While fit has always been a vital cog in the apparel shopper journey, especially as consumers seek assurance that their purchase is the right one, the industry’s digital acceleration in the wake of the Covid-19 pandemic made it a mandate that fit matches shopper expectations.
True Fit, a data-driven fit personalization provider that has dedicated itself to solving this equation for the online shopper, has released a report based on its massive data collection to determine what fashion brands consumers most often cite as their “favorite fitting brands.”
The True Fit Favorite Fitting Brands Report specifically highlights brands such as Old Navy, American Eagle, Levi’s and H&M as shopper darlings when it comes to fit. Kristine Englert, director of enterprise marketing at True Fit, expects these brands all have two things in common: they know their customers well—what they like and what they don’t—and they know what their expectations are in terms of customer experience and the overall brand.
She says that some of the best fitting brands have implemented strategies to speed up the customer feedback loop, which enables them to make changes faster to support shopper needs, such as the recent push to shop earlier throughout the holiday season.
“Old Navy began its Cyber Monday sale on Nov. 19. H&M continues to answer the consumer demand for improved sustainability in fashion,” said Englert. “And American Eagle, which recently launched a major athleisure line, has taken to accommodate a range of contactless-related shopper needs this holiday season. These initiatives exemplify their abilities to listen and react to shopper needs, and applying the same process to product, design and fit helps set them apart as well as keeps shoppers coming back.”
To determine the “favorite fitting brands” in the report, True Fit simply asked shoppers themselves. A brand is defined as “favorite fitting” when a consumer answers the question: “What brand is your favorite fitting [tops, bottoms, etc.]?” during the one-time True Fit registration process.
Throughout the process, consumers then begin to identify items across brands that they love to wear and that they say fit them well. This data builds a “virtual closet” that enables True Fit to personalize the shopper experience in the form of one-to-one personalized style, fit and size recommendations for each consumer across its network of retailers.
True Fit’s analytics team analyzed data from over 20 million registered True Fit consumers over a 13-month period to determine the rankings within the report, while the brands are populated from a pool of thousands of global brands within True Fit’s Fashion Genome.
“That data gets mapped to the Fashion Genome, the largest data set for the fashion retail space which we built, and we’re able to take cues from that product and dissect the meaning behind it using machine learning,” said Englert. If the True Fit customer visits another retail site a few days later, “the shopper will automatically receive a size recommendation whether he or she is new to the site or not, based on an analysis of his or her closet items, mapped to the items in the current shopping experience.”
The favorite fitting brand categories analyzed by True Fit are divided into various categories across men’s and women’s, including age, locale and body type, which includes classifications such as Plus, Petite and Big & Tall.
Moosejaw delivers lessons for all apparel brands
One of True Fit’s clients, Moosejaw, a retailer that specializes in outdoor recreation apparel and gear, is an example of how brands can optimize their fit capabilities.
Operating both its own website and within Walmart.com, which acquired the brand in 2017, Moosejaw partnered with True Fit amid its continued consumer shift to online. The retailer felt the partnership was a necessity to identifying its priorities, such as whether they wanted to implement new value adds including personal shoppers or buy online, pick up in store.
But with the partnership, Moosejaw has also been able to leverage True Fit’s artificial intelligence capabilities to cut down on the growing “size sampling”—the act of buying the same item in multiple sizes to try on at home with the intention of returning what does not fit—and returns.
This paid dividends for the retailer in helping it understand shoppers before they make excess purchases. When online customers placed multiple sizes of the same item into their shopping cart, a change in the UX prompted them to create a True Fit profile. As a result, this allowed True Fit to pair individual consumer data with data in the Fashion Genome to recommend the best fit for hesitant shoppers.
“As a result of identifying size sampling as a growing issue among its shopper base, Moosejaw reduced size sampling rates by 24 percent and as a result, made impactful changes to the complete online shopping experience that contributed to lower return rates and higher revenue,” Englert said.
Many apparel retailers working with True Fit have leveraged the company’s True Insight platform, which collects and aggregates shopper data into digestible lenses that can be used to offer retailers visibility into shopper demographics, buyer trends and aspects such as returns benchmarking and fit inconsistencies in more significant depth.
For example, a retailer can identify return rates by category and compare them to a same-category index to understand performance, and also understand how each style fit runs compared to an industry standard.
Access the True Fit Favorite Fitting Brands Report here.