With half of all fashion e-commerce returns resulting from poor sizing, a size and fit recommendation tool can help retailers to eliminate uncertainty and enhance the customer experience - matching consumers with products they love, buy and keep.
Fashion retailers lose billions each year to product returns and abandoned carts, fueled by the complexity around finding the right size. With technology-powered sizing tools offering a solution, read more to discover how sizing recommendations can support business success.
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E-commerce accounts for 23% of global fashion sales, according to Common Thread, up from just 14% five years ago. In key markets such as the UK, online clothing sales are predicted to overtake physical retail in 2022, with the pandemic prompting many consumers to discover the convenience of shopping from anywhere at any time.
But despite its advantages, online fashion shopping comes with a considerable challenge: navigating the complex and often erratic state of sizing.
With a lack of standardization and no access to a dressing room, the small differences between retailers force customers to search through complicated size charts or place an order and hope for the best. This issue fuels fashion e-commerce’s sky-high return rates, with poor sizing the cause of 52% of fashion e-commerce returns, according to Shopify.
But with the help of technology, retailers can provide an effective solution to customers’ sizing struggles.
Size recommendation tools are digital solutions that analyze each customer’s unique body and fit preferences to provide tailored guidance on which size a customer should purchase.
Often, modern clothing fit recommendation tools are powered by artificial intelligence algorithms, which compare the customer’s inputted data against product data to determine which size would fit best.
These tools come in a variety of shapes and sizes, but, essentially, they remove the complexity around sizing and provide an efficient way for customers to find the right product for their body.
According to the National Retail Federation and Appriss Retail, US shoppers returned 16.6% of the goods they purchased in 2021 – a sharp increase from the 10.6% recorded in 2020. In the apparel market, figures are even higher, with up to 25% of online clothing purchases returned, according to McKinsey, with sizing issues typically to blame.
Every time a purchase is returned, the cost of shipping back and forth between distribution centers, customers and processing facilities is estimated to total 60% of the item’s sale price, according to the Reverse Logistics Association. Reselling these items could help retailers to reduce their losses, but the reality is, only half of the returned garments make it back to store shelves.
Evidently, sizing issues come at a gigantic cost to retailers – and that’s not accounting for abandoned purchases due to sizing uncertainties. According to Forrester, 45% of US customers will abandon a purchase if they’re unable to find an answer to their questions about a product quickly.
Did you know that 3DLOOK has partnered with TA3 SWIM, the creator of innovative shaping swimsuits that support and sculpt the wearer’s body? Using 3DLOOK’s body scanning technology, TA3 SWIM shoppers can snap two photos and instantly receive personalized size recommendations based on their unique body data, providing customers with the confidence they need to checkout.
Quiz-based size recommendation solutions rely on the customer providing answers to a series of questions regarding their body measurements and fit preferences. These quizzes also often take into account how the customer views their own body, asking them to provide an objective assessment of their shape and physique. This data is fed into an algorithm, which compares the information provided against product data to determine the size that best meets the customer’s needs.
Fit quizzes are utilized by the ASOS size recommendation tool, for instance. Customers are asked to enter their height, weight, tummy shape, shoulder shape, age, fit preference, and preferred brands and sizes. This data is used to calculate a size recommendation and a percentage chance that the customer will be happy with the choice.
Quizzes are perfect for customers with privacy concerns or body image issues that make them uncomfortable about sharing photos, but this approach does come with a glaring limitation — results depend on the customer providing accurate information, and even minor inaccuracies can lead to a poor outcome.
Rather than scanning the customer’s body or relying on their best judgment, some solutions analyze the customer’s past purchases. By comparing the product data of a customer’s favorite garments against the products they’re interested in purchasing, these solutions can determine whether a particular size will meet their needs and preferences.
Fashion brand GUESS uses purchase analysis to recommend sizes to its customers in Japan, for example. Customers are able to select from hundreds of alternative retailers and enter the dimensions of products they already own. Instantly and visually, they can see how the width and length of the two products compare.
Purchase analysis provides a simple way for customers to compare items against apparel they already own and love. However, chest size, arm length, shoulder width and so much more can impact whether a product will look good and fit right, requiring a far deeper analysis than most solutions offer.
Like purchase analysis, review analysis solutions rely on historic purchase data to determine whether a particular size is likely to fit the customer. However, in this case, the solution uses data from other customers that have purchased the same product and their opinion on its fit.
The Amazon size recommendation tool, for example, uses information provided in reviews to indicate which size the customer should order. Each product page indicates whether the item is too small, somewhat small, true to size, somewhat large, or too large, and offers guidance on whether customers should order their normal, smaller or larger size.
This offers a simple way for retailers to point customers towards an approach size with no customer input required. However, it also fails to account for differences in fit preference.
Photo-based size recommendation solutions use images of the customer’s body to calculate their measurements. Typically, modern body scanning solutions require the customer to snap photos using their smartphone device. AI technology then extracts measurement data from these photos to compare against product details.
YourFit by 3DLOOK requires customers to take two pictures – one from the front and one from the size – from which 86 measurement points are extracted. Compared to brand- and collection-specific fit data, 3DLOOK’s size recommendation algorithms are able to determine the perfect size and fit for that customer in under a minute.
Photo-based solutions may raise questions about data privacy. However, with due care, these solutions can deliver highly accurate results free from human error in a way that excites and engages the modern consumer. While YourFit does capture user data, this is never shared and is carefully protected using advanced two-way encryption.
All the rage in the early 2000s, hardware body scanning solutions are a rarity in the retail setting nowadays. These systems work in the same way as mobile solutions by comparing the customer’s body measurements against product information. However, as opposed to mobile solutions, hardware scanners use a dedicated tool or machine to collect the customer’s measurements. Typically, these systems come in the form of a 3D scanner, which captures a three-dimensional image of the customer’s body.
Over the years and still to this day, fashion retailers have experimented with creative hardware-based solutions. For example, lingerie label Savage x Fenty recently opened its first brick and mortar store, featuring 3D body scanning installations capable of calculating shoppers’ measurements and providing personalized experiences, such as product recommendations based on their size and shape.
Hardware-powered body scanning solutions can provide accurate results, but they come with a multitude of challenges. Most notably, they require the physical presence of the customer, making these solutions far less convenient than mobile-powered body scanning tools.
According to the National Retail Federation, US retailers lost an estimated $760bn in returned purchases in 2021. As e-commerce sales grow, so too will the losses incurred. Consumers also pay, as prices are inflated to cover losses, as does the environment with returns creating 16m metric tons of CO2 emissions annually in the US alone, according to Optoro.
But implementing size recommendation technology won’t just reduce returns. It can also attract new customers and additional purchases by removing uncertainty around sizing and appealing to the modern consumer. With digital (32%), sustainability (12%) and engagement (11%) ranked as the three biggest opportunities for fashion in 2022, the right size recommendation tool can help retailers to meet all of these consumer demands.
Past reports have highlighted the potentially fatal consequences of sky-high returns on small businesses, and rising supply chain costs will only increase the pressure.
Size recommendation technology can ease the burden on retailers and small stores shouldn’t be discouraged from exploring its benefits — these tools aren’t exclusively for the big name brands. In fact, there are plenty of affordable and easy-to-implement clothing size recommendation systems for woocommerce, Shopify and many other leading e-commerce platforms to pick between, making size solutions accessible to brands of all shapes and sizes.
Boost fit confidence, solve sizing and drive loyalty with the ultimate virtual fitting room experience