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Look as hard as he can, my little dog is never going to find the ball if he is seeking it in the wrong place

The key to solving the fit problem that 'dogs' e-commerce fashion

Dogs do all sorts of things that humans are far too intelligent to do.  For example, I have a little dog that loves to chase after a ball that I throw, running to fetch it back to me, most of the time.  However, if the ball accidentally lands in a prickly bush, he just stares at it soulfully for a couple of seconds, then sets off cheerfully to search for it elsewhere.  He clearly sees where the ball has ended up, but because it's somewhere that he doesn't want to go, his decision about where to look is governed, not by common sense, but by wishful thinking.  That's not something a human would ever do, surely? 

How does this shaggy dog story help to illustrate one of fashion's biggest problems?  E-commerce fashion companies want to send out garments that are correctly sized so as to avoid the main reason for customers to sent them back: poor fit.  The problem is a huge one; returns rates range from some twenty per cent in 'mainstream' sized fashion, up to an eye-watering seventy per cent in the more problematic plus-size sector.  Clearly, this rate is unsustainable.  There are millions – possibly billions – of dollars ultimately to be saved (and made) in dealing with the issue of finding a reliable way to make sure apparel fits e-commerce customers.

One way of preventing all these returns is with fit tools.  Some e-tailers rely on the time-honoured system of offering customers a 'size chart' of clothing measurements with which, should he or she have access to a measuring tape, a customer can compare his or her body metrics.  Clearly, this method, which actually employs nineteenth Century technology (and which bristles with all sorts of problems), does not do the job very effectively.  Elsewhere, e-commerce has adopted more up-to-date tech of varying degrees of sophistication (but none with perfect success), and all eyes are now on the IT industry to see if they can come up with a solution that will carry all before it.

There is a varied field of fit innovations jostling for dominance.  Some rely on scanning or clever mobile phone camera developments, whilst others are still based on consumers being asked to input various body measurements or sizes.  The tech business appears to be doing its best to find the remedy for badly fitting apparel – by looking in the places that it wants to look.  As befits the activity of very clever technically minded people, the emphasis is being laid firmly on developing a lot of very clever technology.  Thus IT will – if it continues to develop at the rate it is going – be extremely effective in establishing a good fit between the spec for a piece of apparel on the one hand, and body data from the customer on the other.

Hereby lies the nub of the problem: data.  At present, some, but not yet all, manufacturers supply the comprehensive level of garment information necessary for these fit tools to feed on.  Some businesses feel that they don't really need to go to the bother of providing the spec, and worse, some act as if their garments' measurements, grading, construction and fabric details should be some kind of industrial secret.  However, these out-dated attitudes will soon be swept away.  In a very short time brands that expect their apparel to be sold online will automatically produce data packs that will enable their product to do just that.   The tech developers will then swoop down on this kind of information, as it tends to be clean, accurate and clear.

But how do we provide the other half of the equation: the information from consumers?
  Will this be clean, accurate and clear?  Every fit tech system relies on accurate customer metrics, be they measurements, scans, and/or stated or unconscious preferences (and repeatedly re-obtaining them, as measurements change on a regular basis during a customer's lifetime, whilst preferences can change over the course of a trend).   Surely, it is therefore to be expected that, first and foremost, all the tools being developed are focused on obtaining customer cooperation, motivating their actions and gaining their trust, as well as the biggest issue of all: reflecting their will. 

Customers (also known as human beings) can be difficult, apparently illogical, contrary, seemingly unpredictable, variable, and wilful.  They have every right to be any or all of these things, and there is no evidence to suggest that they are going to change just because they wish to buy a shirt, regardless of how well fitting it is (or how lovely the print). 

Obtaining their data in a predictable form promises to be a rather prickly undertaking. Many of those who are presently tasked with developing the tech to serve these people (because the ultimate client will not be the retailer, but the consumer), are relying on some somewhat shaky assumptions.

Take, for example, those who in the UK and US make up about half of all womenswear consumers: plus size women.  It is often taken for granted that this cohort, due to their severe fit problem, will be only too happy to provide all sorts of information.  The majority of fit tools ask for height, weight, bust (or bra size), waist and hip measurements, among other metrics.  But there is no evidence that this cohort finds it anywhere near as easy to provide these figures as those who design fit tools assume.

Many larger people, living, as they do, in a judgemental society that sees 'overweight' almost as the worst sin, are extremely sensitive about their bodies. They are often unwilling to go through the process of measuring themselves, do not possess the equipment to do so (many bigger people do not own a weighing machine, for example), dislike knowing their metrics (and avoid doing so at all costs), hate reporting them, get disheartened when they change 'detrimentally', and are very worried about having their measurements accidentally revealed in some way. 

So it is likely that the majority of larger people will avoid situations where their measurements can be taken, and, when they have do have access to their data, will immediately contaminate it.  The idea that every plus-size woman will happily go through a thorough physical revelatory experience (even in the privacy of her own home) in order to obtain better fitting apparel is an exercise in wishful thinking – and one not based on any study I have seen.

With the billions of people on the planet, it is all too easy to undertake an online survey of plus-size women and find many who are happy to supply their measurements.  Some of these will be perfectly accurate – and will be supplied by an assiduously self-selected group of un-selfconscious women.  Other measurements gained the same way will be inaccurate due to the contamination process outlined above: however, in the midst of the Internet, it is very difficult to understand which data is correct, and which is corrupted.

Nor can it be automatically assumed that the scanning tech as it exists today will fare any better: such devices can trigger all the sensitivity to self-revelation that exists with a measuring tape – occasionally more.  Another assumption – that the consumer's emotions will change to adapt to this new system – has got a lot more going for it.  Based on past evidence, consumer behaviour alters all the time, and each generation has its own attitudes.  However, predicting that the next generation will grow-up devoid of sensitivity about their bodies (and, even less likely, predicting that those who are already in the customer cohort will suddenly change) is quite a stretch, and based on no available evidence.

In order to understand each technology's exposure to the problem at hand, every fit tool should have self-monitoring element, carefully picking up data as to whether consumers are providing correct or incorrect information, if they are being deterred by questions as to their size, and the chances of whether they will accept the tool's findings or not.  And every tech specialist working in this field should be diligently concentrating on improving the vital subject that has such a profound effect on the efficacy of their tool: that of customer participation.

It is important not to spend time and resources developing tech that requires consistent data from a consumer who is simply not prepared to provide it with any degree of accuracy. The perfect fit tool, not only for the plus-size woman, but also for all fashion consumers, would be non-revelatory, unconscious continuous monitoring of body data.  The tech, working with the consumer's full knowledge and permission (but with only passive participation and minimal personal input with no revelatory feedback) needs to absorb the consumer's needs without intruding on his or her sensibility. 

It is rewarding to use expertise to chase down complicated and clever solutions; to produce feats of technical virtuosity.  However, it is always best to be realistic from the start, and, if ultimate success can only be hoped for by looking into more prickly, difficult, unsexy and unpredictable areas – to step well out of one's comfort zone – then this is the course of action that should be taken. The tech industry is going to have to pause, take time to look at what the consumer is prepared to do, and reverse-engineer all their technology to utilise what they will actually have to work with.  They may find they have to develop a different approach altogether.

Look as hard as he can, my little dog is never going to find the ball if he is seeking it in the wrong place: he's going to be disappointed, and no amount of wishful thinking is going to alter that.

One consumer will want her clothing as snug as a second skin; another will want apparel that flows loosely over her body

Preferred fit: the science of profitability

In an industry where many consumers are buying their apparel without the ability to try it on first, the capacity to remotely find a perfect fit becomes a key fashion business function.  And where each customer's fit is governed, not only by his or her measurements, but also by their preferred fit, understanding the elements that predict this preference is vital.

'Bad fit' is the number one reason cited in the biggest problem facing e-commerce fashion today: that of garment returns.  Those companies that are first to successfully get to grips with this issue are going to be transformed in many ways, both predictable and unforeseeable, but all of them beneficial.

Returns are unremittingly expensive and wasteful, so if finding a magic bullet to solve the fit problem were easy, it would already have been done.  The issues of fit are about as complex and contradictory as it gets.  This post is not about physical fit (I have – and will – cover this in other pieces): it deals mainly with the more slippery, yet still important, subject of customer preference.  It's one thing to measure a human body and decide the apparel that is going to fit it: it's quite another when we attempt to supply a person with a piece of clothing that they feel truly happy with.  It is only by perfecting both aspects that we will strike gold.

If fashion were not an extremely competitive industry, and if garment returns were not an expensive millstone around its neck, then perhaps we could afford to ignore preferred fit (although there are arguments aplenty against ecological waste and customer disappointment caused by inappropriately sized and graded apparel).  But I would ask every CEO in fashion the same question: if your competitor is going toe-to-toe with you with everything else, would you feel happy for them to be better equipped to deal with preferred fit than your company is? 

I would also ask the tech companies: are you doing enough to make sure that its you, rather than your competitors, that produce the game-changing, 'must-have tech' for fashion, one of the world's major industries?

What is preferred fit?  If you were to take ten customers, and examine the way they like to wear their clothes, at first glance you might think that they are all over the place.

One consumer will want her clothing as snug as a second skin.  Another will want her apparel to flow loosely over her body. One woman will insist that her sleeves be long enough to cover the base of her thumb, yet the next person wants them to expose the bones of her wrists.  Confusingly, the same woman will sometimes have different preferences: her favourite 'comfy' boyfriend jeans may serve a completely different aesthetic than her skin tight ones.  These partialities spread out in all directions, encompassing every area of each garment.  If your aim is to find your customer's preferences, you have got your work cut out.

It is surprising that many companies still do not adequately address the subject of fit, and few even approach the complexities of preferred fit.  If addressed at all, the current approach is often to just to ask a few cursory questions.  Preferences, however, are often subtle and innate, and can be far more complex than immediately obvious.  There are reasons why some consumers cannot say, will not say, or do not even know what to say about them.  The nature of the beast is that we are often talking about personal norms: something that the consumer believes to be a non-issue, so asking predicates the kind of self-awareness or product knowledge that the average consumer doesn't always have.

Then, also, there are reasons why questions are not always practical.  In order to know enough – or anything meaningful, actually – we may have to ask a lot, and we have to have some expertise (and subtlety) as to what to ask.  We have to incentivise the customer to take the time and effort to answer all these questions (preferably honestly), and keep on answering them... because our bodies and preferences change all the time.  There really has to be a better way, because what we need to do is to replicate (as near as possible) the 'trying on' experience.

While looking minutely at individuals will not be the most effective use of our resources, seeking patterns on a grander scale is going to be much more rewarding. Observing the population is like looking at a pointillist painting: standing too close and staring at the individual dots isn't going to get you anywhere.  It's necessary to back off and look at the big picture, because the more dots you can see, the better sense it all makes.  

Let's start at one small part of the picture.  Why would one person want her sleeves to fall so much further down her arms than another?  Do they have anything in common with other people who like the same thing?  

We are often talking about optical illusions.  If your sleeve is above the bones in your wrist, it has a tendency to make your arms look longer.  If they are halfway over your hands, they look much shorter.  As human beings, we tend to want to converge into the middle of the pack.  Indeed, the nearer to the norm we are, the greater is our perceived beauty.  Without realising it, if we are shorter (with short arms), we want to look more 'regular', and many petite women would unconsciously feel alienated if their sleeves draped over their hands, emphasising their smaller stature: infantilising them visually. 

Conversely, a tall person with long arms may start to feel freakish if her upper limbs are seen to overly protrude out of her sleeves, emphasising their divergence from the norm.  Of course, there are 'preferred abnormalities', such as a model's enhanced height or low body fat, but these must be emphasised as 'elite', 'aspirational' and 'intended', and thus well catered to.

This is just one glimpse at the engine behind preferred fit – and there is much else to know.  I've had 30 years of experience of fitting and measuring thousands of fashion consumers, meaning that the pointillist dots started to coalesce a long time ago. 

'All dogs have four legs.  This table has four legs.  Therefore this table is a dog.'
The list of aspects affecting consumer preferences is, on the face of it, pretty banal: age, size, height, body shape, personality type, garment function and/or style, cost...  Yet we have to be subtle and knowledgeable about each of these issues, which are, in fact, very complex.  We cannot lump inappropriate groups together.  And we have to be respectful: we need to avoid using stereotyping and patronisation.  We are predicting, not dictating, preferences; discovering, investigating and learning all the time.

When it comes to 'predictive fit preferences', such is the paucity of our knowledge, we are in the Stone Age.  To mix metaphors, it's a chicken-and-egg situation where we cannot know if it works until we start to use it, and many companies may not want to sink their resources into it until it has been proven.  Yet now, with the ability to gather customer data on a huge scale, we actually have an opportunity to build something very exciting.

We need to be swift to use customer experts, who already know a huge amount about those mysterious consumer preferences, and team them up with thought leaders, statisticians, tech developers, scientists, garment technicians, PRs and other influencers – all manner of differing disciplines – and get stuck in.  It is the correctly targeted use of science – combining human ingenuity, experience and curiosity – that is going to allow us to fully interpret what big data can tell us about our population.  This is not a job that is going to do itself.

There is one other aspect to this situation that, like the oft-mentioned elephant in the corner, looms silently and patiently over the proceedings.  If we do manage to identify customer preferences, do we actually manufacture the clothing necessary in all those diverse fits?  Anyone with an understanding of apparel cutting will know from what I have written that addressing these preferences is likely to widen the envelope of fits, especially towards the more outlying body types.  Those people who already have long arms are going to want to have their sleeves even longer.  The difference between the short and long sleeve length just got extended.  Spread this out across every part of every garment and you get an idea of the issues in question.

Lack of information was the reason why so far we've been having such a problem with creating fashion that fits our customers well enough for them to want to keep it; if we do not know what grading and sizes we actually need, we are unlikely to make the correct ones just coincidentally.  Although it will never be possible for big fashion to suit every preference to perfection, there is enormous opportunity here to create apparel that is far better fitted to purpose.

In 50 years' time, fashion historians will look back on the next decade as a kind of 'mass extinction event' and remark that the comet that has hit Planet Fashion has been e-commerce.  Those companies ill-prepared to develop the correct response to consumer fit are the ones who are going to turn out to be the dinosaurs.


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