Introduction The rapid expansion of fashion e-commerce has reshaped global retail by offering consumers
unprecedented accessibility and choice. Yet, despite its commercial growth, the sector continues
to struggle with structural inefficiencies that undermine both profitability and sustainability.
Chief among these are high cart abandonment rates, substantial product return volumes, and
persistent inconsistencies in sizing standards across brands. Together, these issues not only erode
retailer margins but also generate significant environmental externalities through the destruction
of unsold and returned garments.
Recent research highlights the role of sizing uncertainty as a primary driver of these
challenges, directly influencing both consumer hesitation at the point of purchase and
dissatisfaction post-purchase. In this context, technological innovation, particularly augmented
reality (AR), has been positioned as a potential solution. Virtual try on systems can reduce
uncertainty by enabling consumers to visualize garments on their own bodies or avatars, thereby
increasing confidence, improving conversion rates, and lowering return volumes. While major
retailers have experimented with proprietary AR applications, their adoption remains fragmented
due to the high development costs and technical expertise required, leaving small and mediumsized enterprises largely excluded from these innovations.
This essay examines the systemic challenges of fashion e-commerce, the potential of AR
technologies to address them, and introduces DoesItFit, a cross-platform AR solution designed to
democratize access to virtual try on capabilities. By situating the discussion at the intersection of
economic performance, consumer behavior, and sustainability, the analysis aims to demonstrate
how scalable AR platforms can contribute to both retailer competitiveness and the broader
sustainability agenda within the fashion industry.
Why
Before examining the specific challenges of fashion e-commerce, it is essential to understand the
magnitude of lost revenue across the industry. Fashion e-commerce faces one of the highest
abandonment rates in retail, with 76.48% of carts left incomplete, part of a global $4.6 trillion
loss. According to the Technology Acceptance Model (Davis, 1989; Pavlou, 2003), such
hesitation reflects low perceived usefulness of static sizing tools and high perceived risk in
online shopping.
Online fashion retail faces persistent structural challenges that reduce profitability,
undermine customer satisfaction, and generate significant environmental costs. A central issue is
the exceptionally high rate of product returns (Marriott et al., 2024). In Europe, approximately 20
percent of all clothing ordered online is returned, and between 22 and 43 percent of these
returned items are destroyed before they are ever used (European Environment Agency, 2024).
For retailers, this reality represents not only a financial burden, due to reverse logistics,
restocking, and disposal, but also an operational inefficiency that directly erodes profit margins
(Marriott et al., 2024).
The primary driver behind both cart abandonment and high return rates is sizing
uncertainty. Research shows 52% of shoppers hesitate to complete a purchase when unsure about
fit, while 66% cite poor fit as the main reason for returns. In IS Success Model terms (DeLone &
McLean, 2003), this reflects poor information quality, which undermines trust and satisfaction.
With inconsistent sizing across brands, e.g., a Medium chest varying from 37–41 inches, the
problem has grown into a £25 billion global sizing crisis.
Beyond direct financial implications, the environmental costs of returns and unsold items
are substantial. The European Environment Agency reports that between 4 and 9 percent of all
textile products placed on the EU market are destroyed before use, amounting to an estimated
264,000 to 594,000 tonnes of textiles per year (European Environment Agency, 2024). This
destruction results in avoidable waste and intensifies the fashion sector's already significant
contribution to greenhouse gas emissions. As sustainability becomes an increasingly central
concern for both regulators and consumers, retailers that do not address these inefficiencies risk
reputational harm and may face heightened regulatory scrutiny in the future (Kiran, 2025).
Online shopping requires consumers to rely on static product photos, size charts, and
images of models whose body types rarely match their own. This creates uncertainty about how
items will fit or look in reality, leading to both purchase hesitation and post-purchase
disappointment (Wang et al., 2022). Research shows that 83% of fashion websites fail to provide
sufficient sizing information, leaving consumers without the confidence they need to make
purchasing decisions. The financial implications are severe: while processing a return costs
retailers $20-30 per item, the lost gross margin from a failed conversion often reaches $50 or
more per abandoned cart. Research on augmented reality in online fashion retail shows
significant benefits. According to Barros Picanço et al. (2025), AR enabled virtual try-on
experiences led to a substantial 72% increase in conversion rates, improving from 2.5% to 4.3%.
This heightened engagement is accompanied by a 41.7% reduction in product return rates,
reflecting consumers' enhanced confidence when using AR visualizations.
The market demand for AR is clear: 71% of consumers say they would shop more if AR
were available, and 81% of Gen Z and Millennials already expect it to improve their shopping
experience.
Although AR solutions exist, their adoption remains fragmented and inconsistent.
Leading retailers such as Nike, Zalando, or Sephora have experimented with proprietary AR
features, yet these solutions are generally brand specific, technically demanding, or confined to
individual mobile applications (Sarkis et al., 2025). Smaller and mid sized retailers, who lack the
capacity to develop in-house systems, remain excluded from these innovations (Alam et al.,
2021). AR development costs range from $30,000 to $300,000 or more, with fashion specific AR
applications typically requiring investments of $25,000 to $35,000 for basic solutions. These
substantial upfront costs, combined with the need for specialized technical expertise and ongoing
maintenance, create prohibitive barriers for smaller retailers. As a result, while large retailers can
invest in proprietary AR solutions, an estimated 80%+ of fashion retailers lack access to virtual
try on technologies.
From a practitioner's perspective, the relevance of solving these issues is substantial.
Returns reduce margins and increase operational complexity. Customer hesitation constrains
growth, as shoppers abandon carts or avoid purchasing altogether. The destruction of unsold and
returned textiles carries both environmental and reputational consequences at a time when
sustainability is moving to the forefront of consumer priorities and regulatory frameworks.
Together, these factors point to the urgent need for a scalable, universal AR solution that can
reduce returns, enhance customer confidence, and align the fashion e-commerce sector with
emerging sustainability expectations.
What
DoesItFit is a cross-platform AR oriented product that allows users to try on clothing in a
digital environment. The tool provides users a way to try on clothing without having to order,
wait for delivery and try it on in real life.
The DoesItFit service is implemented by fashion e-commerce companies like Zalando or
About You in their app or website via API. Users can use the implemented tools as a part of the
user experience, by making sure the piece of clothing they are interested in actually fits.
DoesItFit's competitive advantage lies in its cross-platform nature and API first
architecture, which differentiates it from existing AR solutions in the fashion e-commerce space.
While current AR implementations are predominantly brand-specific and require substantial
individual investments, DoesItFit democratizes access to virtual try on technology through a
unified platform approach.
The core differentiation lies in DoesItFit's ability to serve as cross-platform AR
infrastructure that integrates seamlessly across multiple fashion retailers through a single API.
By providing a cross-platform, plug-and-play API, the barrier to entry, traditionally high due to
the need for costly proprietary systems, is drastically lowered. This approach ensures that SMB
sized retailers can implement virtual try on technology without major upfront investments,
technical expertise, or long development cycles.
Secondly, this universality creates data network effects where machine learning
algorithms analyze large data sets to learn, predict, and improve, with more learning generating
more value and producing ever more data in a virtuous circle (Antikainen et al., 2021). Each
interaction contributes to improving AR overlay algorithms and 3D avatar generation capabilities
across all participating platforms, as the platform AI capability learns from data collected on
users to enhance product quality and experience (Tronvoll et al., 2020). Because of the
interoperable nature of DoesItFit, integration costs for e-commerce companies are low compared
to developing it on their own. By connecting the e-commerce platforms with the DoesItFit API
the use of standards can be implemented. This way all file standards, user interface, sizing tools
and data sharing conventions are standardized and can be used across platforms. Because of this
interoperability, the barriers of the network effects will be lowered (Bourreau & Kraemer, 2025)
Furthermore, the platform model enables faster deployment for retailers compared to
proprietary AR development, allowing retailers to implement proven AR technology
immediately rather than undertaking lengthy custom development cycles. Platform based
solutions enable faster delivery and accelerate time to market, providing retailers immediate
access to AR capabilities without the typically lengthy development timeframes.
Finally, the platform’s AR try-on experience is designed to be natively shareable on
social media, enabling users to showcase their virtual looks with followers instantly. This
functionality transforms each try-on into potential organic marketing, creating built in virality
and expanding reach (Krafcik, 2025). Shared try on moments increase customer engagement,
enhance brand visibility, and foster a community driven shopping experience that is increasingly
favored by Gen Z and Millennial audiences.
DoesItFit solves multiple business model inefficiencies and societal problems with the
implementation of the cross-platform AR service. Using digital product fitting tools, research
suggests that fit-related return costs can be reduced by up to 80% (Gustafsson et al., 2021). The
research lays its focus on digital fitting of shoes, where the customers feet are scanned with a
camera under a glass platform. While customers were not able to verify the fit every time, digital
fitting still has a major effect on the amount of returns depending on the actual accurate
verification rate. This has an impact on the three main problems stated in the previous chapter;
cart abandonment, product return costs, and waste/sustainability
As mentioned, the conversion rate in e-commerce by implementing AR is suggested to
increase by 71% if AR-functionalities are included (Barros Picanço et al., 2025). DoesItFit can
contribute to this substantial increase. This will relieve companies of the high percentage of cart
abandonment.
While cart abandonment is a problem of conversion, and does not directly impact the
spending of e-commerce companies, processing returns does increase spending. The cost of
processing a return costs around 17% of the prime cost for each item, not including the
environmental costs. By letting customers fit their desired clothing beforehand, ordering the
wrong piece of clothing or size be decreased significantly with research suggesting a 80% fitverification rate in testing (Gustafsson et al., 2021).
Lastly, the environmental impact of large-scale returns all over the world is substantial
with an estimated 264,000 to 594,000 tonnes of destroyed textiles per year before it is used by
customers (European Environment Agency, 2024). By preventing unnecessary returns caused by
ill fitting clothes, DoesItFit can contribute to Sustainable Development Goals 12 and 13 (UN,
2021)
How
DoesItFit uses two core technologies to directly address high return rates and buyer
hesitation in online fashion. Computer vision based augmented reality overlays and 3D avatars
generated from quick body scans with the user’s smart phone.
Firstly, a shopper simply uploads a photo or turns on live video and the system detects
their body outline, posture and key landmarks in real time. A digital model of the garment is then
placed over and moves naturally with them. This process gives a realistic sense of how the item
will look on their own body rather than on a generic model, immediately reducing uncertainty
about style, colour and appearance. Industry data show that products presented with AR content
see almost double the conversion rates compared with those without; for example, Shopify
reported a 94% higher conversion rate for products with VR/AR content and other studies find
20-40% conversion increases when AR try-on is implemented (Think with Google, 2024 &
Reydar, 2024). Higher conversion is attributed to customers’ greater confidence when they can
visualize items on themselves before purchase.
Secondly, 3D avatars from quick body scans with smart phones, allows DoesItFit to
capture a user’s actual body measurements and shape. Instead of showing clothes on a
standardised avatar, the system simulates fabric on a digital twin of the shopper, which produces
much more realistic size and fit feedback. For more accurate results and scans, wearing slim
clothes during the scanning process is preferred. Research shows that mobile 3D body scanning
yields contact free measurements with good accuracy (Tandfonline, 2023), and studies
comparing real scans to generic avatars find that avatars alone often miss details like body
asymmetry and distribution of volume, which directly affect garment fit (Balach,2019). With this
feature, users can test different sizes or styles on their digital twin before ordering, which reduces
guessing wrong sizes and returns.
Together these two processes tackle the main drivers of online apparel returns. In Europe
roughly 20% of clothing ordered online is returned, and between 22-43% of those returned items
are destroyed before ever being worn (European Environment Agency, 2024). Fit or sizing issues
are the leading cause of returns worldwide (Zheng, 2024). Studies of AR and virtual try on
implementations report 20-30% fewer returns, especially for size-related issues (GetFocal,
2025). By lowering fit uncertainty, DoesItFit not only reduces costly and environmentally
harmful returns but also gives shoppers the confidence to buy. In short, this combination of
computer-vision AR overlays and 3D avatars provides a clear, evidence-backed technological
pathway to higher conversion rates and lower returns for fashion e-commerce.