Inconsistencies in sizing has become a significant problem area for the fashion industry. Online shopping further complicates things. Consumers often need to decode size charts which are typically generic, or physically measure themselves, in order to find items that work, hopefully.
It is important to take these issues into account. Brands need a system that reflects unique sizing & fitting of each product, focusing on the preferences of the shopper as well. There does not have to be a generic “one size fits all” recommendation.
Size universality is an alluring idea that appeals to our need for order, but according to fashion experts, that would make it even harder to buy clothes, given how diverse our bodies are. Instead, we need something to help us navigate the existing size variation. Recommendation widgets that guide us to the right sizes, are about to make it easier to buy clothes that fit without ever visiting a dressing room.
Easysize takes all of these issues into consideration, gets as close to real-life as possible, to deliver the right results. Here's how:
In the past, sizing was just about measurements, because fashion was mainly tailor-made. With the evolution of industry and the concept of ready-to-wear coming into the picture, sizing is not just a mathematical problem of mapping consumer's body measurements to garment measurements anymore.
At Easysize, we care more about the shopper's style and fit and how they want an item to look on them. We don't use size charts, size conversion tables or garment measurements when recommending the size. We avoid anything that would make our recommendation inaccurate, or will be hard for brands to maintain.
In real life, the same shopper might wear a slim-fitted shirt to work and a comfy oversized hoodie to chill at home – their body is the same but the choice of sizes for both the items will be different. It is also possible that the same t-shirt can be bought and worn by a shopper in different sizes and ways: a tighter one to wear underneath a shirt, a looser one – to wear it by itself or exercise in. The possibilities are endless. To deliver exactly all this and more, Easysize uses data which is more relevant than mere measurements. When recommending the right size, we try to get as close to real-life as possible – e.g. replicate how a shopper would choose the size in real life. And hence, every recommendation makes much more sense and instills confidence in the customer. This helps to convert a new shopper into a recurring and loyal one.
Another problem for e-commerce is that the attention span of shoppers is very brief. It is important for a brand to keep the shopper interested and not distract them. This is the reason why Easysize refrains from asking difficult or time-consuming questions that would distract a shopper from shopping – no measurements, nothing that takes time. The interaction with our tool takes less than 10-20 seconds. There is no need for the shopper to leave an online website or shop and download a separate app, for example, to take a body scan, etc. All interactions take place instantly on the online shopping platform itself.
At Easysize, we make a size recommendation for every product individually – e.g. if your size recommendation is Medium for one t-shirt, it doesn't mean that all the other t-shirts will be in size Medium. Our size recommendation is calculated on the SKU-basis guaranteeing that all unique product features (fabric, clothing cut etc.) and all style preferences of a shopper (slim-fit polos but regular-fit cycling t-shirts) are taken into account. We don't use shortcuts - like converting country sizes (UK 8 ⇒ US 4) or mapping one size recommendation to the entire category.
We find shoppers' fashion-doubles – other shoppers in our database that have similar preferences to them to analyse orders & returns data of an online-shop to better understand their shoppers' unique habits. In the case of Fit Quiz, we also utilise direct consumer feedback to better understand how exactly shoppers like items on the SKU-level. By comparing one shopper's preferences to others, we are able to accurately predict what they're likely to enjoy. Think about it as a Netflix algorithm – you watched a few movies, and know it can recommend what you will like to enjoy.
Once we make a recommendation, and a user has purchased and received it, we then reach out with a request for feedback. That not only helps us to improve this particular user’s future recommendations, but also update all affected weights and in turn improve all future recommendations. That closes the loop and allows us to accompany a user on every step of their experience.
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