Modelling virtual size, style and fit conversion tools in online fashion retailing

Modelling virtual size, style and fit conversion tools in online fashion retailing

Sophie Miell, University of Manchester Dr Delia Vazquez, University of Manchester Dr Simeon Gill, University of Manchester

Introduction

The value of the online fashion industry is expected to reach $19.6 trillion by 2019, and despite late adoption to online channels, clothing is now the most frequently purchased product category online (Euro monitor 2014, Mintel 2015a). A proliferation in tablet and mobile browsing has sparked a new generation of digitally habitual consumers (Mintel 2015b), who use multiple channels to gather information to evaluate alternatives through the decision-making process (Balasubramanian, Raghunathan, & Mahajan, 2005; Berry et al., 2010; Cho & Workman, 2011; González-Benito, Martos-Partal, & San, 2015; Rangaswamy & Van Bruggen, 2005). Such unprecedented growth in online sales environments has impacted on modern consumer lifestyles and consequently the smartphone has become synonymous in daily life (Berry et al., 2010), providing solutions to everyday problems; such as garment fit, size or product selection. This has necessitated that retailers provide integrated channel experiences to keep consumers satisfied (Blázquez, 2014; Verhoef, Kannan, & Inman, 2015). As return rates are prevalent in the online fashion industry (Mintel 2014), an inherent challenge for retailers is to reduce the perceived risks (Bauer 1960) a consumer attributes towards an online product or transaction (Yu, Lee, & Damhorst, 2012). Due to being sensory and symbolic, fashion is highly experiential in consumption considered a high risk product category (Dai & Forsythe, 2014; Foscht, Ernstreiter, Maloles III, Sinha, & Swoboda, 2013). It may be argued that more knowledgeable and brand loyal consumers who are familiar with the process of purchasing online, may experience reduced perceived risk (Nepomuceno, Laroche, & Richard, 2014). Indeed, consumers are developing cognitive habits such as ‘mental intangibility’ where product visualisation and pre-purchase expectations are created in the mind (Nepomuceno et al., 2014 p.621). However, retailers additionally need provide the necessary information in order to target new customers.

Product fit is a primary reason for consumers choosing to return fashion products (McKinsey

20 14). Consumer’s lack sensory information (Park & Kim, 2003) but benefit through the increased 20 14). Consumer’s lack sensory information (Park & Kim, 2003) but benefit through the increased

be attributed to the flexibility of a retailer’s returns policy, often at no cost to the consumer (Foscht et al., 2013; Powers & Jack, 2015). Although it may be argued that returns that require ‘low-hassle’

to the consumer may positively impact profit (Davis, Hagerty, & Gerstner, 1998, p. 1). Such approaches to customer service have had a significant impact on environment through reversing logistics processes incurring costs and negatively affecting the retailer’s bottom line; (Foscht et al., 2013) a factor that is relatively under-investigated or addressed by retailers and has arguably become the norm in the modern retailing environment. Retailers and Marketers are increasingly attempting to address this strategic area of lost profit, targeting their expertise and resource to effectively manage returns (Foscht et al., 2013) and what has not been previously uncovered in academic research, is how returns information can be used to lead improvements in the retailer’s supply chain. Several departments of the business such as merchandising, inventory management and buying would be affected if the information from consumer returns and reviews were used lucratively to feedback into the retail business, so the research asks; how can this be realistically implemented in retailer strategy? Do online virtual size, style and fit tools enable the consumer more power in their decision making, or are they a way for the retailer to eliminate unsatisfying consumer product choices? Do such tools work in terms of utilitarian buying facilitation or are they purely a hedonic function in the buying decision making process? How important is the impact of lack of social contact through making decisions using online product conversion tools, and do those with social influence and presence work more effectively? These questions posit the importance of exploring the area of product conversion tools by means of understanding the co-existing influences of both the business and consumer.

Literature Review

Using search terms related to virtual products, 3D scanning and online retail (i.e. online fitting room, online virtual technology, e-sizing and online consumer behaviour) literature was explored within the areas related to style, size and fit visualisation and recommendation. Firstly, previous academic research with virtual clothing stimuli or 3D avatars was reviewed in virtual clothing technology literature. Garment visualisation software and virtual fitting room websites may provide the user with the means to information they may acquire in physical environments (Gill, 2015; J. Kim & Forsythe, 2008; Merle, Senecal, & St-Onge, 2012), such as through the use of 3D Virtual models and fitting rooms. Research in this area has mainly focussed around the simulation of garment fit in digital product development environments or computer aided manufacture.

Research relating to consumer responses to 3D models, avatars and personalised models were categorised as online consumer behaviour literature. The key themes within the literature were tabulated to allow cross-comparison to existing examples of virtual fit interfaces examined through content analysis. The growth of virtual product experience technology has enabled consumers to more realistically assess garments when shopping online (J. Kim & Forsythe, 2008). Online fashion retailing websites are becoming increasingly interactive with opportunities for the consumer to customise, simulate and manipulate features as a form of online entertainment and reduce risk (Fiore & Jin, 2003; Fiore, Kim, & Lee, 2005; Shim & Lee, 2011). Research in this area has progressed from zoom product features to virtual fitting rooms and augmented reality (Yaoyuneyong, Foster, & Flynn, 2014). Thirdly, highly interactive technologies and virtual try-on stimuli was categorised as online marketing literature. Literature in this area has focussed around the marketing based theory of Image Interactivity Technology which assists with fit and sizing in online fashion retailing (Blázquez, 2014; Fiore & Jin, 2003; Fiore et al., 2005).

Methods

Firstly the identification of virtual fit websites was undertaken through detecting products or interfaces through searches and existing research stimuli that offer either business to business (B-B) or business to consumer (B-C) services, these are defined by operational structure and outlined in table 1.1. Secondly, 20 websites were selected out of more than 35 existing virtual fit, e-size and style based websites. Content analysis of 20 websites was used to record details of existing virtual fit interfaces and the results of these methods were compared. The websites, which enabled consumer selection and eventual purchase enabling facilitation of real garments, was the primary inclusion criteria for the study (B-C). These were either retailer-hosted products, or independent interfaces, industry application were excluded for the study (Table 1.1). Many of the virtual fit websites were in beta-testing stages with large UK retailers (Ellis-Chadwick, Doherty, & Hart, 2002; Marciniak & Bruce, 2004) (table 1.1), which provided a basis for investigating their functions within the host retailer’s website. This was undertaken between December 2014 and February 2016. Classification of the websites were made based on how the websites had been used and what they offered the consumer, and this method has been previously used to understand features of fashion websites (Marciniak & Bruce, 2004). This was then used as a basis for guiding the discussion and results by pairing the classification area with the relevant academic theory as shown in table 1.2. Content analysis enables measurement of the relationship between media and the environment, external to the consumer (Krippendorff, 1989), and Firstly the identification of virtual fit websites was undertaken through detecting products or interfaces through searches and existing research stimuli that offer either business to business (B-B) or business to consumer (B-C) services, these are defined by operational structure and outlined in table 1.1. Secondly, 20 websites were selected out of more than 35 existing virtual fit, e-size and style based websites. Content analysis of 20 websites was used to record details of existing virtual fit interfaces and the results of these methods were compared. The websites, which enabled consumer selection and eventual purchase enabling facilitation of real garments, was the primary inclusion criteria for the study (B-C). These were either retailer-hosted products, or independent interfaces, industry application were excluded for the study (Table 1.1). Many of the virtual fit websites were in beta-testing stages with large UK retailers (Ellis-Chadwick, Doherty, & Hart, 2002; Marciniak & Bruce, 2004) (table 1.1), which provided a basis for investigating their functions within the host retailer’s website. This was undertaken between December 2014 and February 2016. Classification of the websites were made based on how the websites had been used and what they offered the consumer, and this method has been previously used to understand features of fashion websites (Marciniak & Bruce, 2004). This was then used as a basis for guiding the discussion and results by pairing the classification area with the relevant academic theory as shown in table 1.2. Content analysis enables measurement of the relationship between media and the environment, external to the consumer (Krippendorff, 1989), and

Table 1. Classification based on interface or product operational structure

Interface/Product

Service Independent

Operational structure

Example

B-C interface

Users create accounts with the interface.

Fitbay

To purchase they are directed to external transactional websites.

B-C product

Retailer hosted

Users create accounts with the product

Dressipi in

hosted on the retailer’s website. The

Very.co.uk

profile the user makes is dependent on the hosting retailer’s merchandise.

Industry

B-B application

Software developed for virtual garment

Optitex

visualisation technology

Source: Adapted from (Marciniak & Bruce, 2004).

Recommendations have been made in order to explain how virtual fit websites will be expected to develop in the future in line with previous academic research, applied to real world websites and their functions, these are coded within the model as research proposition 1: (RP1). The next section will discuss the insights found from content analysis of consumer Virtual Fit websites.

Advances in web technology have resulted in varied media content and design of interfaces, as they are continually evolving in their format (Kim and Kuljis 2010) (table 2).

Findings and Discussion

Three categories were derived through finding interface similarities through content analysis; Size and Style Recommendation, Fit and Size Recommendation and Fit Visualisation. Size and Style Recommendation websites provide the user with a size or garment recommendation based on an algorithm produced through questions. The prerequisite questions and algorithms varied across interface developer, including style preference, body esteem and body satisfaction (Pisut & Connell, 2007; Shin & Baytar, 2013), with other platforms based on a comparison of previous garment purchases. These tools allow for the monitoring of browsing, recording likes and dislikes and a fuller understanding of the consumer purchase process and therefore this was found to be

a prevalent factor for the success of virtual fit interfaces, such as size Dressipi which lead to the first research proposition for understanding and research the tools in the future.

RP1. Consumer reviews of clothing will be monitored and used within the development of virtual fit websites.

Fit Visualisation interfaces are based around an avatar and may include tension maps where the consumer can assess how tight or loose a garment may be. Fit Visualisation websites include more pictorial information than size recommendation websites (Gill 2015). Consumers can view images in contexts that simulate the garment on the body. Through Fit Visualisation, retailers can provide the consumer with a richer experience through the use of avatars where they can grasp

a more realistic experience of the garment (Yaoyuneyong et al., 2014). It is essential that virtual fit technologies are accurate and realistic in their simulation of garments adopted by the consumer for fit assessment (Kim and LaBat 2013). Fit Recommendation websites enable consumers can try on different sizes of garments after entering basic measurements. Textual descriptions and basic images will show them the front and back of the garment. Such websites do not take into account the highly subjective nature of body shape and often images of avatars or models are not detailed enough for consumers to make an accurate assessment. Therefore, in the future websites should be designed to enhance consumer trust with the interface. Similarly, interfaces may use questions regarding previous purchases to produce size recommendations. By combining this information with a specific retailer, virtual fit companies can act as a custodian for where they may need to improve fit, sizing or style within their clothing ranges and impart a personal aspect of exploring to the algorithms. Therefore, in the future Virtual Fit websites should

be designed to enhance consumer trust with the interface:

RP2. Virtual fit websites will be designed to enhance consumer trust.

The websites analysed were predominantly PC compatible with few combining utilisation of multi- channels, which alluded to the need for integration to mobile and tablet as most online fashion retailers are developing mobile compatible websites or applications. Becoming mobile compatible may therefore increase consumer perceptions of such innovative technology, enhancing

perceptions of utility and ultimately increase the potential for further consumer adoption (Shankar & Yadav, 2010):

RP3. Virtual fit platforms will become mobile compatible.

In order to simulate an accurate avatar, multiple measurements are required to produce a realistic presentation of the body (Gill 2015), the basic measurements required for fit recommendation cover height, weight and bra size, indicating the limited accuracy of fit visualisation tools. It would prove conclusive to offer more accurate capturing methods similar to body scanning in order to improve the accuracy of virtual avatars(Power, Apeagyei, & Jefferson, 2011):

RP4. Virtual fit visualisation websites will include more accurate and innovative methods of capturing body measurements, influenced by 3D body scanning technology.

Data driven strategies are becoming more prevalent in the retail industry (Pousttchi & Hufenbach, 2016; Verhoef et al., 2010) and website and application flow is an important factor for the consumer browsing experience. Retailers and researchers recognise that delivering a seamless experiences to consumers is key (Verhoef et al. 2015)

RP5. Virtual fit consumer experiences will be seamless in the online shopping journey.

Table 2. Content analysis results

Category Explanation

Retailer

Theory applied in

Theory applied to

Sample (n): 20

examples

existing literature

content analysis results

selected virtual fit, e- size and style interfaces.

Personal recommendation

Size and Style By entering basic

Dressipi, -

Body esteem

measurements and information

M&S, Shop

(Shin and Baytar

recommendations

N = 10 of total

about previous purchases and

Direct, Arcadia

garment fit, consumers are

Group

Crowd power

E-Satisfaction (Yang

and Wu 2008) prediction from a particular

given a recommended size

Fits.me –

(Labrecque et al.

Technology Confidence retailer. Dressipi includes style

M&Co, Hugo

Big Data information and

Boss, Coats

Yaoyuneyong et

Body satisfaction (Pisut recommendations.

Viyella, LK

al. 2014)

and Connell 2007) Fitbay includes size

Bennett.

Fitbay

recommendations of body doubles to the user's measurements. Style recommendations are provided through browsing other users profiles.

Fit Visualisation Metail and Qvit users can view

Metail – Evans, Telepresence &

Visual Customisation

N = 8 of total

garments on personalised,

F&F, House of

Perceived risk

Virtual Visualisation

websites

computer generated 3D

Holland.

(Shim and Lee

avatars. Tightness and

Qvit

looseness of garments are

Fitbay

Image

indicated through heat/tension

Interactivity

maps. Sheer and colour

Technology

changes can also be made.

(Fiore and Jin

Basic measurements such as

2003, Fiore et al.

height, weight and bra size are

2005, Lee et al.

used.

Fitbay users can view images

Reactions to 3D

of other users wearing

Simulation (D.

garments to depict how a

Kim & Labat,

garment in their body

2013; D.-E. Kim

shape/size would fit.

& LaBat, 2012;

Song & Ashdown, 2015)

Fit Fits.me includes text-based

Big Data (Laney 2001, Recommendation

Fits.me – LK

(Bye, Mckinney,

descriptors of how a garment

Bennett

& Bye, 2007)

Gandomi and Haiden

Weighting: 8%

will fit the consumer based on

Fitbay

2015, Bello-Orgaz et al.

N=2 of total

their measurements mapped 2015)

websites

against retailer size charts. Personalisation (Lee

8%

Consumers can view their and Park 2009) body match and body doubles’

Consumer co-creation garment choices and garment

(Dholakia et al. 2010) fit on other users’ profiles

Curation through Fitbay. Users can comment on fit choices of other users.