In his latest post for WhichPLM, fit expert Mark Charlton explores what he has coined the ‘Fit-ernet of Things’. Mark believes that, in order to really predict fit preferences, a foundation of connected individual data needs to be collected, analysed and used.
I have a passion for great fitting apparel, and for almost 30 years, I have been helping brands understand sizing constructs and globalize fit offerings. Most of my articles thus far have addressed the complexities of creating, perfecting and executing fit across a diverse and ever changing consumer landscape. And this article is no exception. I would like to continue the discussion of fit and sizing, exploring the topic of individual fit preference
I liken individual fit preference to individual food taste: we all like different foods, different levels of flavour; one person’s ‘too spicy’ is another person’s ‘not spicy enough’; we all have differently sized appetites and both taste and appetite are variable depending on mood, time, season, and so on.
We all use clothing to express our individualism. Part of this individualism is fit preference – simply put, how tight, loose, long or short an item of clothing is. To emphasize the point that fit preference is variable over time, think of an item of clothing that has been a constant over the years, replaced many times but fundamentally the same – a pair of 5 pocket denim jeans for example. I would guess that over the years your 5 pocket denim jeans have gone from loose fit to slim fit to skinny fit, from mid-rise to low-rise to high-rise, from straight leg, to boot cut, to tapered, to skinny and so on. Fundamentally the same pair of 5 pocket denims but subtly different every time you replace these. This is fit preference.
Trend is a key influencer in fit preference. Not only is fit preference individual, but our pace of trend adoption is individual based on many things: age, peer group, societal influences, and even historical influences (we all have that relative or friend who seams to be stuck in a time warp from a style perspective!).
Accurately fitting an individual requires an understanding of their individual preference at that particular point in time.
The industry is, in my opinion, at a junction. One path being the brick and mortar push model, of creating apparel with a built-in fit preference and push this on the consumer. Here’s a T-shirt we designed using our fit standards then created this to fit slim. We call this “slim fit”. Here you are, consumer, hopefully you like it and our preference matches your preference and hopefully your body shape is close to our fit standards and, if not, size up or try on a few different sizes and figure it out.
Another path, more akin to e-commerce, is to create a sizing widget to ask questions to understand an individual’s size, shape and fit preference, and then recommend products based on their answers.
Both options have pros and cons. The pro of the traditional brick and mortar model is that the consumer is in control, they have the choice of all options available and can navigate these options generally via the fitting room. They hopefully aren’t too frustrated by the process or the verbiage used to communicate fit intent (I state this as fit naming and sizing can be a reason not to purchase [search ‘vanity sizing’ if you are not familiar with this term]) and makes a purchase. The con, however, is that no data can be gathered from the journey to purchase. How many products were tried in the fitting room? And in what size? What was the hit ratio (garments tried to garments purchased)? What is the consumer frustration / satisfaction rating?
The pro of the e-com model is that it limits choice; if I answer my preference is ‘loose’, the model will show me loose fitting products. However, limited choice could result in loss of sales.
I have a hypothesis. This is a hypothesis based on the above con of the brick and mortar model, limited to no data collection from the fitting room experience, hence this being a hypothesis versus fact-based conclusion.
My hypothesis is this. Consumers buy products and wear them differently to the designer’s / brand’s fit intent. An example there is that slim fit T-shirt: I like the fabric, the colour, the graphic, neckline, pocket etc. however I just don’t want a slim fit T-shirt. Simple. I would just size up until I reach my desired individual fit preference. The question is just how much apparel is being worn differently than the designers / brands intended? My guess is more than the original intended fit!
Reusing my food taste analogy, one person’s perception of ‘spicy’ versus another person’s perception of ‘spicy’ are quite often different. The same can be said of loose fit: what I deem a loose fit versus what a brand (insert any brand) deems a loose fit could be different. Therefore limiting choice based on a subjective clustering of “loose” product is definitely a con and will result in lost sales.
I say this to reiterate how understanding individual fit preference is extremely important – equally as important as understanding individual body shape, size and proportion. One without the other is an incomplete picture. In order to accurately fit a consumer, one needs to understand the consumer’s individual body shape, size proportion then their individual fit preference.
Body scanning technology is advancing and scaling to ensure brands can create an accurate understanding of an individuals body dimension’s, however, technology still needs to accelerate in understanding and predicting fit preference. One option is to simply match individual body measurements with product purchased to understand how the product is being worn. In the example of the slim fit T-shirt, if my body dimensions match me with a size medium however I purchase said slim fit T-shirt in a size XL clearly I am not wearing this slim.
This information would answer the question of how much apparel is being worn differently than the designers / brands intended?
With this information brands could create more of what the consumer wants and in a way that is easier to navigate.
One could also argue that accurately predicting fit preference would reduce close outs as the purchased size ratio would more accurately match the consumers needs and in turn reduce total cost of ownership.
The challenge is that both body dimensions and fit preferences are variable over time.
The utopian state would be if this could accurately be predicted. If you know my body dimensions and my purchase history, AKA my fit preference linked to my influences, then could you predict how I adopt trend and how I would like my next purchase to fit? Of course the more data that is collected the more accurate the prediction.
Use Netflix as an example: everyone’s “home page” looks different, individualized based on individual viewing history, individual predictions based on individual viewing history, content creation based on what viewers want to see, also variable over time and linked to influences, holidays, societal events, etc.
Apparel fit preference is no different. The question is who is building this foundation of connected individual data – the FoT, “Fit-ernet of things”.