In his first piece for 2020, fit expert Mark Charlton discusses individual fit preference in a consumer-centric era. Understanding and predicting fit preference, Mark believes, is key to solving the problem of apparel fit.
Describing data as ‘the new gold’ is not something I profess to have conceived. It’s a phrase we all know and all understand: the importance of collecting and analyzing data. Yet data is only gold if your company knows how to mine said data into relevant, timely, and actionable consumer insights that solve problems.
I have a passion for great fitting apparel and for over 20 years I have been helping brands fit apparel, understand sizing constructs and globalize fit offerings. In this article I’m going to explore data, it’s role in the sizing and the fitting process, how we can use data to create better fitting garments and also where I believe there is a fundamental gap in the current data collection landscape.
Data collection, its use within product creation and specifically fit and sizing, is (as most things are) a journey – a journey I believe we are still traveling on.
Types of Data
Firstly, let’s take sales data. This data is mature and most brands and retailers can provide history of sales by size and by fit proposition, helping merchants understand if “slim fit” is selling, for example, or if extending a current size construct and adding XXL is required. As an industry we have been using sales data as the compass to chart our growth for decades.
Now onto early returns data, which is also mature data and can be linked to sales data to look for patterns. A crude example would be: all larger sizes in slim fit are returning as they’re “too small“ indicating a somewhat obvious next step: slim fit is not as relevant at the larger end of the sizing spectrum and that perhaps a different grade is required for slim fitting garments, to allow the same style to be offered across the size range with better relevancy. Again as an industry we have been using returns data to guide decisions and/or validate sales trends for some time.
Thirdly, consumer feedback is also used as a data point. This, in my opinion, is a little more subjective and based more on “art” than on the science of raw sales / returns data. Consumer feedback comes in all forms. It’s extremely relevant however harder to directly link and review for patterns. In my experience consumer feedback is generally used to validate any findings from sales / returns.
The challenge with all of the above is that they are reactive: sales data can only be fed back into product creation once the sale has happened; returns data even later; and consumer feedback later still. With long selling periods and long “go to market” processes the feedback becomes old news.
The industry is evolving and selling periods are reducing; no longer do retailers want winter product in August, then no newness until January. Consumers are demanding a much more “buy now wear now” approach / availability leading to more newness throughout the year and shorter selling cycles. Brands and retailers are desperately trying to shorten “go to market” processes to make better use of sales data and apply learnings in a timely manner.
A huge introduction of late has been the vast amount of body data that is now available to brands and retailers. This provides a very detailed picture of your consumer base and the diversity of body shapes and sizes any one brand needs to cater for. The dilemma for a brand or retailer is what to do with this information?
In most cases a brand or retailer can compare actual consumer body shape and size data to its target consumer body data and sizing constructs. However, I guarantee that the outcome is, whilst actual consumers come in all different shapes and sizes, a brand still caters for an average.
I once read an article that drew an analogy to body shape averaging to skin tone averaging! If you looked at the different skin tones that exist across your consumer landscape then created one single average skin tone it wouldn’t look like anyone! I think the same is somewhat true for body shapes.
If you examine all the ingredients of apparel, and specifically the components that effect fit, then where good data exists I believe there is one huge and critical gap!
You can liken fit preference to taste: it’s individual, it varies and evolves over time, it’s situational, it’s cultural, it’s demographical, it’s weather dependent! So if we can’t predict the weather with 100% accuracy, how can we obtain data on fit preference to predict it?
Fit preference, for me, questions all the other data that today we call ‘mature’ and make decisions on. 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 where that product)?
For decades the industry has operated in a “push” model, pushing product on consumers and the reality of what consumers did with the product post purchase was of little to no relevance, as the purchase had already occurred.
Now, 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.
Sure, there are fit finder-type questionnaires on some e-commerce sites that try to understand your individual fit preference to match to product, and that’s a step in the right direction. In my experience and exposure this technology is not closed loop, or self-learning. So, how does this relate back to individual body data, sizing constructs and brand fit intent paired to sales and returns data to roll up into meaning insights? Does the product do what the brand intended or something different and, if different, what should a brand do? Also in my experience and exposure, this technology cannot predict changes in fit preference due to trend or end use.
I would like to see technology evolve to a place where: if you know my location, and there fore the weather and how I am likely to layer / use the product, and you know my purchase history and therefore what’s already in my closet (sizes and fits), and you know my social circle, influences and the pace I am likely to adopt fashion, then why can’t you predict my fit preference? Why can’t you extract my individual body measurements to compare and contrast this against product offering and send the size that is right for situation, body shape, body size and fit preference? I don’t believe this is something one brand can independently; it’s more of an interface / personal operating system. After all, few people are head-to-toe clothed in one brand 100% of the time.
As an industry we need to acknowledge this fact and ask, what’s the bigger advantage: brand fit secret sauce or solving fit agnostic of brand for the consumer?
All the best inventions are ones that intuitively and conveniently solve pain points. Apparel fit and sizing is a pain point – speak to any consumer who wears clothes. Fit preference, understanding and predicting it, I believe, is the missing piece in solving this puzzle. I also believe the data exists in less obvious places, and we just have to learn how to mine it!