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TRADITIONAL VS. BIG-DATA FASHION TREND FORECASTING:

AN EXAMINATION USING WGSN AND EDITED

by

Mikayla DuBreuil

A thesis submitted to the Faculty of the University of Delaware in partial fulfillment of the requirements for the degree of Master of Science in Fashion and Apparel Studies

Spring 2020

© 2020 Mikayla DuBreuil All Rights Reserved

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27832243

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TRADITIONAL VS. BIG-DATA FASHION TREND FORECASTING:

AN EXAMINATION USING WGSN AND EDITED

by

Mikayla DuBreuil

Approved: __________________________________________________________ Sheng Lu, Ph.D. Professor in charge of thesis on behalf of the Advisory Committee

Approved: ___________________________________________________________ Hye-Shin Kim, Ph.D. Chair of the Department of Fashion and Apparel Studies

Approved: __________________________________________________________ John Pelesko, Ph.D. Dean of the College of Arts and Sciences

Approved: __________________________________________________________ Douglas J. Doren, Ph.D. Interim Vice Provost for Graduate and Professional Education and Dean of the Graduate College

iii

ACKNOWLEDGEMENTS

First, I want to thank Dr. Sheng Lu, my advisor, for providing me the

opportunity to research a topic that is changing the fashion industry and has helped me

grow as a student. I am so grateful for your unparalleled mentorship, constant support,

and critical insight that made this such an exciting, thought-provoking journey. Any

student that has the opportunity to work with Dr. Lu is lucky—he is simply one of the

best! I cannot exaggerate how happy I feel to have been your advisee for the past two

years.

To my committee, Professors Brenda Shaffer and Katya Roelse—thank you for

sharing your insight and helping to make my research a success. Your expertise has

broadened the scope of my study and made it more industry-relevant from both

business and design perspectives.

Additionally, I want to thank Dr. Cao, Director of Graduate Studies, for

encouraging me to pursue the graduate program and making it a reality. I am grateful

for the time he has allowed me to grow these past two years in the University of

Delaware Department of Fashion and Apparel Studies.

Finally, to my friends and family, thank you for your support, smiles, and

laughs along the way. Kendall and Cheyenne—I couldn’t have done it without you

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and I’m so glad that graduate school brought us closer together. Mom and Dad, thank

you for your encouragement and for advocating for my success!

v

TABLE OF CONTENTS

LIST OF TABLES…………………………………………………………………....….vii LIST OF FIGURES.…………………………………………………………………….viii ABSTRACT……………………………………………………………………………...ix

Chapter

1 INTRODUCTION ........................................................................................................1

1.1 Introduction ................................................................................................................1 1.2 Research Question .....................................................................................................3 1.3 Key Definitions ..........................................................................................................4

2 LITERATURE REVIEW .............................................................................................5

2.1 Fashion Trend Forecasting and Related Theories. .....................................................5 2.2 Using Big Data in the Fashion Industry .....................................................................7

2.3 Debate on the Application of Big Data for Fashion Trend Forecasting ....................8 2.4 Summary ..................................................................................................................10

3 METHODS AND DATA .............................................................................................12

3.1 Data collection .........................................................................................................12 3.2 Data analysis ............................................................................................................15

4 RESULTS AND DISCUSSION……………………………………………………..23

4.1 Descriptive Analysis ................................................................................................23 4.2 Statistical Analysis ...................................................................................................31

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5 IMPLICATIONS AND FUTURE RESEARCH AGENDAS ......................................35

5.1 Findings....................................................................................................................35 5.2 Implications..............................................................................................................35 5.3 Future Research Agendas ........................................................................................37

REFERENCES.........................................................................................................................40

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LIST OF TABLES

Table 1.1 Fashion Trend Forecasting Definitions................................................................4

Table 3.1 Fashion Trend Forecasts Generated by WGSN .................................................13

Table 3.2 Fashion Trend Forecast by EDITED: Key Factors Considered ........................15

Table 3.3 Fashion Trend Forecast Coding Scheme ...........................................................16

Table 3.4 Pattern definitions ..............................................................................................17

Table 3.5 Design details .....................................................................................................19

Table 4.1 WGSN Fashion Trend Forecasts for Womenswear in the U.S. Market in the Spring/Summer 2018 Season ............................................................................23

Table 4.2 EDITED Fashion Trend Forecasts for Womenswear in the U.S. Market in the Spring/Summer 2018 Season ............................................................................28

Table 4.3 WGSN and EDITED Trend Forecasts: Results Comparison ............................31

Table 4.4 Results of Independent Sample T-test ...............................................................32

viii

LIST OF FIGURES

Figure 4.1 Example of WGSN Forecast Coding Illustration (for case #3) ........................28

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ABSTRACT

Traditionally, fashion trend forecasting is conducted through a human-based process

that relies heavily on designers’ artistic viewpoints. However, with the emergence of data

science and the increasing availability of data inputs from consumers, the possibility of

using big data tools to forecast fashion trends is attracting growing interest among the

academia and practitioners in the fashion industry.

This study empirically evaluated the similarities and differences of the results of

traditional human-based fashion trend forecasts with the ones generated by a big data

tool. Based on the comparison of 20 paired fashion trend forecasts for womenswear in the

U.S. retail market during the 2018 Spring/Summer Season (S/S 2018) generated by

WGSN (i.e., tradtional human-based approach for trend forecasting) and EDITED (i.e., a

fashion big data tool) and by using the independent sample t-test, the study finds that:

First, WGSN and EDITED were able to generate very similar trend forecasts for the

pattern. Second, WGSN and EDITED were able to generate overall similar trend

forecasts for the color. Third, the forecast results by WGSN and EDITED for the design

details were the least similar statistically.

The findings of this study fulfill a critical research gap regarding the feasibility of

using big data for fashion companies’ creative activity. Particularly, the findings suggest

the great potential of using big data tools to aid fashion companies’ forecasts and the

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creation of new products. Additionally, the results of the study significantly increase our

knowledge of the benefits and limitations of using big data in fashion forecasting.

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Chapter 1

INTRODUCTION 1.1 Introduction

Fashion trend forecasting, which refers to the profession of envisioning future trends

in style and foreseeing consumers’ desires, is crucial to fashion companies’ business

success (Rousso, 2012; Furukawa, Miura, Mori, Uchida, & Hasegawa, 2019). Fashion

companies use inputs from the trend forecasts to create products that are appealing to

consumers, drive retail sales, and reduce excess inventory (Jackson, 2007).

Traditionally, fashion trend forecasting is done through a human-based process of

examining artistic viewpoints, culture, societal attitudes, and current events to predict

future trends (Grammenos, 2015). In this process, fashion forecasters collect and analyze

inspiration to translate it into themes, design details, colors, and patterns that will be

popular in the next season (Rousso, 2012). Creativity is considered central to this process

as consumers desire originality and uniqueness (Ming Law, Zhang, & Leung, 2004).

However, traditional fashion trend forecasts created by designers also face

limitations and critics. For example, some criticize traditional fashion trend forecasting as

‘opinionated guesswork’ due to designers’ tendencies to rely on their ‘gut feel’ to predict

trends (Tehrani & Ahrens, 2016; Fox, Graul, & Peng, 2018). Particularly, it is of concern

that the traditional designer-based fashion trend forecast involves high business risk

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because of designers’ over-reliance on their artistic vision and too little inputs from the

commercial point-of-view (Visual Next, 2018; Israeli & Avery, 2018).

The shortcomings of the traditional approach of forecasting fashion trends along

with the emergence of data science have inspired fashion companies to explore new ways

of trend forecasting (Israeli & Avery, 2018; Chaudhuri, 2018). Big data typically refers to

large data sets that require advanced computational tools to analyze (Merriam-Webster,

2019). Fashion retailers have been actively using big data to support their business

operations (Fox et al., 2018). Big data tools, in particular, have demonstrated

effectiveness in helping fashion companies improve product assortment for targeted

markets, more accurately predict future sales, and optimize inventories across all selling

channels (Fox et al., 2018). In comparison, the use of big data to support fashion

companies’ creative activities, such as trend forecasting, remains nascent, yet promising

(Choi & Hui, 2011; Ren, Chan, & Ram, 2017). Potentially, big data tools could create

fashion forecasts and reveal patterns, trends, and predictions in consumer preferences by

leveraging the breadth and large quantities of data and advanced analysis (Joshi, 2018,

para. 2; Ren et al., 2017). These insights may allow fashion companies to more accurately

forecast what particular apparel patterns or colors will be market-popular, as well as the

duration of these trends (Joshi, 2018). Nevertheless, the extent to which big data tools can

be used to forecast fashion trends effectively remains largely unknown (Gaimster, 2012;

Sun & Zhao, 2018).

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1.2 Research Question

Given the growing interest in exploring the use of big data for creative activities

in the fashion industry and the lack of existing research on the topic, this empirical

study intends to compare the results of traditional designer-based fashion trend

forecasts with the trend forecasts generated by big-data, focusing on evaluating

their similarities and differences. The study is important because:

First, current literature on the use of big data tools in fashion companies focuses

on merchandising strategies such as improving product assortments, markdown

optimization, and producing sales forecasts (Joshi, 2018; Visual Next, 2018). Whereas,

the results of this study will significantly increase our knowledge of the benefits and

limitations of big data as a creative tool to fashion forecast. Second, both fashion

companies and educational institutions can benefit from this study by gaining a greater

understanding of how technology and data are changing the fashion industry, as well as

the role of creativity and traditional methods in fashion forecasting. Additionally, the

findings of the study will produce valuable empirical results that can inform how

business-minded individuals, creatives, and educators alike can maximize the usage of

big-data based fashion trend forecasting as well as traditional methods.

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1.3 Key Definitions

For the purpose of this research, we define the following terms, as shown in Table

1.1:

Table 1.1 Fashion Trend Forecasting Definitions

Terms Definition

Fashion Trend Forecasting

“the practice of predicting upcoming trends based on past and

present style-related information, the interpretation and analysis

of the motivation behind a trend, and an explanation of why the

prediction is likely to occur” (Rousso, 2012, p. 296)

Traditional Fashion Trend Forecasting

Fashion trend forecasting, which is produced by humans through

creative methods, where artistic viewpoints, culture, societal

attitudes, and current events serve as inspiration (Grammenos,

2015).

Big-data based Fashion Trend Forecasting

Fashion trend forecasting, which is generated by big data tools

through artificial intelligence and quantitative analysis

(EDITED, 2019).

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Chapter 2

LITERATURE REVIEW

This section will go over the pertinent fashion theories, the current application of

big data in the fashion industry, and critically evaluate the possibility of using big data as

an alternative to the traditional designer-based approach to forecast fashion trends from a

theoretical perspective.

2.1 Fashion Trend Forecasting and Related Theories.

Conventional fashion forecasting is based on human-centric methods, where

forecasters examine the world around them—from culture, business, and arts to science

and technology (Gaimster, 2012). Typically, the forecasters would gather inspirations

from multiple design disciplines to spur creative thinking and predict consumers’ desires.

Forecasters are required to gain deep cultural insights into upcoming trends and to predict

trends through curtailing gathered information on existing fashions (Gaimster, 2012).

Ultimately, fashion forecasters’ role is to recognize upcoming trends, produce trend

reports, and provide methods of implementation to improve business’ product lines and

sales (Kim, Fiore & Kim, 2013).

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Fashion theories suggest that three aspects, namely color, pattern, and design

details, are the most critical components for trend forecasting (Blaszcyk & Wubs, 2018;

Jackson, 2007). First of all, color (such as blue, yellow and red) is essential to fashion

trend forecasting since it is a building block for creating trend-right, and top-selling

designs (Blaszcyk & Wubs, 2018). Studies find that when shopping for clothing, both

online and offline, consumers are often initially attracted to a garment by its color (Park,

Kim, Funches, & Foxx, 2012). Through differentiating the fashion colors that will be

popular either long-term throughout the whole season or short-term that rapidly rise,

peak, and fizzle out, companies will be able to place the right product design at the right

time in the market (King, 2012, p. 540). Empirical studies also suggest that the accuracy

of color forecasting can be critical to a garment’s ultimate success in retail sales (Choi,

Hui, Ng, & Yu, 2012).

The second aspect is the pattern (such as stripes, spots, and checks), which refers

to ‘a repeated decorative design that can be printed, stitched or woven into a fabric’

(Ambrose & Harris, 2007, p.182). Like the color, the pattern also plays a vital role in

fashion trend forecasting since it is another primary clothing attribute that influences a

consumer’s purchasing decision (Rousso, 2012). For example, in 2018, garments and

footwear that used leopard print proliferated among young consumers, helping fashion

brands and retailers that carried such products achieve substantial business success (Yau,

2018). As another evidence of the importance of trend forecasting for pattern, data

indicates that apparel featuring floral and lace patterns overall achieved a higher

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inventory turnover and greater profit margins than those with spots and stripes patterns in

the U.S. retail market between 2017 and 2018 (EDITED, 2019).

The third aspect is the design details, which refer to the fabric type and shape of

the clothing, such as denim for fabric and a round neck for shape (Jackson, 2007). The

design details affect nearly every product offering, from jackets to dresses, making it a

crucial component of fashion trend forecasting (Resnick & Montania, 2003). Successful

forecasting for design details further allows retailers to maximize profits by

implementing them in multiple styles for a period that may even extend beyond the

current season (Jackson, 2007).

2.2 Using Big Data in the Fashion Industry

With the increasing availability of data inputs and the advancement of related

analysis tools, fashion companies have begun to take advantage of the threshold of

opportunities provided by big data to improve their business operations (Thomassey &

Zeng, 2018). Studies show that the current application of big data by fashion companies

is particularly popular in the business aspects, such as demand forecasting, pricing

optimization, supply chain management, and consumer behavior analysis (Silva, Hassani,

& Madsen, 2019). Scholars from multiple academic disciplines also have been

developing mathematical models and algorithms to explore new ways of using big data to

solve specific fashion business problems, such as improving speed to market and

controlling the inventory level (Choi & Hui, 2011; Ren, Chan, & Ram, 2017; Boone,

Ganeshan, Jain, & Sanders, 2019). For example, Choi & Hui (2011) develops the

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Extreme Learning Machine Fast Forecasting model to yield more accurate sales demand

forecasts for fashion companies based on inputs from a wide selection of sales data at the

stock keeping unit (SKU) level. Similarly, Pantano, Giglio, & Dennis (2019) use machine

learning to analyze social inputs from consumers’ tweets rather than sales data to

understand the popularity of different fashion brands. However, the research methods

applied and the source of data used by the existing studies varied significantly, making it

challenging to compare the results and evaluate their application values to the fashion

industry.

On the other hand, a growing number of big data analysis tools dedicated to the

fashion apparel sector, such as EDITED and Trendalytics, are launched to the market.

These tools, in general, allow fashion companies to more effectively leverage data

collected from multiple sources, such as social media and e-commerce websites, to

improve their decision makings in product assortment and pricing (EDITED, 2019;

Trendalytics, 2019). Despite these big data tools’ popularity among industry users,

however, their usage by academic studies remains rare.

2.3 Debate on the Application of Big Data for Fashion Trend Forecasting

Compared with its much broader application in the business aspects, the use of

big data for fashion trend forecasting as a creative activity is still at its nascent stage, yet

remains promising (Choi et al., 2014; Israeli & Avery, 2018; & Ren et al., 2017). On the

one hand, some studies suggest that because consumers’ fashion taste stays relatively

stable over time, it is feasible to use historical data such as purchasing history to predict

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what fashion patterns, colors or styles consumers may like in the future (Gaimster, 2012;

Israeli & Avery, 2018). The studies advocating the potential of big data-based fashion

trend forecasting also emphasize the ability of data science to target upcoming trends and

allow companies to more quickly create popular and best-selling items based on concrete

numbers (Joshi, 2018). Particularly, based on leveraging intelligence contained in

historical records, the big data-driven trend forecasting provides companies with

opportunities to gain more insights into the market dynamics, and better understand

consumers’ tastes, wants and lifestyles, helping to improve the accuracy of trend

forecasting than otherwise (Brannon, 2010; Chaudhuri, 2018). Further, some pioneering

studies have proposed theoretical procedures and methods to use big data tools for trend

forecasting in specific areas, such as color, although few empirical research using real

market data has been conducted so far (Gu & Liu, 2010; Thomassey & Zeng, 2018).

However, the big data-driven trend forecasting does not come without concerns

and challenges. First, despite its powerful advanced machine learning techniques, big

data has demonstrated difficulty in understanding and processing critical cultural factors

that influence fashion trends, such as societal attitudes, movements in politics, ethics,

emotion, and aesthetics (McDowell, 2019). Second, since big data does not have original

creative thinking skills or an aesthetic perspective, it is of concern that big-data based

trend forecasts may not be able to satisfy consumers’ desires for fresh, out-of-the-box

designs (Ming Law et al., 2004). Additionally, even a small and medium-sized retailer

today could carry hundreds of thousands of clothing in different categories, styles,

patterns and colors (EDITED, 2019). Such a large number of apparel items newly

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released or sold every season makes it highly challenging for big data to pinpoint and

narrow down what design details will be the most popular, given the similar data inputs

used to generate the forecasts (Arte, 2017).

2.4 Summary

In summary, currently, there is no consensus regarding the possibility and

reliability of using big-data tool generated fashion trend forecasting over the traditional

fashion designer-based methods. The studies advocating the potential of big data

frequently emphasize the ability of data science that can quickly target upcoming trends

and allow companies to create popular, best-selling items based on concrete numbers

(Joshi, 2018; Israeli & Avery, 2018). However, other research contends that big-data

based fashion trend forecasts can be unreliable due to its limitations to assess cultural

data and satisfy consumers’ desire for the original product (Ming Law et al., 2004).

Studies that support traditional trend forecasting methods further stress the importance of

forward-thinking designers due to the unpredictable nature of fashion (Ming Law et al.,

2004).

Additionally, despite the heated debate, there is a lack of empirical studies that

evaluate the effectiveness of big-data based fashion trend forecasting compared with

traditional fashion trend forecasting methods.

This study will fulfill these critical research gaps, and provide fashion retailers

and academic institutions a greater understanding of the possibility, reliability, benefits,

and limitations of using big data tools to forecast fashion trends. Overall, the empirical

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results of this study will enhance fashion retailers’ fashion trend forecasting decisions as

well as academic institutions’ curriculum on big data and fashion trend forecasting.

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Chapter 3

METHODS AND DATA

Given the competing theoretical views regarding the feasibility and effectiveness

of using big-data tools for fashion trend forecasting and the lack of empirical studies, this

study intends to fulfill this research gap. Specifically, the study will focus on evaluating

the similarities and differences of the results of fashion trend forecasts generated by the

traditional approach versus those based on using the big-data tool.

3.1 Data collection

For fashion trend forecasts generated in a traditional approach, this study

collected data from WGSN, a world-renowned service provider for fashion trend

forecasting (WGSN, 2019). WGSN’s trend analysis is consistently cited throughout

academic literature as one of the most trusted sources for traditional fashion forecasts

(Jackson, 2007; Rousso, 2012).

Specifically, based on data availability, this study used all the 20 fashion trend

forecasts created by WGSN for womenswear in the Spring/Summer 2018 (S/S 18) season

targeting the U.S. retail market. Notably, the United States is one of the largest apparel

consumption markets in the world, and womenswear typically accounts for over 60

percent of apparel products available in the market (EDITED, 2019).

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As shown in Table 3.1, each of these 20 forecasts focuses on a particular product

category (such as ‘Dresses & Skirts’) during a specific time-segment of the S/S 18 season

(such as ‘Spring transitional’).

Table 3.1 Fashion Trend Forecasts Generated by WGSN

Case Market Season Product category

1 Womenswear Spring Transitional Trousers & Shorts

2 Womenswear Spring Transitional Jackets & Outerwear

3 Womenswear Spring Transitional Swimwear

4 Womenswear Spring Transitional Dresses & Skirts

5 Womenswear Spring Transitional Knitwear

6 Womenswear Spring, Mid-Spring & Festival Trousers & Shorts

7 Womenswear Spring, Mid-Spring & Festival Jackets & Outerwear

8 Womenswear Spring, Mid-Spring & Festival Swimwear

9 Womenswear Spring, Mid-Spring & Festival Dresses & Skirts

10 Womenswear Spring, Mid-Spring & Festival Knitwear

11 Womenswear Summer & High Summer Woven Tops

12 Womenswear Summer & High Summer Trousers & Shorts

13 Womenswear Summer & High Summer Jackets & Outerwear

14 Womenswear Summer & High Summer Dresses & Skirts

15 Womenswear Summer & High Summer Knitwear

16 Womenswear Summer Transitional Swimwear

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17 Womenswear Summer Transitional Trousers & Shorts

18 Womenswear Summer Transitional Jackets & Outerwear

19 Womenswear Summer Transitional Dresses & Skirts

20 Womenswear Summer Transitional Knitwear

Data source: WGSN (2019)

In correspondence with these 20 traditional fashion trend forecasts, we generated

another 20 comparative trend forecasts by using EDITED, a big data tool, which covers

the real-time pricing, inventory and assortment information of over 30 million apparel

products at the SKU level sold by more than 90,000 brands and retailers in the U.S.

market since 2016 (EDITED, 2019). Each trend forecast generated by EDITED focused

on the exact same product category of womenswear targeting the U.S. market during the

exact same time segment of the S/S 18 season as their paired WGSN forecast. As

explained in Table 3.2, when using these millions of data points to generate trend

forecasts, we asked EDITED to consider three factors deemed as the most critical for

market-popular fashion items, including inventory level, retail price and markdown, and

frequency of replenishment (Sterlacci & Arbuckle, 2009; Heching, Gallego, & van

Ryzin, 2002; Burns, Mullet & Bryant, 2016).

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Table 3.2 Fashion Trend Forecast by EDITED: Key Factors Considered

Factors Justification

Inventory level The apparel items need to maintain a high inventory level and

always be in-stock during the examined period. The high stock level

implies that retailers were willing to carry these items because of

their popularity among consumers (Sterlacci & Arbuckle, 2009;

Balar, Malviya, Prasad, & Gangurde, 2013).

Pricing and

discount

The apparel items were always sold at the full price during the

examined period, which implies that retailers did not need to use

markdown to drive more sales because of the genuine popularity of

the items (Heching, Gallego, & van Ryzin, 2002).

Replenishment The apparel items need to be replenished at least once during the

examined period. Having replenishment suggests that retailers

intended to carry sufficient inventories and avoid stockout for these

items because of their popularity among consumers (Burns, Mullet,

& Bryant, 2016).

Note: For the purpose of the study, in addition to the factors stated above, the apparel items shall also meet the following criteria: 1) womenswear; 2) sold in the U.S. retail market.

3.2 Data analysis

Following Jackson (2007)’s principles of fashion forecasting, this study first

conducted a content analysis of each of these 40 paired trend forecasts (i.e., 20 generated

by WGSN and 20 generated by EDITED) and coded their respective predictions for the

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color (such as ‘red’ and ‘green’), patterns (such as ‘Long Sleeve’ and ‘V-neck’) and

design details (such as ‘Stripes’). To generate objective and unbiased results, we

developed a coding scheme based on EDITED’s handbook for key terms and the industry

common practices as detailed in Table 3.3 (Saldana, 2015). Tables 3.4 and 3.5 specify

definitions for pattern and design details.

Table 3.3 Fashion Trend Forecast Coding Scheme

Design details Pattern Color

Long Sleeve Plain Black

Peter Pan Collar Patterns Grey

Round Neck Stripes Maroon

V Neck Spots Red

Flat Checks Pink

Heeled Floral Fuchsia

3/4 Length Sleeve Lace Purple

Beading Animal Blue

Denim Camo Navy

Fringing Conversational Teal

Jewels/Gems Aztec Aqua

Leather (incl faux leather) Geometric Green

Longline Graphic Lime

Maxi/Long Length Tile Yellow

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Metallic Paisley Orange

Metallic Abstract Copper

Mini Length/Size Brown

Peplum Gold

Sequins Neutral

Sleeveless Silver

Tropical Pattern White

Velvet

Table 3.4 Pattern definitions Patterns Definition

Plain Refers to garments that contain no pattern (i.e. solid in color with no

recurring motif) (EDITED, 2019).

Patterns Refers to garments that contain a pattern (i.e. displays a recurring

motif) (EDITED, 2019).

Stripes “Parallel bands of color” (EDITED, 2019).

Spots “Uniform circles that repeat themselves” (EDITED,2019).

Checks “Gridded prints such as houndstooth, tartan or gingham” (EDITED,

2019).

Floral

“Prints that have flowers, including tropical but excluding lace”

(EDITED, 2019).

Lace “The fabric of lace or the print of lace (EDITED, 2019).

Animal “Garment is made to resemble the pattern of the skin and fur of

an animal such as a leopard, cheetah or zebra” (EDITED, 2019).

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Camo

“Design of irregular patches of dull colours (such as browns and

greens), as used in military camouflage” (EDITED, 2019).

Conversational

“Any print with a recognizable picture in it, such as cats, birds or

stars” (EDITED, 2019).

Aztec “Tribal inspired patterns” (EDITED, 2019).

Geometric

“Geometry is the use of straight lines and shapes to create a pattern.

For example, a garment printed with rectangles, hexagons or

squares” (EDITED, 2019).

Graphic

“Logos, images or text normally placement printed on the front centre

of the garment” (EDITED, 2019).

Tile

“Normally flowery design within a grid repeated over and over”

(EDITED, 2019).

Paisley “A droplet-shaped motif of Persian origin” (Ambrose & Harris, 2007,

p.182).

Abstract “The opposite of geometric. Painterly, abstract patterns” (EDITED,

2019).

Note: Pattern refers to “a repeated decorative design that can be printed, stitched or woven into a fabric” (Ambrose & Harris, 2007, p.182). For pattern identification, EDITED uses powerful image recognition software to analyze the product’s image and notes the listed name of the pattern on the retailer’s website (EDITED, 2019).

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Table 3.5 Design details

Design details Definition

Long Sleeve “a part of a garment covering an arm,” from shoulder to wrist

(Merriam-Webster, 2019).

Peter Pan

Collar

“a usually small flat close-fitting collar with rounded ends

that meet at the top in front” (Merriam-Webster, 2019).

Round Neck a circlular-shaped neck of a garment (Merriam-Webster,

2019).

V Neck “a V-shaped neck of a garment” (Merriam-Webster, 2019).

Flat “of a shoe heel : very low and broad” (Merriam-Webster,

2019).

Heeled “an element called a top piece that is added to the rear end of

the sole of a shoe, lifting the back of the shoe away from the

ground” (Ambrose & Harris, 2007, p.130).

3/4 Length

Sleeve

“a part of a garment covering an arm,” from shoulder to mid-

forearm (Merriam-Webster, 2019).

Beading “Beads that are attached to a fabric for decorative or other

purposes” (Ambrose & Harris, 2007, p. 34).

Denim “Durable, cotton fabric, primarily used to make jeans” (Ambrose

& Harris, 2007, p. 90).

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Fringing “an ornamental border consisting of short straight or twisted

threads or strips hanging from cut or raveled edges or from a

separate band” (Merriam-Webster, 2019).

Jewels/Gems “Decorative objects worn on the person or clothes, often made

with precious metals such as gold, silver and platinum and

gemstones “ (Ambrose & Harris, 2007, p. 142).

Leather (incl

faux leather)

A material produced from the tanned hides and skins of many

different animals, but usually cattle, sheep, pig and goat

(Ambrose & Harris, 2007, p. 151).

Longline Hems that are longer than the normal length for a particular

garment (Rayment, 2015).

Maxi/Long

Length

“an ankle-length skirt” (Ambrose & Harris, 2007, p. 169).

Metallic “resembling metal: such as having iridescent and reflective

properties” (Merriam-Webster, 2019).

Mini

Length/Size

“A short skirt with a hemline that is typically at least 20cm or

eight inches above the knee” (Ambrose & Harris, 2007, p. 169).

Peplum “a short section attached to the waistline of a blouse, jacket, or

dress” (Merriam-Webster, 2019).

Sequins “a small plate of shining metal or plastic used for

ornamentation especially on clothing” (Merriam-Webster,

2019).

21

Sleeveless Apparel without “a part of a garment covering an arm”

(Merriam-Webster, 2019).

Tropical

Pattern

Prints that resemble tropical environments, such as “plants,

fruits, and animals” (The Fashion Folks, 2016).

Velvet “A tufted fabric made from silk, cotton or synthetic fibres with

evenly distributed cut threads to give a short, dense pile“

(Ambrose & Harris, 2007, p.107)

Note: “Design details” refers to aspects of a garment’s shape and fabric type (Jackson, 2007). Examples of garment shape include necklines, hem lengths, and collars (Rousso, 2012). To locate products with specific design details, EDITED searches for products that include the term in its description (EDITED, 2019).

Then, we rated the degree of similarities of the results of the paired trend forecasts

generated by WGSN and EDITED according to the following coding scheme:

• Value=2 (Very similar): EDITED’s forecast matched more than 50% of WGSN’s

forecast

• Value =1(Similar): EDITED’s forecast matched some but less than 50% of WGSN’s

forecast

• Value =0 (Different): EDITED’s forecast matched none of WGSN's forecast

Next, we used the independent samples t-test for the rating scores in the previous

step to evaluate how statistically significant is the similarity of WGSN and EDITED’s

fashion trend forecasts (Ott & Longnecker, 2015). The independent samples t-test is

22

widely used by researchers to determine whether the means of variables between two

different groups are significantly different (such as North, de Vos, & Kotze, 2003 and

Jung & Shen, 2011). For this study, the WGSN forecasts and EDITED forecasts were the

two independent groups while the rating scores for the color, pattern, and design details

served as the variables for mean comparisons.

23

Chapter 4

RESULTS AND DISCUSSION

4.1 Descriptive Analysis Table 4.1WGSN Fashion Trend Forecasts for Womenswear in the U.S. Market in the Spring/Summer 2018 Season

Case Design Details Patterns Colors

1 Denim Plain, Stripes White, Neutral, Brown,

Grey, Black

2 long sleeve, VNeck,

Sleeveless, Leather,

Maxi/long length

Plain Black, Grey, Neutral,

Brown, White

3 metallic, round neck,

sleeveless

plain, abstract,

geometric

red, copper, neutral,

brown, white, green,

black, silver

4 long sleeve, maxi/long

length, mini length/size

plain, checks,

stripes

blue, brown, white,

neutral, black

5 long sleeve, round neck

stripes, plain,

geometric

brown, white, red,

neutrals, blue

6 Denim plain black, white, brown,

neutrals

7 long sleeve, denim lace red, pink, orange, blue,

navy, white, brown

24

8 sleeveless, fringing, v

neck, round neck

geometric, stripes,

lace, floral, tile

black, white, red, pink,

orange, blue, navy,

copper

9 round neck, maxi/long

length, long sleeve,

sleeveless

plain, stripes, lace red, pink, orange, blue,

navy, white, copper,

black, brown

10 long sleeve, fringing stripes, plain blue, white, red, pink,

orange, navy, brown

11 Longline, tropical pattern,

sleeveless

Stripes, plain, floral White, black, pink,

orange, green

12 Tropical pattern Floral, plain Black, pink, green,

blue, neutral

13 Maxi/long length, tropical

pattern

Floral, plain Teal, green, neutral,

pink, blue

14 Maxi/long length, long

sleeve, ¾ sleeve, v neck

plain White, blue, neutral,

green, pink

15 Long Sleeve, VNeck,

Maxi/long length, round

neck, sleeveless

Plain, stripe Pink, blue, navy, grey

16 Metallic plain Black, white, neutral,

maroon, copper

17 Others* plain Purple, gray, black,

white

18 Denim, longline, long

sleeve

plain Blue, navy, black,

green

19 Maxi/long length plain Black, blue, grey,

maroon, neutral

25

20 Longline, VNeck, Long

Sleeve

Plain, stripes Blue, neutral, white,

pink

Note *: No design details match options listed in Table 2.1; analyzed based on trend forecasts from WGSN (2019)

The coded WGSN spring 2018 forecasts are summarized in Table 4.1. A detailed

coding example is illustrated in Figure 4.1.

According to the results, first, overall, most fashion trends seem short-lived and

unstable, with few spanning the entirety of the S/S 18 season. While a few key trends in

design details, pattern, and color were suggested to stay throughout spring, many other

trends were concentrated into shorter, distinct time periods, such as ‘Spring Transitional’

and ‘Summer & High Summer.’ This result is far from surprising. As suggested by

previous studies, specific design details, color, and pattern are often isolated to certain

time periods of the season due to weather or social events. Jackson (2007) describes these

time periods as ‘user occasions’ or when shoppers seek an item due to changing lifestyle

activities, weather, or special events. For example, retailers consistently offer a surplus of

festive party dresses in December to accommodate for the holidays (Blaszcyk & Wubs,

2018). Likewise, WGSN forecasted that ‘tropical pattern design details, floral patterns,

and pink, green, and blue colors’ were going to be popular for women’s trousers & shorts

during ‘Summer & High Summer’ in S/S 18—a period when many consumers go on

tropical vacations or to the beach. Therefore, to satisfy the ‘user occasion’ of going on

tropical vacations or to the beach, these trend predictions were isolated to ‘Summer &

High Summer,’ rather than the entirety of S/S 18, consistent with Jackson’s (2007) trend

theory. Understandably, it is not rare to see fashion retailers constantly add new products

26

during different periods in a season to accommodate consumers’ desire for freshness and

novelty (Ming Law et al., 2004).

Second, among the three dimensions of fashion trend forecasts examined, the

pattern had the highest trend stability, whereas the design details seem to be most

unpredictable. For instance, as shown in Table 3, the pattern ‘plain’ is present in almost

every forecast, and the pattern ‘stripes’ is present in nearly half of the forecasts,

evidencing their relative stability throughout the S/S 18 season. By contrast, most

individual design details are present in less than 20 percent of the total forecasts for the

S/S 18 season, suggesting the propensity of design details to be much less predictable.

Additionally, it is interesting to see color forecasts demonstrate the greatest diverse

results with a greater number of predictions than either design details or patterns.

Furthermore, the results of trend forecasts seem to vary substantially among

product categories. For example, of all product categories examined, knitwear appeared

to have the most stable trends—with ‘stripes, and plain patterns, long sleeve design

details, and blue color’ predicted to be popular throughout the S/S 18 season. Since

knitwear is often made up of classics or staple garments that serve to be worn year after

year (Brannon, 2005, p. 62), trends in the design details, pattern, and color for knitwear

understandably were forecasted to be more consistent. By contrast, swimwear, along with

jackets and outerwear, had the most variability in trend, with almost none predicting

trends that lasted all season. Since swimwear is worn less frequently and is often bought

for short trips or vacations, its designs are typically unique with a relatively higher trend

variability across different periods (Brannon, 2005, p. 61).

27

28

Figure 4.1 Example of WGSN Forecast Coding Illustration (for case #3)

Table 4.2 EDITED Fashion Trend Forecasts for Womenswear in the U.S. Market in the Spring/Summer 2018 Season

Case Design Details Patterns Colors

1 Denim plain, stripes, floral, checks

Black, white, grey, navy,

blue, brown

2 Leather, long sleeve, denim

Plain, stripes, floral,

graphic

Black, white, blue, navy,

grey

3 No result* plain, stripes, Aztec,

floral

Black, white, blue,

brown, pink, grey, red

4 Sleeveless, mini length/size, V Neck

plain, lace, floral,

stripes

Blacks, white, pink,

grey, red, blue, navy

5 long sleeve, round neck, V neck

Plain, graphic,

stripes, aztec

Black, white, grey, blue,

pink

6 Denim Stripes, floral, checks Black, white, grey, blue

29

7 Long sleeve Plain, checks, floral,

graphic, stripes, lace

Black, white, navy, blue,

grey

8 No result* Plain, floral, stripes,

abstract

Black, white, blue, pink,

brown, green

9 V neck, sleeveless, mini

length/size, maxi/long

length

Plain, floral, lace,

stripes

Black, white, pink, blue,

red, grey

10 Long sleeve, round neck, V

neck, sleeveless, metallic

Plain, graphic, stripes Black, white, grey

11 V neck, long sleeve,

sleeveless

Plain, floral, stripes,

checks, graphic

Black, white, navy, blue,

neutral, yellow, green

12 No result* Plain, stripes, checks,

floral, spots

Black, blue, navy, white,

neutral, green

13 Long sleeve, leather Plain, checks, stripes,

animal

Black, blue, grey, white,

green, navy, pink,

neutral

14 Sleeveless, V neck, mini

length/size, maxi/long

length

Plain, lace, floral,

stripes

Black, navy, white, blue,

pink, red, neutral

15 Sleeveless, long sleeve,

round neck, V neck

Plain, stripes, floral,

lace

Black, neutral, white,

pink, navy, grey, blue

16 No result* Plain, floral, stripes Black, white, orange,

blue, navy, pink

17 No result* Plain, floral, stripes Black, navy, grey, blue,

brown, green, white

18 Long sleeve Plain, checks, stripes,

animal

Black, blue, green,

brown, navy, grey

19 V neck, mini length/size,

long sleeve

Plain, lace, floral,

stripes

Black, navy, red, blue,

maroon, white

30

20 Long sleeve, round neck, V

neck

Plain, stripes, checks,

floral, lace

Black, neutral, grey,

brown, pink, white,

green

Data source: Coded based on trend forecasts by EDITED (2019). The coding scheme is detailed in Table 3.3 Note*: EDITED was unable to pinpoint a clear design detail to be popular in the market.

Table 4.2 summarizes the coded EDITED forecasts for womenswear in the U.S.

market during the S/S 18 season. Compared with the forecast results produced by

WGSN, trend forecasts generated by EDITED, as a big data tool, appeared to be much

more stable and coherent, with most trends spanning the entirety of the S/S 18 season.

This result, however, is far from surprising. Notably, EDITED-based trend forecasts used

popular selling items in the market as inputs, which include not only trendy apparel but

also basic fashion (EDITED, 2019). For instance, basic wardrobe essentials, such as a

pair of work slacks or a black dress, often generate stable sales revenue for retailers,

whereas few retailers carry trendy items only to avoid high business risks (Israeli &

Avery, 2018). Additionally, the results of the EDITED forecasts are based on items that

had been replenished in the stock frequently enough, evidence that the item has been

consistently popular in the market and thus is more likely to look ‘similar’ in fashion

trends throughout the season (Burns, Mullet, & Bryant, 2016).

Specifically, for the S/S 18 season womenswear fashion trends in the U.S. retail

market generated by EDITED:

First, the forecast result for the color is the most stable among all the three

dimensions examined. It is interesting to note that almost all forecasts, regardless of the

31

clothing type and sub-season, contain basic colors—‘black’ and ‘white’ in their trend

predictions (Min, 2015). Beyond basic colors, EDITED also identifies ‘blue’ and ‘pink’

to be popular throughout the S/S 18 season. The lack of color diversity in EDITED’s

forecast results echoes the concerns that generating inspirational, creative color palettes

could be a weakness of big data-based fashion forecasts (Tehrani & Ahrens, 2016).

Second, the pattern forecasts by EDITED overall displayed more diversity than

the results by WGSN. For example, Table 4.1 and Table 4.2 show that for a particular

category of womenswear in the U.S. market during a particular sub-season in S/S 18,

EDITED typically forecasts 3-4 patterns to be popular compared with only 2-3 generated

by WGSN. A possible explanation for the greater pattern diversity of the EDITED

forecasts is that big data tools can more easily monitor the popularity of all patterns

available in the market simultaneously, whereas humans can only validate pattern trend

predictions in a much narrower scope once at a time (Rousso, 2012).

Additionally, the trend forecasts by EDITED have fewer variations among

product categories than the results of WGSN. As shown in Table 4.2, except for the fad-

oriented swimwear, the EDITED-generated trend forecasts for other product categories

were fairly consistent and interconnected across different sub-seasons in S/S 18.

4.2 Statistical Analysis

Table 4.3 WGSN and EDITED Trend Forecasts: Results Comparison

Similarity/Content Design Details Patterns Colors

Very Similar 30% (N=6) 85% (N=17) 55% (N=11)

Similar 40% (N=8) 15% (N=3) 45% (N=9)

32

Different 30% (N=6) 0% (N=0) 0% (N=0)

As summarized in Table 4.3, the similarity in the results of trend forecasts

generated by WGSN and EDITED varied across the three dimensions examined.

Specifically, the forecasts for the design details by the two approaches turned out to be

the least similar with 30% (N=6) cases being totally ‘different’ (i.e., EDITED’s forecast

matched none of WGSN's forecast). In comparison, the forecasts for the color and pattern

shared more common outcomes, with no cases being totally ‘different.’ Meanwhile,

WGSN and EDITED’s predictions for the pattern had the highest degree of similarity

overall, with 85% (N=17) cases being classified as ‘very similar’ (i.e., EDITED’s

forecast matched more than 50% of WGSN’s forecast). Likewise, the WGSN and

EDITED forecasts for the color matched well too, with over half of the cases (55%,

N=11) being classified as ‘very similar.’

Table 4.4 Results of Independent Samples T-test

Variables/Hypothesis Mean=0 Mean=1 Mean=1.5 Mean=2

Design Details 5.627**

(0.00)

0.000

(1.00)

-2.814**

(0.01)

-5.627**

(0.00)

Pattern 22.584**

(0.00)

10.376**

(0.00)

4.273**

(0.00)

-1.831

(0.83)

Color 13.581** 4.819** 0.4381 -3.943**

33

(0.00) (0.00) (0.666) (0.01)

**: p<.01 at the 99% confidence level; *: p<.05 at the 95% confidence level

To further statistically evaluate the degree of similarity between the forecast

results generated by WGSN and EDITED, we conducted the independent sample T-test

on the 20 paired trend forecasts (i.e., a total of 40 trend forecasts listed in Table 4.1 and

Table 4.2). The result of the test is shown in Table 4.4. Specifically:

First, according to the results, we rejected the null hypothesis that the mean value

for “design details” can be 0 (p=0.00), 1.5 (p=0.01) and 2 (p=0.00) at the 99% confidence

level. However, we could not reject the null hypothesis that the mean value for design

details is 1 (p=1.00>0.05) at the 99% confidence level, meaning WGSN and EDITED’s

forecasts for design details are statistically ‘similar’. Second, we rejected the null

hypothesis that the mean value for pattern can be 0 (p=0.00), 1 (p=0.00), and 1.5 (p=0.00)

at the 99% confidence level. However, we could not reject the null hypothesis that the

mean value for the pattern is 2 (p=0.83>0.01), at the 99% confidence level; thus, WGSN

and EDITED’s forecasts for the pattern statistically are ‘very similar’ as defined by the

study. Third, while we rejected the null hypothesis that the mean value for the color can

be 0 (p=0.00), 1 (p=0.00), or 2 (p=0.01) at the 99% confidence level, we could not reject

the null hypothesis that the mean value for the color is 1.5 (p=0.666>.05), at the 99%

confidence level, indicating that WGSN and EDITED’s color forecasts are between

‘similar’ and ‘very similar.’

Overall, the statistical analysis confirms the promise and potential of using big

data to forecast fashion trends since all forecasts were at least ‘similar’ (i.e., EDITED’s

34

forecast matched some but less than 50% of WGSN’s forecast). The results also suggest

that big data seems to be more effective in generating forecasts for the pattern and color,

whereas its capability of predicting the design details raises more questions. This result is

not entirely surprising. As suggested by previous studies, the pattern and color are more

straightforward and predictable, whereas the multiplicity of the design details makes it

more difficult for big data to forecast (Jackson, 2007).

35

Chapter 5

IMPLICATIONS AND FUTURE RESEARCH AGENDAS

5.1 Findings

This study empirically evaluated the similarities and differences of the results of

traditional human-based fashion trend forecasts (WGSN) with the ones generated by big

data tool (EDITED). Based on the comparison of 20 paired fashion trend forecasts for

S/S 2018 womenswear in the U.S. retail market generated by WGSN and EDITED and

by using the independent sample t-test, the study finds that:

First, at the 99% confidence level, WGSN and EDITED’s forecasts statistically are

suggested as ‘similar’ (i.e., EDITED’s forecast matched some but less than 50% of

WGSN’s forecast) for the design details, such as fabric and shape of the clothing.

Second, at the 99% confidence level, WGSN and EDITED’s forecasts statistically

are suggested as ‘very similar’ (i.e., EDITED’s forecast matched more than 50% of

WGSN’s forecast for the pattern of the clothing.

Third, at the 99% confidence level, WGSN and EDITED’s forecasts for the color

statistically are suggested between ‘similar’ and ‘very similar.’

5.2 Implications

The findings of the study fulfill a critical research gap regarding the feasibility of using

big data for fashion companies’ creative activity and significantly enhance our

36

understanding of both the advantages and limitations of using big data for fashion trend

forecasting (Choi & Hui, 2011; Ren et al., 2017). The findings of the study also have

several important implications:

First, the findings of this study suggest the great potential of using big data tools

to aid fashion companies’ forecasts and the creation of new products. Notably, the results

of the study reveal that many of the paired WGSN and EDITED forecasts showed a high

degree of similarity, particularly regarding patterns and colors. As these two aspects are

critical components of fashion trend forecasts, fashion companies could consider

adopting big data tools to help improve the accuracy of pattern and color forecasts.

Several fashion companies, such as Gap, Madewell, and Puma, have already started to

incorporate big data tools to improve their fashion trend forecasting methods (Israeli &

Avery, 2018).

There is also a growing number of big data tools newly launched to the market,

such as Trendalytics, aiming to help fashion brands and retailers improve their business

operations through big data analysis (Trendalytics, 2019). The findings of this study

suggest that adding and strengthening the functions of fashion trend forecasts could be a

promising area for these big data analytics tools to expand their service to fashion brands

and retailers.

Further, it is interesting to note that even traditional players in the fashion trend

forecast business, such as WGSN, have begun to explore the role of big data in trend

forecasting. For example, WGSN recently launched several tools, including a consumer

37

insight survey and WGSN Instock, to help fashion companies identify emerging

consumer preferences for fashion products supported by data analysis (WGSN, 2019).

Second, the findings of the study also illustrate the limits of using big data tools in

fashion trend forecasts. Notably, WGSN and EDITED produced different predictions for

design details. This result, however, echoes the findings of previous studies, which shows

that consumers’ desire for novel, innovative, and exciting design details are shaped by

many unpredictable and complex factors, making it difficult for big data to predict based

on the inputs from historical data alone (Barnes & Lea-Greenwood, 2010; Ming Law et

al., 2004). a

It shall also be noted that some design details suggested by WGSN, such as ‘patch

pocket’, ‘wide leg’, or ‘dropped waist’, were beyond the coverage by EDITED (EDITED,

2019; WGSN, 2019). This result suggests that the feature and capacity of the big data

tool is another factor that may limit the flexibility and outcome of the forecasts.

5.3 Future Research Agendas

Despite the interesting results, this study also has several limitations that future

research might overcome.

First, while this study looked at womenswear only, future studies may yield trend

results by looking at other product categories, such as menswear and children’s wear. It

will be interesting to compare the capability of big data tools in forecasting the fashion

trends for different product categories. The results will help us understand the strengths

38

and weaknesses of using big data tools for creative activities in the fashion industry

further.

Second, the method of this study also can be applied to investigate fashion trends

in other seasons, such as Fall/Winter and Resort. Specifically, forecast results may

change due to seasonal variations in product categories associated with special events and

weather. For instance, most Fall/Winter fashion lines often include a ‘holiday’ collection,

featuring party dresses and eveningwear, to accommodate the frequent celebrations that

occur in November and December. Holiday collections may be easier for big data to

forecast since specific colors, such as red and green, as well as the design details, like

sequins, are consistently popular year after year—enabling big data tools to forecast

based on historical data easily. However, Resort wear collections may present a great

challenge for big data tools. These collections are commonly released in January—a

cold-weather month in the Northern Hemisphere—but provide apparel, such as

swimwear, for vacationing in warm areas.

Moreover, future research may explore alternative big data methods to fashion

forecast, such as considering inputs from social media or predictive analytics. Research

that integrates traditional and big data-based fashion trend forecasting techniques can

reveal valuable insight on how to optimize the advantages and disadvantages of both

methods. For example, traditional fashion trend forecasting can provide inspiration that

satisfies consumers’ desire for originality while big data-based methods reduce business

risk by providing commercial inputs (Israeli & Avery, 2018; Ming Law et al., 2003).

Finally, as the fashion industry becomes increasingly data-driven, understanding how to

39

incorporate data analytics into fashion education will be essential to promote the

preparedness and success of fashion students. Fashion educators are already beginning to

incorporate data analytics into fashion trend forecasting and merchandising courses by

teaching students how to use EDITED. Undoubtedly, big data’s creative applications in

fashion trend forecasting hold immense potential to foster fashion companies’ success

and largely benefit the fashion industry.

40

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