science for just over a year now, and what I enjoy about their online training program is not only does it have courses for the basics and fundamentals in math, programming and data visualization, but it offers advanced specialization courses as well. These courses demonstrate the application of data science and analytics in different fields and allows me to learn about industries that I have no experience in.

365 Data Science recently published (and I recently took) a course on Fashion Analytics in Tableau which explores the use of data analytics in fashion and retail in order to gain an edge in competitive markets.

The course introduces the need for more data-driven decision-making given the increase in online retail in 2020 due to the COVID-19 pandemic. Not to mention, emerging companies benefit from their integral data applications, while existing companies are being left behind because they are failing to adapt.

The use of data analytics in the fashion industry is essential for building customer relationships, targeted advertising, user experience and conversion on digital channels. Beyond customer relationship management and marketing, data analytics also optimizes product allocation, demand, pricing, supply chains and more.

The biggest takeaway from the introduction to this course is to always align one’s analytical technologies with their business strategies.

Since data can have an impact on different parts of a fashion retail company, the course then dives into the following lessons:

• Consumer-Driven Marketing
• Consumer Analytics – Product Recommendation
• Digital and Web Analytics
• Supply Chain Analytics
• Integrated Demand Forecasting
• Pricing Optimization
• Store Localization, Clustering, and In-Store Optimization
• AI For Predicting Fashion Trends

A case study, where you build your own fashion analytics dashboard in Tableau, wraps up the course. This project emphasizes the importance of building dashboards that have a clear idea, tell a story, and highlight the main points. Most importantly – do not have random charts that include all the data.

Click below and head on over to my fashion analytics dashboard story in Tableau:

Dashboards like these can tell you a lot of information. On the company KPIs slide, if you were to compare the gross profit percentage year-over-year, you could say that you made more sales in 2020 than 2019. You could and even look at this metric by specific countries to see where your company had any losses in sales.

If you move to the consumer engagement slide, you will have a better understanding of your customers by what tier they fall into and compare these tiers to decide where to invest more in marketing efforts.

Or look at the customer lifecycle slide to understand the frequency of purchases, to know the right time to retarget customers and increase sales.

These are just a couple of ways to use this dashboard story to improve sales, but it is clear from this that data-driven decision-making in fashion and retail brings with it a lot of opportunities.