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Data is in the Details: The benefits of unified product attribution across brands

Personal Preference Defined By Attributes 

Every shopper has preferences about the clothing and shoes he or she chooses to wear; these preferences help us express our personal styles. These preferences also tend to follow common trends throughout a shopper’s wardrobe, creating predictability about the clothing and shoes each shopper continues to buy. 
In the fashion industry, these preferences are known as attributes, characteristics such as color, sleeve length, dress style and neckline that technically define an item. They are the details that make a brand, garment or shoe unique and enable the wearer to express their personal style. An attribute can be a subtle detail that the shopper may not know he or she gravitates towards. 

Applying Attributes

Attributes drive True Fit's understanding of shoppers; each shopper is unique and has his or her own set of preferences. Each garment is also unique and made up of its own set of attributes.
Attributes can also be identified by a specific “reason” or “occasion” that  the shopper is motivated to buy for. For example, Mark might be shopping for an upcoming event, like a job interview. The material, style, color, and price range of his blazer and button down combo, plus whether he pairs it with a patterned or solid tie and pocket square all qualify as attributes that define the message Mark is looking communicate during his interview. 
Take a look at the images below. By applying attributes about the dresses worn from the women below, data leads us to make a few assumptions:
  1. Both have an affinity for "off the shoulder" neckline 
  2. Both like the a-line cut of dresses 
  3. One gravitates to bold prints when shopping and one prefers to be bold through through solid color

Different attributes, like pattern neckline and sleeve type, define how shoppers choose clothing. Often times attributes repeat themselves across wardrobe items..png
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Trends emerge as attributes from purchase data is decoded and aggregated, and retailers can use this attribute insight to learn about each shoppers likes and dislikes.

Historically, there has been no universal dictionary or defined standard for attributes in the apparel and footwear industry. Just like sizing, attributes are defined differently by each brand. The measurements or cut of wide leg denim likely differ across multiple denim brands

However, True Fit’s dedicated team of fashion experts translates attributes into data as part of the retail industry's largest data set, representing to millions of styles across regions, stores, categories and brands. By applying a unified structure of attributes to each product we can fully understand the details that make up each garment or shoe in the True Fit network, called the Fashion Genome.  

Translating Data into Personalization

Many brands and retailers utilize attribution performance data as a foundational way to drive sales planning, manufacturing, merchandising and markdown decisions. Brands and retailers rely on attribution performance data every day to make timely business decisions through the process of connecting assigned attributes within each product to transaction history. (For example, a scoop neck knit might perform better than a mock neck knit in last year’s Autumn Winter line; that insight helps shape next year’s Autumn Winter assortment.)

Details are data and at True Fit we understand both fashion’s details and how that translates to the buying and returning patterns of each shopper. Retailers and brands can leverage connected data to create a personalized shopping experience. For every single shopper, retailers have the ability to present the most relevant products to their shoppers; to either only show shoppers items that match attribute affinity trends or prioritize items that the shopper gravitates towards to ensure she or he finds what each likes easily.   

When shoppers have a more personalized shopping experience, they are more likely to feel understood by the retailer, and in turn return to make a repeat purchase. In fact, we know that when shoppers receive a personalized experience they return to a retailer more frequently and convert at a higher rate than those who don't receive a personalized experience, with 31% higher sessions and a 161% higher conversion rate.  

Read how retailers across the globe are leveraging attribute data to help drive incremental revenue lift.

Can unified attribution data help your brand deliver personalized experiences to shoppers, plus help make design, manufacturing, merchandising and markdown decisions? Learn about your shoppers beyond your brand to drive higher session numbers and conversion rates by contacting True Fit here.

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