How Data Analytics is Changing the Fashion Industry
Do you know how Zara ended up being one of the key retailers over the past few years? Apart from having a team of great designers who design great clothes, they have a data processing center which is open every hour of the day. Most stores struggle with the problem of limited supply, which stems from the time elapsed between order and distribution. Zara solved this problem with an adaptive, data-driven supply chain management. Zara’s process starts in a similar way to the traditional retailers – with an initial order. The difference is that instead of ordering the bulk of the quantity for the season, Zara only orders a small amount of merchandise. Once the merchandise hits the stores, Zara collects sales data and analyzes each SKU’s sales against supply. Zara does even more, it analyzes performance of features of different SKUs. For example, they might identify that pants with patches sell better than pants without patches, or that certain colors or fits move faster than others. Zara then uses these insights to guide their following orders. They will design and manufacture models that have the most popular features to satisfy demand.
Of late, fashion retailers are increasingly turning to data analytics to keep up with the latest trends and client demands. Apart from having to meet the demands of “fast fashion” - turnaround time from the ramp to stores - retailers must also price items correctly, know when to reduce them, stock enough of the right styles, colors, fabrics and sizes, and ensure that stores are well supplied and operate efficiently.
Data analytics is not new to the industry, which has analyzed sales information in the past. What is new is the way data is now available, such as the information from social media sites or apps or other online websites; the biggest change is in the amount of data (unstructured/structured) available from non-sales sources. Retailers are even starting to use cognitive computing, or more specifically programs that simulate human thought process and mimic the functions of the brain, to discover more about what customers might want. If this can give a retailer a two-week jump on trend prediction, then those two weeks of selling time in stores is golden in this highly competitive industry. Apart from tracking customer behavior while shopping, and understanding the pattern there, data analytics can also help to improve the design and management of shops and department stores[2,3]. Despite the growth of online fashion outlets, many consumers still visit stores to touch and try clothing or shoes before buying. There are a lot of ways to track customer presence in a store, but what they decide to do with the data and how they use this data depends on different retailers/stores.
I have had my eye on fashion analytics for a while now, which led me to research a career in the same field. During my research, I came across a company named WGSN, which is headquartered in New York City with many offices globally. This company conducts catwalk analytics, where fashion experts tag garments based on type, style, color fabric and other details during the runway presentation. Analyzing this data reveals the current runway trends; for example, whether skirts or trousers are dominant in a particular season or the dominant style of trousers. However, they can run into certain issues here.
Applying analytics to fashion globally is slightly tricky since garments may have different names in different territories. For example, trousers in the UK are known as pants in the US. Furthermore, the lines between garment types are blurring with hybrids such as the “coatigan”, a softer or knitted version of a coat. The use of data is helping businesses adopt a more counterintuitive approach by designing algorithms that will choose people’s clothes for them. I came across another company, Stitch Fix, which is based out of San Francisco. They have developed algorithms that aim to ensure that as few items as possible sent to customers will be returned. No particular styles are shown on their website. Instead, users create a “style profile” by answering questions on everything from their favorite colors and fabrics to their size, budget and lifestyle. The algorithm then chooses five items to send the shopper; future sendings are adjusted based on user feedback on fit and look. This type of an algorithm, which deals with sizes, helps solve one of the biggest problems in the fashion industry – there are no unified sizes and designers often have different ways of cutting and sizing which leads to a lot of products being returned (as of 2013 around 10% of garments were returned mainly due to poor fit). There are so many factors to consider when it comes to clothing, especially for women. The fashion industry is just beginning to use data analytics to solve their problems, and it will be interesting to see how completely they can utilize its potential.
- Palmer, Maija (2016, October) : Fashion turns to data analytics to cut number of returned items https://www.ft.com/content/536a4870-33d7-11e6-bda0-04585c31b153
- Kumar, Rajnish (2013, September) : The fashion industry finds a crystal ball in data analytics - https://apparelmag.com/fashion-industry-finds-crystal-ball-data-analytics
- Nayar, Ajith ( 2016, December) : Top 5 Analytics Trends in Fashion Retail - http://customerthink.com/top-5-analytics-trends-in-fashion-retail/
- Murray, Sarah (2016, October) : Data analytics is on trend with fashion houses - https://www.ft.com/content/621d20c0-7033-11e6-a0c9-1365ce54b926
- Loeb, Walter (2013, October) : Zara’s secret to success- https://www.forbes.com/sites/walterloeb/2013/10/14/zaras-secret-to-success-the-new-science-of-retailing-a-must-read/#1e627465534f