According to research, between twenty to forty percent of all clothes purchased online is returned, often due to issues with size or fitting costing fashion e-tailers millions. Need to return ill-fitting clothes purchased from a website can be a disappointing experience. After purchasing a garment you want, you wait– a day, a week, often more and finally receive your order, thoroughly wrapped in paper or plastic. Delighted and positive, you open it, only to find that it doesn’t fit. It’s too loose or too tight, or perhaps doesn’t fall the way you had imagined. You put it back in its box with a dispirited sigh and request a pick up or worse, plan a visit to the post office.
Returns due to the poor fit are also a major pain point for online retailers. In the last few years, fashion and apparel has ended up being the fastest growing eCommerce category and the 2nd biggest after customer electronics, however fit related returns cost fashion etailers millions, not only in terms of lost revenue however also because of the expense associated with shipping and processing them. “Usually the return rate for an online retailer is going to be 20 to 30 percent,” said Sucharita Mulpuru, vice president and analyst at Forrester Research, a global research and advisory firm.” As a retailer, you may have paid for outgoing shipping, sometimes you’ve also paid for return shipping– and a few of those products may now be used and not qualified for resale.
Low User Adoption
Fit solution companies routinely focus the requirement for devices that are low-friction, easy-to-use and appealing. However many of the existing options on the market still rely heavily on user-provided data to surface size recommendations and experience low adoption.
Expensive For Retailers
Fit guidance solutions can likewise be expensive to execute and run. Some retailers partner with companies for photo clothes and render outfits in a sensible method on its virtual mannequins. However this process presently costs retail partners more.
To produce precise results, fit recommendation algorithms also require heavy and consistent input of measurement data. Recommendation engines need a lot of input in order to be efficient which works with retailers and allows shoppers to see how specific garments might look based on their personal measurements and what they already have.
Quick fashion is a reality today with thousands of new items being launched every week; we believe it will be hard to gather enough data rapidly enough to come up with accurate fit recommendations. Finally, to produce accurate 3-D renderings, you need a lot of data, which is costly for the retailers.
If you are an ingrained user who is used to buying 3 garments [in order to try on multiple sizes and return those that do not fit– at significant cost to the retailer], the first time you use a new technology, you’re most likely still going to buy three. The marketing has actually to be lined up to motivate you to choose the recommendations that were put forward.
Technical solutions like digital mannequins and data-driven fit recommendations may have advanced in recent cycles simple alternatives like customer ratings and reviews– even accurate sizing charts– can go a long way towards helping shoppers make better fit decisions, reducing return rates for retailers.
The perfect solution would be where you ‘d input your dimensions when and, in a Google search, you ‘d be able to learn, ‘Here are works that we believe would fit you that are readily available and in stock now. A lot of these companies are doing one-off engagements with merchants and consumers don’t have enough communication with these retailers to make it all that useful.