Fashion Trends: Analytics vs. Forecasting

Is it over for trend forecasting? The fashion industry has been using trend forecasting techniques as a means to predict up-and-coming trends for years, but how accurate are forecasts in the dynamic market of today? This report takes a hard look at the differences between trend forecasting and trend analysis.

Written by Amelia TehJuly 1, 2019

A Time of Reckoning for Southeast Asian Fashion Brands

Southeast Asia is poised to reach US$53 billion in online retail by the year 2023, with a compound annual growth rate (CAGR) of 23%[1] fuelled by a growing middle-class population as well as high internet and smartphone penetration.

 

Fashion Leads Category Growth

While consumer electronics remains the largest category in the region, fashion (including apparel, footwear, and accessories) and cosmetics are forecasted to be the key drivers of future growth, mostly due to the lack of availability of brands in offline retail channels.

Zalora, the fashion e-tailer with a strong foothold in Southeast Asia, reported that sales doubled every year since taking part in Singles’ Day in 2014. Site traffic and items ordered tripled in 2018 compared to the prior year, as shared by Giulio Xiloyannis, Zalora’s Chief Commercial Officer, in an interview with South China Morning Post.

 

Trend Analytics Drive Growth Opportunities

With a positive industry outlook and growing online channels, fashion brands are now powered with retail and consumer data that is tracked, collected and managed through own websites, external sites and social media. The data, when layered with structured analytics and examined in detail, offers excellent insights to inform decision-making and devise strategies.

Trend analytics enables one such process that significantly influences fashion design and buying, with insights on a specific style, silhouette and colour direction up to 95% accuracy[2].

The birth of Mr P. by Mr Porter and 8 By Yoox are testaments to this. Both retailers utilise transactional data to spot bestsellers and product opportunities and combine it with an eye for design to execute buys for their respective in-house labels[3].

 

[1] “Online Retail In Southeast Asia Is Expected To Reach $53 Billion By 2023”, Forrester, November 2018
[2] 95% is the level of data accuracy of Omnilytics dashboard
[3] “The rise of the in-house brand”, Financial Times, January 2019

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Trend Analysis vs. Forecasting

While more retailers in the US and UK are optimising trend analytics to chase “bestseller” re-ordering and to leverage on current demand momentum, Southeast Asia region is falling behind on digitisation and deploying analytics. Retailers who do not adopt the required changes to meet consumer demand will rapidly lose out in market share. Here’s why:

 

A trend forecast provides broad trend concepts which typically only commercialise in the next two years or more. This framework brings about high risk with low accuracy (less than 30%)[1] due to the long timeframe to mass adoption and the high dependency on intuitive interpretation of concepts (Chart 1). On the other spectrum, trend analytics incorporates data to provide insights on current product performance, supporting informed decision-making. This includes the decisions to chase an opportunity, to repeat a bestseller or to design a new collection. The influence of intuition is minimal, hence lowering the risk of overstocking while maximising profit.

 

Consumers today have greater access to the fashion industry than ever before. Hence, they are able to communicate and drive tastes through social media and across geographies, in real-time. This calls for an approach that analyses demand from a consumer behaviour perspective, than top-level macro predictions. As the industry gets more complex and competitive, these trend forecasts have not changed even though we now have so much data about consumers. Referring to these one-size-fits-the-industry reports on what to make next is no longer relevant.

 

[1] Commented by a senior executive of a trend forecasting agency.

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Case Study #1
Animal Prints – The Importance of Right Timing

Animal prints had a strong presence in Fall/Winter 2018 fashion weeks, which took place in the spring of Feb 2018. Spring/Summer 2019 runways observed recurring animal prints, but by then, the trend had already reached the masses, as seen on the high street and social media.

 

Data Signals on Trend Movement

Omnilytics software analysed more than 400,000 data points from five of the top fast fashion retailers – ASOS, Zara, Boohoo, Mango and Urban Outfitters, to find 22,179 animal printed new-in products in the US and UK on a growth trajectory over the past 14 months (Chart 2)[1].

The animal print saw new-in lifted steadily in spring 2018 and peaked by summer, before growing aggressively to its culmination by the holiday season. In seeing the rise in April, and with the trend supported by fashion weeks, brands with efficient supply chain management could have developed animal printed styles and launched by July. In another scenario, retailers could have optimised the animal prints it had in stock to execute visual merchandising and marketing to show that they were tapped into the latest fashion trend.

For brands who had missed the data signals, they not only failed to achieve early commercial success but also ran the risk of over-stocking to markdown if they had launched the trend in the later seasons.

 

[1] “The Evolution of Animal Prints”, Omnilytics, Mar 2019.

 

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Case Study #2
Ultra Violet – An Underwhelming Offtake

Announced by Pantone® as 2018’s colour of the year, Ultra Violet gathered great hype as it arrived at an opportune time with the colour purple historically associated with efforts to achieve gender equality. However, despite publicity including colour applications across industries as published by WGSN, Ultra Violet did not take flight as predicted.

 

Data Doesn’t Lie

Omnilytics software ran an in-depth analysis on the vivid colour spectrum at the top 10 fast fashion retailers[1] and revealed that the highest newness came in vivid red, termed by Pantone® as Valiant Poppy[2]. While both colours achieved decent sell-out rates over 12 months in 2018, there were 3,558 new-in products launched in Valiant Poppy, compared to just 127 new products in Ultra Violet (Chart 3).

With trend analytics, a buyer can tactically approach stocking Ultra Violet by only selecting the bestselling styles in small quantities, while confidently order Valiant Red and Russet Orange as these came through for the dominant colour story. Buyers/merchandisers can also look at historic data to see how Ultra Violet has performed and how it has been discounted over time.

 

[1] ASOS, Zara, Boohoo, Missguided, H&M, Fashion Nova, Forever 21, Topshop, Nasty Gal and Urban Outfitters in the US and UK.
[2] “FW18 Key Colour Trends from Runway to Retail”, Omnilytics, Mar 2019.
[3] Omnilytics technical colour name that correspond to a specific Pantone colour.

 

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The State of Mass Market Adoption: Southeast Asia & 6-Month Opportunity

With internet penetration growing at a rapid pace, Southeast Asians can now access fashion events and pop culture happenings at the same time as the rest of the world. However, there is still an adoption lag.

Consumer Readiness

Global trends, driven by progressive design, are not adopted as quickly by Southeast Asian consumers, compared to their Western counterparts, for several reasons. These include the high price tags on designer pieces, the fact that some trends are not tropical weather friendly, or deemed unbefitting to the majority of modest consumers.

As a result, brands in the region only adopt trends about six months after a wider global adoption and reaching massive commercialisation (Chart 6).

 

The Silver Lining

The 6-month gap presents a window of opportunity for the local retailers to research and identify a particular trend’s much talked about products, and then produce in time for adoption by the “early majority” in this part of the region.

With trend analytics, fashion buyers can speed up this process by analysing the relevant market leaders, such as ready-to-wear designers or brands from the UK, US, Korea or Japan, to identify bestsellers in terms of style, silhouette and colour.

 

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Meet the Author

Amelia Teh

Amelia Teh has over 18 years of experience working in retail with leading brands like Levi Strauss and Topshop. She now harnesses that expertise as Director of Business Intelligence at Omnilytics to provide insights on commercial planning and decision-making with data.

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