AI and Fashion Forecasting

Among all the industries destined to be disrupted by AI, fashion forecasting is one of the less obvious ones people would think of. The main reason is that the current way forecasting firms conduct these analysis of future fashion trends is a mix of data-driven methodologies and also subjective interpretations by their staff frequently visiting bars, shops, and catwalks around the world. A specialized forecaster would spend time walking around cities like Milan, visiting local shops and bars that would otherwise have no information on the internet, gather intel on the latest trends during these site visits, and summarize their findings. On the other hand, a lot of data is used, including macroeconomic trends such as political sentiment and economic indices.

 

An early use of AI in fashion industry is sales demand forecasting. Since retailers’ sales data is now mostly digitized, past demand and sales history could be leveraged to forecast short-term demand, which is used by EDITED, a retail analytics firm (1). The accuracy of such forecast is yet to be determined, but the additional insight provided to retails in inventory planning could be significant from a supply chain management perspective. The benefits of more and more accurate forecasting in sales demand would definitely help with improving inventory turnover, better managing ordering cycles by taking into consideration product ordering lead time and popularity. The side effect of an over-reliance on AI in this type of trend/demand forecasting is discrimination against more up-and-coming designers yet to prove themselves in the market. Historical data would favor incumbents in forecasting future demand, while relatively young, small players would miss out on potential sales due to lack of market proven demand. Since the level of unpredictability is very high in fashion, and new brands usually become popular in unexpected ways (social media, online influencers, advertisements, trunk shows, etc.), the heavy use of AI in demand forecasting and therefore inventory management for retailers could be a boon to large incumbents but a curse for young players hoping to gain a solid foothold in the industry.

 

Large tech firms have now entered the fashion trend analytics arena. Google has set up a “Trendspotting” division specialized in summarizing and publishing annual fashion trends using its large database of search data. So far, the findings through this department haven’t been “groundbreaking”. Basic, sometimes obvious trends are being uncovered through their analytics division. Maybe this is a sign that there is still a place for more traditional fashion forecasting firms that send human agents to brick-and-mortar stores trying to scope out the latest trends and tell stories of future fashion trends.

 

Anyone working in the fashion industry would probably agree that the rules of the trade are less data-centric than they are human-centric. Past data can serve as a basic guide to what the industry would favor tomorrow, but the products & trends making waves now and tomorrow are sometimes dictated by carefully orchestrated, human-driven decisions made by a select few of people at the top of the fashion food chain. It would be very exciting to incorporate the use of AI in the industry, but there would still be a need for human “expert voices” that could influence the latest trends by some simple Tweets, Instagram posts, or comments on fashion magazines.

 

References:

1. https://www.economist.com/news/business/21725599-technology-may-be-disrupting-peculiar-business-can-data-predict-fashion-trends

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