Predictive Analytics x Fashion
Being in Milan during the crazy Fashion Week season has me thinking about upcoming trends. Along with many others, I'm already trying to figure out what is going to be trendy next season after watching all the runway shows. However, one day, with the use of predictive analytics, we may not even need to play this guessing game because predictive analysts will be able to forecast trends.
The one thing that all fashion brands have in common is their desire to create products that are on trend. Traditionally, trends have originated from runway styles at the semiannual fashion weeks. Designers predict what trends are going to come next and display them in their runway shows. Eventually fast fashion companies pick up on these trends and mimic them to create fashionable products at an affordable price. This whole process can be time consuming and sometimes inaccurate. Predictive Analysis is a way for brands to forecast these trends ahead of time. The introduction of Predictive Analysis into fashion will speed up the process for designing and merchandising. In addition, brands will be able to calculate future sales to ensure that they are producing the perfect amount of a product. Overall, trends will be maximized and markdowns will be minimized.
One of the leaders in the fashion predictive analysis industry is EDITED. Some of EDITED’s many notable customers are Farfetch, Net-a-Porter and Topshop. EDITED helps brands with pricing, assortment and trend forecasting using big data. Their trend monitors track how trends and styles are performing worldwide in real-time. The use of this real-time data helps their clients decide whether they should back a particular trend or give up on a declining trend. EDITED provides its clients with the data necessary to make their most important decisions.
Another industry leader is Makersights. Makersights is a predictive analytics platform that works with big brands, such as Ralph Lauren, Sperry, MM LaFleur and more to improve their product design and development. In the past, companies would use time consuming focus groups to learn more about what consumer thought about their products. However, that is no longer necessary. Makersights pulls information on consumer insights and applies sales data and machine learning to that information. This process is called “actionable product intelligence”. The goal is for these brands to create more accurate sales plans and to gain consumer insights on certain patterns, colors, prices and trends.
Overall, more and more brands are turning to Predictive Analysis to help them make decisions about design, merchandising, pricing and production. Brands no longer have to catch up on trends. Instead of seeing trends at competitors and recreating them, companies now have the ability to forecast these trends ahead of time to make sure that they are always ahead of the curve.