1. What is body data? 

According to a post by Martech Advisor, “Customer data is the behavioral, demographic and personal information about customers collected by businesses and marketing companies to understand, communicate, and engage with customers.” 

Customer body data is a more specific type of information within this broad category. It relates to the customer’s body measurements based on either algorithmic calculations, body scanning, or manual methods of measuring using a tape measure.

  1. Why is body data relevant to apparel brands. Or why should it be?

By understanding real-time and actual customer body measurements, a brand can start to build a more accurate picture of the body shape and sizes of their customer base, instead of just using standard fit models (which might not be an accurate representation of real customer sizes). 

By increasing the accuracy of their customer body data, growing the data set, and looking for patterns based on feedback and machine learning, companies can leverage this data to help significantly improve the apparel design process and customer experience both online and in-store. Accurate customer body data can help brands with the right technology generate detailed insights into fit-related returns and product successes. Combined with garment data specs, these insights can help designers create better-fitting clothing as well as enable brands to connect customers to clothes that fit them better, reducing the need for fit-related returns that divert valuable resources and impact a brand’s bottom-line.  


Bold Metrics uses machine learning technology to accurately predict customer body measurements and help brands unlock the power of body data to reduce returns, boost conversions and improve sustainability efforts in measurable ways.

Additional data such as fit and sizing data, including returns data from customer returns due to poor fit, customer fit feedback and sales data (which indicates which items in which sizes are doing well and which aren’t selling) can also be leveraged in new ways once companies have access to accurate customer body data. 

Example: Smart Size Chart:

Fit technology consultant Mark Charlton’s recent article for WhichPLM (Data: The New Gold) was a detailed look at why data from fit, sizing, customer feedback and sales can and should be used by apparel brands to help address customer’s needs and create clothing recommendations uniquely tailored to individual preferences. Charlton goes on to add how “in a consumer-centric era, understanding individual body shape and size along with individual fit preference is of critical importance. There is no mature data for individual fit preference, how this links to sales, returns, and feedback.

Bold Metrics has the solution with our Smart Size Chart—a size recommendation tool for eCommerce that requires just 4-6 simple data inputs to accurately predict a customer’s body measurements and connect them to their preferred fit, using data that is based not just on body size and garment style, but also on each customer’s unique style preferences. 

In his article, Charlton used the example of a men’s t-shirt- “What if that men‘s slim-fitting tee that is selling really well is being purchased by women and worn as a boyfriend style tee, therefore not slim-fitting at all? How, as a brand, do you measure brand fit intent (the way the product was intended to be worn) versus individual fit preference (how actual consumers wear that product)?”

Using the Bold Metrics Smart Size Chart, the answer is simple. Customers answer 4- 6 easy questions, ones they would know without the need to measure themselves (i.e., height, weight, age, gender etc…) and our robust Virtual Sizer API uses a proprietary algorithm to predict over 50 different body measurements for each customer, creating an accurate and detailed profile of each customer. But it goes beyond that, with the technology taking into consideration each customer’s personal fit preferences and showing them just how tight or loose a garment will be on their body. That tight-fitting men’s tee will be shown to fit loosely, or maybe even as a slightly oversized fit for most female customers. Assuming that they are looking for such a fit, they can then make their purchase according to personal style and fit preferences.


The Smart Size Chart by Bold Metrics shows the customer exactly where the shirt is tight or loose on the body, so they can decide based on their unique fit preference, just like how they would do in-store.

  1. How can a brand unlock the power of customer body data?

Having a large data set doesn’t mean anything without the tools to understand and leverage it effectively. Bold Metrics has spent the last seven years helping trusted brands unlock the power of their customer body data. The result? A much better understanding of what customers want in terms of fit, sizing, and design. By enabling accurate size predictions in detail and at scale, Bold Metrics can create data-backed insights to help supercharge a brand’s supply chain.

Bold Metrics’ powerful tools combine customer body measurements with garment spec data, along with purchase and returns data to draw out insights that can be used by brands to make better-fitting clothes. By creating clothes that fit real customer’s bodies, brands can reduce fit-related returns by 50% or more as customers are matched with clothing in sizes that fit better. Individual garment style, as well as personal style and fit preferences, are also taken into context, giving the customer an improved and personalized shopping experience. Reducing returns and unnecessary manufacturing waste creates a more sustainable approach that cuts down on unused or misallocated resources. It reduces the carbon footprint that arises from transporting and processing apparel returns, most of which tend to end up not recycled or back on the shelves for sale, but in landfill or incinerators.

Apparel brands that leverage body data see an improved customer experience that leverages actual customer body data to reduce returns, boost conversions, and improve sustainability at scale.

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