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Improving sizing and fitting through digitalization

Digitalization projects not only concern customer experience or touchpoint-related activities, but also offer far-reaching possibilities to improve sizing and fitting.

Sizing has always been a key topic in the apparel and shoe industry and can create satisfied and recurring customers through a positive shopping experience. Everyone in the industry is aware of the customer’s desire for “feel-good sizes” and the wide range of body types and individual clothing preferences. In brick-and-mortar retail, customers expect the store personnel to recommend articles precisely suiting to their figure. One problem is that since the size specifications of manufacturers often differ, there are hardly uniform sizes in practice. Furthermore, body shapes have changed over time, but the standardized size tables have hardly changed since the post-war period. Add to that differences in shape caused in production – even for the same sizes – and the result is a large variance between dimensions and body shapes. Ultimately the fit is wrong and in the worst case, this results in a dissatisfied customer. This in turn leads to the non-purchase or return of articles – a scenario that can be avoided by using the possibilities of digitalization.

Individual tailoring represents a great challenge, if not even impossibility, for the mass producer. In the shoe industry there are more and more examples of individual production, e.g. the Adidas Speedfactory, the Vans Customs Platform or Fischer Sports Vacuum. In apparel retail, on the other hand, it is less a matter of individual production, but rather of recommending articles with a fit that suits the customer’s wishes.

This is where digitization comes into play: 3D scanners and sizing recommendation systems are two possibilities.

The first experiments with 3D body scanners in apparel retail date back to the turn of the millennium, but they were never implemented on a wide-scale. Karstadt’s attempt to establish a solution by Human Solutions in 2002 and Adidas’ CyberFIT Body Kinectizer in 2013 are two examples. Currently there are new approaches such as Outfittery’s pop-up scanning stations in train stations and airports or CutForYou in Berlin and Atelier NA in Düsseldorf.

Generally, 3D body scanners are primarily used for individual made-to-measure clothing, whereas mass producers hardly use 3D body scanners thus far. The shoe industry is much more advanced here, as the use of 3D foot scanners in brick-and-mortar retail shows. Examples include: HUMANIC YourSIZE, Sport Check Mifitto 3D-Footscan, LOWA Footscan 360° or Sport-Conrad Amisura Lightbeam. The rapidly evolving possibilities of 3D printing are currently reviving 3D scanning. This is accelerated through the use of virtual 3D models (avatars) in design and product development.

In the recent past, data-driven sizing and fit recommendation systems have been used more frequently. The shopping experience is thus moving away from individual tailoring and 3D body scans, towards digital communication. How it works: Users send their personal measurements and photos of their neck, torso, etc. as well as pictures of desired clothing articles via a mobile app. Based on comprehensive data collections, including 3D body scans, these recommendation systems then predict body measurements and approximate the fit with corresponding articles using artificial intelligence technologies, in particular machine learning and image recognition (i.e. computer vision).

What are the advantages and disadvantages of the solutions and what determines their successful implementation?

Individual measuring via 3D body/foot scanners only really makes sense if a “customized” (i.e. lot size one) product is created with the measurement data, or if the product can be individualized, for instance the inner shape of a (ski) boot or tailor-made shirts/suits. Nevertheless, the number of suppliers of 3D body scanners is quite large, e.g. Human Solutions, VITRONIC, LECTRA, BotSpot, Staramba, Inver-tex, Volumental, Styku, and SizeStream. Amazon has also entered the race, having recently acquired the provider Body Labs.

The central requirement is the availability of a 3D scanner in a retail store. In addition to the relatively high procurement costs, shopfitting aspects (design, space requirements, location, sales area reduction, utilization, etc.) as well as changes to the sales process must be taken into account. For the customer, 3D scanning must be fast and uncomplicated. In particular, the customer must be willing to undergo the measurement procedure and be able to live with the result. This can be tricky, as customers often lie about their actual size and may not want to see the image of the body scan. Up to now, 3D scanner data is not standardized, which makes it almost impossible for customers to transfer their size data between retailers or brands and thus presents a usability issue. Various international initiatives are attempting to offer services via mobile scanning stations in shopping centers (Tailored for You Scanning Events) or by truck (Arden Reed Tailor Truck).

A pure software solution, as a counterpart to 3D scanners, is the aim of size and fit recommendation systems. Their use is particularly relevant for e-commerce applications or if the costs of installing 3D scanners are to be avoided. A wide range of providers provide such software. Solutions via mobile app are available from, MySizeID, Stitch Fix, Rakuten, Curvetips,, and Special size comparison systems, in which manufacturer-independent size specifications and fits can be compared, are offered by True Fit, Fit Analytics, fitfyle, BoldMetrics and

In comparison to 3D scanners, which are needed in every store, a software solution can be a cost-effective alternative and can also be used in brick-and-mortar stores, e.g. via an app on a sales tablet. Customers can use the recommendation systems both via integration in the web shop and in specific apps. However, the main disadvantage is a high but not 100% matching hit rate. This is caused by a large number of possible influencing factors, in particular the data basis (e.g. from images, measuring points, comparison values) as well as the quality of the algorithm used to determine the fitting articles. Such systems are already quite powerful in 2D image recognition, but a 3D calculation is still problematic. This is shown by the example of Stitch Fix, who in February 2018 broke off their attempt to calculate sizes based on images provided by the customer.

A recommendation for or against the different types of systems can only be made on an individual basis.

Nevertheless, mature solutions already exist on the market to support the customer in the purchase process. The use of digital technologies allows customers to better find the right size and improves the shopping experience in the long term. For retailers, this is reflected through a decrease in returns and an increase in turnover.

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