Designing with Data: How Fashion Analytics Supports Creativity

Designing with Data: How Fashion Analytics Supports Creativity

By Sufiana

Designing with Data: How Fashion Analytics Supports Creativity

Data analytics is re-shaping fashion’s old school methods. More and more companies are looking towards data to refine customer preferences and pinpoint products that better fulfil customers’ needs. 

Traditionally, fashion brands use information such as sales history, assortment details and inventory records to guide the development of the following season’s collection.

Details from past collections are scrutinised at the end of every season and the successful aspects are reconfigured for the new season. Although this model may work for some, it certainly does not match the speed of the current trend cycle. 

Industry professionals know how challenging it is to keep up with these consumer demands. In an increasingly competitive market, getting your product right is crucial – one mistake can make or break a business.

Fashion analytics provides designers with accurate insights into the market as it unfolds. These insights give designers a better understanding of consumer demand and minimise costly risk. In fact, fashion analytics is able to impact various segments within the fashion industry, from design and marketing to even the resale market.

Fashion analytics: 8 by Yoox Fall 2018  ad campaign
8 by Yoox Fall 2018 ad campaign. Image credit: YNAP

Recently, luxury multi-retailer Yoox put analytics at the core of its first in-house collection, 8 by Yoox. The range of stylish essentials for both men and women debuted in late 2018, developed using algorithms and analytic tools. It identifies patterns of shared information across social media and magazines.

The information is paired with extensive internal sales data and customer feedback to tailor a collection that is not only trend-led but also specifically targeted to their consumers.

Fashion Analytics: Actionable Insights 

With accurate information on product design, trend analysis, sales tracking and pricing, the best fashion analytics takes raw data and presents it in a cohesive and visual manner that is insightful for designers.

At Omnilytics, we connect thousands of data points to create insights into key product details, informing the creative process.

Here are 3 ways data can enhance the designing process: 

#1. Trend Validation

Historically, fashion brands would rely on trend forecast reports or fashion magazines to identify up-and-coming trends which are puzzled together by trend agencies or fashion writers. While this information is great for research material, it lacks the accuracy and data to support any substantial business decision.

Trend analysis, on the other hand, is grounded in fact and looks at current market demands to chase future opportunities. 

fashion analytics: The animal print looks from Spring/Summer ‘19 runways.
The animal print looks from Spring/Summer ‘19 runways. Image Source: Glowsly

For instance, animal prints were popular on the F/W ‘18 runways, which took place during February 2018. The trend was spotted again on the runways of S/S ‘19. However, the trend was already in mass adoption across the high street by April of 2018. 

To put animal prints to the test, Omnilytics ran an analysis of the assortments at the top fast fashion e-tailers – ASOS, Zara, Boohoo, Mango and Urban Outfitters. Based on this, we found over 22,000 new animal printed items in the US and UK in the span of 14 months. 

fashion analytics: A chart of new arrivals of animal print across seasonality.
A chart of new arrivals of animal print across seasonality

Omnilytics Trend Performance showed animal print first started lifting in early 2018 and began to rise steadily from April 2018, before reaching its peak in Q4. 

Based on this information, merchandisers and buyers would have been able to identify the trend in April in preparation for the summer collection launch in July.

Brands that were not able to leverage the trend on time missed out on the sales opportunity and risked over-stocking if the trend was introduced any later.

#2. Colour Selection

Colours are an integral part of introducing newness and telling seasonal narratives. Although staple colours such as black, white and grey would work for any time of the year, seasonal colours will be influenced by market trends and external factors such as Instagram trends.

A major influencer for colour trends within fashion is Pantone’s Colour Of The Year forecast. The colour of 2019 was declared as Living Coral late last year. Soon after the announcement, fashion retailers rushed to stock the shade for the coming Summer collection.

Omnilytics data showed Coral was being stocked aggressively up to April of 2019 with its sell-out fluctuating throughout Spring/Summer 2019. Overall, the performance of coral was lacklustre – it was outshone by punchier shades of orange and yellow.

fashion analytics: living coral looks from the runway
From left: Acne Studios, Prada, Marc Jacobs. Image source: Vogue.com

Retailers that had data on colour would have been able to study colour trends across the fashion industry before deciding to integrate coral into any future collections. These insights are especially helpful for building seasonal colour schemes.

Fashion analytics: Chart of Net-a-Porter’s colour palettes from Spring/Summer’ 18 and Spring/Summer ‘19
Charting Net-a-Porter’s colour palettes from Spring/Summer’ 18 and Spring/Summer ‘19

To get a better understanding of seasonal colour changes, we compared the top six contributing colours of S/S ’18 and S/S ’19 by Net-a-Porter. As seen in the chart, Black and White were the top contributing colours in both seasons, representing Net-a-Porter’s core colours. 

The following four colours represent seasonal changes with Pink, Grey and Brown shifting in dominance between the seasons. Interestingly, Blue, which contributed 4.9% to Net-a-Porter’s S/S ‘18 colour scheme, was completely overtaken by Yellow in the following season.

#3. Product-Market Fit

As retailers expand globally, seasonality becomes increasingly challenging across different markets. Even within a specific segment, local trends will differ according to the season, weather and festivities. 

Globally distributed brands are constantly having difficulty catering to audiences in different countries due to these localised trends, especially when it comes to assortment planning for countries in the southern hemisphere. Markets such as Australia and South Africa have often posed a challenge due to their opposing seasons. 

fashion analytics:Assortment mix at Cotton On Australia and Cotton On Singapore.
Assortment mix at Cotton On Australia and Cotton On Singapore.

Above, we analysed the assortment mix of Cotton On in two different countries for the month of July. The weather conditions in Australia and Singapore differ greatly during this month. While July is one of the coldest months in Australia, it is one of the hottest months in Singapore. 

As seen in the above chart, Outerwear makes up 8% of Cotton On Australia’s assortment, whereas the same category barely makes up 1% at Cotton On Singapore. This shows the importance of factoring in local market conditions and customer needs when assortment planning. 

Having historical data will not only help retailers understand how to build a competitive assortment in foreign territories, but it can also help tap into local trends within specific categories to understand what kinds of products to create. 

fashion analytics: A chart of trending outerwear sub-categories during winter in Australia
A chart of trending outerwear sub-categories during winter in Australia

Key Takeaways

Ultimately, fashion analytics exists to support a fashion designer’s creative process.

We do not look to the past to simply repeat design that has worked before. Instead, we find areas of opportunity from performance data and focus on what the consumer really wants. 

Processes that typically use old-school methods of intuition and gut-feel can be guided by data to reduce risk.

By having validation on major aspects of the product development process such as trends, colour selection and market demands, the industry is able to avoid making costly mistakes.

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