Predictive and prescriptive AI in Fashion Industry

For many years, the fashion and apparel industry relied heavily on intuition and limited available data to predict pre-season, in-season, and post-season customer demand. But as every industry turns to data analytics to gain a better understanding of omnichannel customers, the fashion industry has followed the trend and is now turning towards data analytics to predict demand, assortments, inventory turns and other related underlying omnichannel trends.

The major challenges that fashion retailers have tried to solve through data analytics are accurate pricing of latest as well as out-going items, adequate inventory management of the hot-selling brands/items filtered by style, size and colors, and design and management of stores. In one fix, fashion trend forecaster WSGN tracks the clicks of online shoppers and the items bought. They analyze the data and provide the retailers with a list of items/products that are starting to fall off the demand curve or trend. This sends a trigger for markdowns in the sales channels and ensures the retailer remains competitive as it relates to margin protection and inventory sell-through.

Fashion retailers are beginning to rely on predictive (data models built using statistical algorithms and machine learning) and prescriptive (user recommendations-based data insights) data analytics based on forward-looking analysis to help them understand what customers actually want, so that customer service, merchandising and other operations become more predictable and easier to execute. The ability to predict the latest fashion trends and manage seasonal fluctuations ensures that the retailers do not lag behind their competition.

In fact, some Fashion Brands have reported a net increase in sales after using such real-time data analytics. Fashion icons like Burberry and Ralph Lauren have resorted to Big Data analysis to pre-calculate their next fashion lines and assortments. Other retailers have built in the flexibility to combine ERP, PLM and BI data to predict customer preferences.

Overall, fashion retailers have begun to realize the importance of predictive and prescriptive analytics to not only manage day-to-day tasks, but also read the market and anticipate trends and implement market strategies to either launch new product lines or engage and keep existing customers interested in their present products.

Retailers that want to start on this journey must adopt a two-fold strategy:

The analytics or IT team must integrate structured, syndicated and unstructured customer, sales and product data.
Retailers should start with the processing of a few key predictive data models for assortments, pricing or promotions, using a handful of store or geographic clusters as a pilot.

After reviewing the success of such programs, retailers can scale these programs enterprise-wide. However, we must caution that while the use of analytics in fashion and apparel has practical applications for planned supply chain and inventory management improvements, using it to determine the whims and fancies of shoppers is a challenge and should be approached judiciously.