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4 Ways Big Data Is Going To Revolutionize The Fashion Industry


Fashion is an industry that lives and breathes on its ability to discover and develop trends. The retail arm of the fashion industry has long relied on its ability to assess demand in order to drive revenue, while reducing the of discounts and other profit reducing sales tactics. Online shopping has completely changed the way fashion companies operate. For some it means they are able to ditch costly brick and mortar operations and operate in a much more lean and profitable fashion. Others leverage online sales to mitigate issues with in-stocks in their stores. Big data is bringing a new wave of change coming for the fashion industry however, one that will not only change how clothing is sold, but also how it is sourced, designed and merchandised.

1. Data will help discover trends

Up until now the fashion industry has been using Last Year (LY) sales data to determine how popular a particular style or trend was. While individual designers often consider their work to be art, they also understand that trends drive revenue, and as such they will focus on creating designs that are likely to sell based on LY performance. Similarly retailers will often base their purchasing based on similar numbers. With the advent of Big Data however, retailers and designers can make smarter evaluations based on new data sets. For example, by tracking Google searches companies can identify what potential customers are looking for and make purchasing decisions to more quickly meet that demand.

Companies like EDITD, and Worth Global Style network are changing the way fashion designers and retailers engage with big data. Instead of just looking at LY performance, these agencies use different data sets to evaluate the potential success of new merchandise. They also help leverage data to identify successful pricing models and timelines to help brands make decisions about when to adjust pricing to meet demand.

2. Data will help major retailers identify up and coming artists.

Retailers prefer to source style and products from designers with a proven track record of sales performance. These designers command higher prices because of their notoriety however, which can be an issue for mid-tier retailers with fashion savvy but price conscious customers. A Penn State Study demonstrated how data analytics could change the way the fashion industry identifies trend setters.

“By analyzing relevant words and phrases from fashion reviews, researchers were able to identify a network of influence among major designers and track how those style trends moved through the industry, said Heng Xu, associate professor ofinformation sciences and technology, Penn State.”

The same method can be applied to help find designers that are popular with consumers but not yet signed to major brands. This information could completely change the way retailers source their merchandise. By leveraging social media analytics to identify up-and-comers retailers can source lower cost design talent, and leverage their established manufacturing networks to help that designer scale their production to the retailer’s needs. In doing so they will create low-cost, offerings to consumers with high fashion value.

3. Data will change the way companies merchandise.

Big data represents a new opportunity for testing that is so far unrealised in markets. For example retailers and designers can conduct consumer experiments like never before. Factors like advertising, brick-and-mortar merchandising, and social media effectiveness can be evaluated, almost in real time, to help companies make quick pivots and invest their resources towards the most effective methods. For example a retailer could track the performance of an advertisement on their website, collect performance data, change the language of the advertisement and then repeat the same test. This can help retailers and designers make key brand decisions that will have a verifiable impact on revenue.

Brick and Mortar tactics will also be subject to constant evaluation. Companies like Burberry, Target, Birchbox, and the Gap have all leveraged the use of Concept or Pop-up stores to gather data about product performance and brick and mortar potential. Burberry uses it’s concept store to test digital merchandising tactics, while Target went with a product test approach, allowing consumers to try smart home products from up and coming startups with no risk. Tools like RFID are becoming more prevalent as well, helping retailers improve inventory tracking, and track sales performance all the way down to individual item and size.

4. Data will upgrade the performance of clothing.

Smart Fashion is the new wave of tech development in the fashion industry and it is completely driven by the consumer desire for data. CES 2016 was stacked with wearables that track health and fitness performance, and numerous other factors. Smart Fashion also blurs the lines between tech and fashion companies, with names like Ralph Lauren, Samsung, and Sensoria all helping innovate and create products that improve your fitness journey and performance. These products do everything from track body heat, record heart rates, adjustable bra support, and charging via solar power. While some of these products aren’t fully realized or fashionable yet, it’s only a matter of time before newer, more marketable iterations become available.

Another data-driven insight that can be used to improve clothing performance is return/refund data. As point of sale systems become more sophisticated retailers can track what items are most frequently returned due to defects or product quality. This can inform both purchasing and manufacturing decisions. Similarly data on exchanges can help retailers adjust sizing to more accurately reflect their consumer’s preference and body type.

Fortune favors the data savvy fashionista. Designers, retailers, digital agencies and consumers alike are recognizing the ways Big Data is changing the industry. It’s an open field of innovation, and brands that make an early mark with innovative solutions stand to make a significant share of the profits.

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Nick Hastreiter

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