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3D Doesn’t Help with Sizing Strategies, Data Does

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In today’s guest post, Greg Moore, CEO of BodyBlock AI, shares the importance of sizing data for the apparel market. Greg is a corporate visionary with nearly 15 years’ experience in applying analytics to key business verticals for increasing returns. 

On the way back from PI Apparel in I took some time to digest the week’s events with my team. The resounding message was the transition to digital and the integration of 3D. PVH EU’s STITCH and HATCH teams presented some of their great work as they have almost fully digitized PVH EU’s product lines as well as sales and merchandising platforms. While I see 3D design and pipeline digitization on the rise, it is still only used by about 5% of the industry, and it will take some time for the industry to continue to integrate these tools …but it will happen.

3D and digital tools help to enhance a brand’s sustainability when it comes to product development, but do not do much for the other huge problem in the industry: sizing and fit. Frankly, 3D and digital pipelines allow brands to speed up their time to market, which puts more product on the streets. If these products are not sized for the brand’s consumer, it just exacerbates the returns and sustainability problem.

Creating a Win / Win / Win

Imagine if the apparel industry optimized sizing strategies to produce clothing that best fit their target consumers and integrated 3D and digital tools. It would speed up time to market, reduce costs and environmental impact during product development and sales, as well as reduce the $300B loss due to poor sizing and fit strategies… all while increasing the consumer’s satisfaction. That’s a win / win / win if I’ve ever seen one.

So with this background, I want to highlight one of the many great conversations from the show. We were chatting with a brand that designs and deploys its products for mid-tier consumers throughout the African continent. While they knew quite a bit about their target consumer persona (age ranges, ethnicity variance, disposable income, desired color palettes, etc.), they knew very little, if anything, about the body dimensions of their customers. They relied heavily on their factories, who predominantly relied on country / continent wide standards. While this is very common, unfortunately it’s very wrong as countries don’t generally purchase your products… consumers do, and those consumers generally share similar personas. While this brand was not ready to embrace 3D design just yet, their profitability and lifetime value of the customer was significantly lower than expectations due to poor sizing strategies. They knew they needed a change but didn’t quite know where to turn. The old ways were not working!

Poor Sizing Results in Poor Fit

One of the biggest factors in poor fit is a poor strategy around sizing. Most size constructs are built upon tiny amounts of data or outdated sizing studies and therefore, for the vast majority of brands, the clothing you design is not actually designed to fit your target customers. The good news is that this problem can now be solved, but it requires a tremendous amount of data and teams with profound data science skills.

Current Sizing Data Options

Let’s take a look at some of the pools of data available to help with this problem:

  1. CAESAR database, which was captured around 1996 and contains 4,400 measured bodies from 2 continents
  2. Sizing studies from Human Solutions (Avalution). Avalution publishes that they have 13,000 German scans, 18,000 North American scans, and 6,000 Italian scans from their website.
  3. Alvanon acquired rights to the intellifit database in 2006, and further the bodidata database in 2017, which you can review from their website.
  4. BodyBlock AI has exclusive rights to the Fit3D database, which has scanned 1.2 million bodies in the past 3 years from its network of thousands of 3D body scanners deployed in 55 countries. This dataset grows by 2,500 to 3,000 new unique 3D body scans every day as listed on their website. Furthermore this rate doubles each year.

How to Develop a Sizing Strategy

As you evaluate the evolution of your sizing strategy, there are a few things you should keep in mind:

  1. Understand your target consumer

Where do they shop, what is their ethnicity, what is their age, what are their disposable income ranges, are they generally petite / standard / big / tall / etc.? The more you know the better you can develop target audiences to further study.

  1. Evaluate potential vendors

How much data do they have on your target consumer? You will be surprised how data quantities break down as you begin to apply filters. You will not find an anthropometrist or data scientist that wishes they had less data by which to make decisions. Does the vendor capture the body measurements that are important for your product, do they have the ability to match their data with your consumer persona, what tools do they have to develop sizing strategy models, do they work as consultants (i.e. pay for time) or give you access to platforms (i.e. allowing you to be curious as your time allows), can they deploy avatars / 3D models that represent your sizing strategy, can they deploy avatars / 3D models that help you validate fit within a size (i.e. not just the average body, but a variation of bodies that all fit within each size)?

  1. Develop KPIs for the project

As with every project, it is important to understand what success is. For example, if one of your size constructs has a 8% fit coverage, maybe you want to get to 24% (i.e. a 3 times increase in fit coverage).

  1. Develop a budget

It is important that you have commitment to the project from leadership, both to deploy the outcomes, and also to invest appropriately to get the best results. It is important to understand that sizing strategy projects directly impact lifetime value of the customer and returns… so develop appropriate budgets and buy in for this.

  1. Stay curious

A few of the available solutions provide you with platforms to access the underlying data directly. These solutions enable you to continuously study and understand the body dimensions of your target consumer audiences. This type of access helps you understand more than a few primary measurements for sizing strategy but allows you to integrate strategies around sizing throughout your business model. I’ll touch on this in more diligence in another post.

You will notice that the only time I mentioned 3D was to talk about how much data each vendor has. 3D body scanning is simply a way to capture standardized body measurements on the human population. So, while 3D body models / avatars are used in 3D design systems, 3D is not necessarily required to ensure that you have the best sizing strategy for your target consumer.

Let’s Look at the Math – Why Does this Matter?

Now you may ask yourself, why is this so important? Through our research, we find that most brands develop products that only fit 5-10% of their target audience. That’s right folks, 5-10%. This means that 90 – 95% of the people that want to wear your brand cannot find sizes that fit their unique bodies the way the designer intended. No wonder we have a returns problem. It has further been noted that 90% of consumers are likely to purchase more products from your brand if their first fit experience is positive. So, when you size for your audience, you enhance your consumer’s first experience, which increases their satisfaction with your brand, increases their lifetime value, reduces your returns, and enhances your brand’s sustainability quotient.

In conclusion, whether or not you use 3D in your business or have embraced the digital revolution, you should absolutely ensure that your sizing strategies match your target consumers. And if you do use 3D in your business, make sure that the 3D models / avatars that you use actually represent a sizing strategy developed around your consumers, not country or continent populations, or worse yet, a strategy based on a small antiquated dataset.

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Lydia Mageean Lydia Mageean has been part of the WhichPLM team for over six years now. She has a creative and media background, and is responsible for maintaining and updating our website content, liaising with advertisers, working on special projects like the Annual Review, and more.Joining mid-2013 as our Online Editor, she has since become WhichPLM’s Editor. In addition to taking on writing and interviewing responsibilities, Lydia has also become the primary point of contact for news, events, features and other aspects of our ever-growing online content library and tools.