FITA PRODUCT HIGHLIGHTS
By
Biki John
May 12, 2020
3 min read
In this article, we cover how to reduce returns in e-commerce by focusing on how Fit Analytics is using the power of machine learning to combat the apparel industry’s multi-size purchase problem.
The Multi-Size Purchase Issue
Poor fit is one of the leading reasons shoppers return items they purchase online. Sizing varies across different apparel brands and even within a single retailer’s store. This has led shoppers to mistrust online sizing information and taking matters into their own hands by multi-size ordering.
Why do Shoppers Order in Multiple Sizes?
When CNBC asked David Sobie (co-founder and CEO of Happy Returns) about the ratio of returns between customers who buy in stores and those that shop online, he informed them that “shoppers return 5 to 10 percent of what they purchase in store but 15 to 40 percent of what they buy online”.
Customers shop differently online than they do when they’re in a store. This is due to online limitations like not being able to physically try on an item. To increase the chance of getting the perfect fit, many shoppers have taken to solving the size and fit inconsistency problem by making multi-size purchases. A multi-size order is when a shopper orders multiple sizes of the same item. The goal is to return the sizes that don’t fit well and keep the size they are happy with.
This is one of the reasons why shoppers gravitate towards retailers that have hassle-free returns policies as these lax policies work in their favor. For example, some stores offer to bear the cost of a customer’s return – in some cases shipping is paid by the retailer both ways.
However, whilst these kinds of lax return policies are advantageous to shoppers, they are less so for the retailer who has to suffer the financial impact.
Photo by John Schnobrich
The Costly Impact of Returns
Returns are the bane of retailers’ business because of the burden they impose which include:
Cost of processing returns, in terms of staff and resources
Lack of ability to sell at the original price due to damage or merchandise being out of season
Customer mistrust which has a negative impact on customer loyalty
According to Pazzl, when comparing the impact of returns on brick and mortar stores to online stores, it was noted that “while return rates to stores are around 8%, this jumps to around 25% for items bought online”.
When Barclaycard conducted a survey to learn more about the impact returns had on online retailers, the survey showed that “57% of retailers said that dealing with returns has a negative impact on the day-to-day running of their business, 33% of online retailers offer free returns but offset the cost of this by charging for delivery and 20% said they’d increased the price of products to cover the cost of returns.”
Returns affect a retailers’ profit margin. Statista executed a U.S. survey to assess the financial impact of returns on online retailers and estimated that “return deliveries will cost $550 billion by 2020, 75.2% more than four years prior. Worse, that number doesn’t include restocking expenses nor inventory losses.”
Fit Analytics’ Solution to Reduce Returns in E-Commerce
E-commerce development and optimization agency, Command C, asserts that, online clothing sales currently have an estimated “ 20-40% return rate, and a whopping 77% of shoppers say they have returned a purchase due to poor fit. Additionally, 58% say they would buy more clothes online if they could be assured of fit.”
Clearly, there is a fit problem online. In response to this, Fit Analytics has devised a size recommendation tool – Fit Finder – to help customers find the right fit. This intuitive size advisor is powered by a sophisticated machine learning framework that allows the solution to become more accurate with every fit recommendation it delivers.
Fit Finder helps shoppers find their fit by asking size-related questions e.g. weight and body shape, and then it compares the answers with the data it has accumulated from other shoppers, including if the item was returned or not. With this sophisticated data, the innovative size tool is able to recommend the right size.
Fit Finder is designed to ensure that both shoppers and online retailers emerge as winners. With the aid of Fit Finder, customers find the perfect fit which motivates them to keep returning to the store to buy more items that work for their bodies. On the opposite side of the same coin, retailers experience fewer merchandise returns, a boost in conversions, and are able to capitalize on long-term relationships with customers.
Learn more about Fit Finder and how it helps eTailers to sell smarter.
To learn more about how we can help you and support your specific needs Contact us.
