Dressing Up with Data Science (Part 1)
By: Winfrey Xian Ying Kong
Tags: Fashion, Data Science, AI, Trend Forecast, Big Data And Analytics

Picture from: Medium

Stage 1: Designing

“So, who is the new designer? AI or Human?”

To begin with, the designing stage is the root of all fashion trends. It is where designers scratch their heads just to produce the one masterpiece after hundreds, or even thousands of editings. Designing is more than just sketching garments. More accurately, it encapsulates many components of the design process like researching, drawing, coloring, conceptualizing, pattern making, etc.

In recent years, famous designers and top fashion companies have started utilizing big data in creating product designs that align with the consumers’ preferences in terms of styles, patterns, and colors. With that, fashion companies are able to strategize their design planning to take place either ahead of the fashion trend or during the season.

[Ahead of the trend]

In this first case scenario, where we are predicting future fashion trends (“trend forecasting”), we require mass data collection and analysis on the past sales, as well as comparison of trends up to one to two years in advance. While most fashion retail companies have the financial capability and capacity to develop models to extract insights from their customer base, some of them also leverage external platforms to gauge market movement to quickly respond to the fluctuating fashion trends.

There are online big data tools and trend forecasting platforms that provide data insights ranging from the fashion industry at-large (such as fashion trends), competitive analysis and price fluctuations, to the nitty-gritty details such as color codes of a particular trend with the extensive use of big data analytics and machine learning. For example, EDITED is a data analytics tool that uses visual pattern recognition, optical character recognition, and text crawling to collect fashion product data and provide insights such as the product activity of certain design style and distribution of patterns. These platforms help businesses identify and develop strategies in terms of product designs and sales based on the key success factors of other companies, instead of relying on guesswork.

Picture from: EDITED

Apart from analyzing from a business perspective, trend forecasters typically identify trends from Fashion Weeks and social media using image recognition and hashtag detection. Images of fashion wear from runways and social media platforms are categorized and analyzed to project the popularity, as well as the spread of trends given a particular region at a fixed period of time. However, one shortcoming of image recognition technology is that it does not provide information about customers acceptance and perception towards a certain trend. It turns out that hashtags on social media platforms are able to make up for this as it can provide information about consumers sentiment, as well as the engagement between a fashion brand and its customers. As the forecasters closely monitor such data online, they are able to predict trends in the near future be it a comeback or new fashion, allowing fashion retailers to plan their design process ahead of time.

Picture from: intelistyle

[During the season]

In the second case scenario, where garments are designed and produced during the season, fashion retailers usually conduct real-time analysis on product sales and consumer sentiment such that they are able to identify changes in consumer preference and purchasing behavior to refine product designs and alter production. Companies adopting such strategy usually produce relatively smaller batches of product for each given design with flexible and easily accessible distribution channels.

For example, Zara has created a central management system that constantly analyzes and evaluates their customers’ fashion sensibility by consolidating all types of engagement with their customers. At offline stores, their personnel actively collect feedback from customers, ranging from the overall shopping satisfaction to the tiniest detail like color, zips, pattern and sizes of apparel. The feedback would then be reported to regional managers through the system on a daily basis, thus allowing the managers to further analyze customer preferences across the region. Zara operates in a way that they only send a small number of new products to the local stores bi-weekly with more than 350 in-house designers authorized to make product decisions. This allows Zara to continuously improve their product design, quickly adapt to changes and even predict their customers’ preferences in the near future.