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When it comes to fit, the most challenging sector of fashion ecommerce is plus-size womenswear...

Fashion's imaginary consumers

The fashion industry risks creating an inventory – and a set of fit tools – designed for a largely imaginary consumer base.  If this sounds unlikely, it’s worth remembering that it is something that has happened before.

Fashion e-commerce is suffering from an unsustainable returns habit that damages profitability, ruins customer loyalty, upsets shoppers, is wasteful and extremely damaging to the environment.  The majority of these returns are reported to be due to ‘fit issues’.  It’s a no-brainer that the industry needs to find a way to send out clothing that can be relied upon to fit its consumers.



One flank of the battle is for the fashion industry to ensure that the apparel being manufactured is ‘fit’ for purpose.  To do so, it’s necessary to understand exactly what clothing sizing and gradings should be produced in order to reflect society and satisfy demand.  This will entail a study of one of the most complex entities in the universe: the human consumer, both body and brain.  Due to the considerable variability of the population, it is going to result in a much broader range of sizing offers than has been produced up to this point.

The battle’s other flank involves tech companies developing the tools that solve the myriad technical issues involved in targeting customers with suitably sized and graded garments, a task made more difficult when involving the more comprehensive range of apparel that will be on offer.  For this to be achieved, it’s necessary not only to match the level of population metrics expertise of their fashion colleagues, but also to acquire their thorough technical knowledge about all garments being retailed – the matrix of measurements, fabric characteristics, relevant construction specs, usage information and designer preferences. 

When it comes to fit, the most challenging sector of fashion e-commerce is plus-size womenswear, which is what I will be addressing in this post.  Here, the rates of return can be swingeing: much higher even than in ‘mainstream’ fashion.

The legacy of plus-size fashion’s sizing (and the root cause of its inflated fit problem) is that the grading has been ‘extrapolated up’ from 'mainstream' sized women, where historically, sizing research tended to originate.  The idea that curve women are simply larger versions of smaller women may be true to a certain extent, but this is far too reductive: these larger consumers have much more exaggerated body shapes than their smaller equivalents, so, where they have been graded on a false premise of conformity, it's all too easy for garments to completely miss the essential fit points.  It would actually be more informative to create a range of diverse cuts based on the physiques of larger women, and shrink these down to their smaller counterparts, who would, in all likelihood, be delighted with the subsequent advances in the fit of their garments.

Historically, the plus cohort has been underserved by the clothing industry, of which this lack of specific research is an example.   It was long assumed that larger people are not as valuable to fashion commerce as their ‘mainstream’ counterparts, partly as they have traditionally averaged a lower spend, but also because their association with a brand was considered negatively.  Putting it bluntly, many companies did not like the aesthetics associated with larger people. 

With the growth in the proportion of fuller sized individuals in the population, both these considerations are fading away: the younger generation no longer balks at seeing brand ambassadors who vary from the traditional models’ slender bodyshape, whilst it has come more widely accepted that any shortfall in the spend associated with plus-size women is caused primarily by the poorer offer available to them – which actually represents an opportunity for forward-looking companies. 

Now that these reservations are being removed, in order to develop this sector to its full potential, the industry will need to reduce fashion returns significantly, necessitating a specific and comprehensive study of female bodies – from the smallest to the largest – in the kind of depth that has never been achieved or attempted before.

There are two general methods of collecting consumer body metrics: those undertaken by professionals, and consumers' self-reporting.  Some enquiries have involved experts who have reached out into the population to weigh, measure, scan, take surveys and live-test volunteers.  In others, subjects have been asked to either measure themselves, fill out surveys – or allow their bodies to be scanned in some way.  We already know that far fewer plus-size women are willing to participate in such studies, yet we are relying on this work because we will not gain a complete understanding of the customer base without it.  Is there a particular group of larger women that is more likely to step forward to provide data?  Is this going to have an effect on the quality of the information collected?

When designing anything for larger people (be it tech or clothing), it is advisable to think about people holistically, and consider, not just their physiques, but also their preferences, personalities, emotions, experiences and thinking.  This is particularly relevant to the prickly subject of how to go about finding a realistic plus-size sample of the population to study.  Many larger people (with good reason) are resistant to having their bodies categorised, scanned, analysed, measured or observed. Putting aside the differences brought about by the vagaries of personality types (which varies across all women of every size and body shape), there are particular reasons why the body confidence of certain groups of larger women is more resilient than others. 

A clue can be seen when observing the shapes of larger women who are happy to exhibit themselves, and compare them to the rest of the population (who largely don’t).  ‘Curve’ fashion models are women who make a living out of the fact that they (and society) find their bodies aesthetically pleasing, and these women usually tend to have certain features in common.  They are young, with a balanced physique, tall with smooth lines, and usually have either ‘perfectly proportioned’ or ‘hourglass’ body shapes.  They usually tend to share a European physical type.  These women are not at all representative of the plus-size population as a whole.

Larger women come in a range of highly distinct body shapes, the rarest types being the 'perfectly proportioned' and 'hourglass'.  This should come as no surprise to anyone: we don't really expect models to represent an ‘average’ woman.  What may come as a shock to the uninitiated is how resistant most plus-size women (who do not share this ‘aspirational’ shape: indeed, they vary from it considerably) are to being accurately measured.  When calling on the population to volunteer body metrics, it is necessary to be extremely careful not to end-up with a highly self-selected, un-representational sample.

It could be that a survey of plus-size consumers finds that 90% of them have hourglass or well-proportioned body shapes, when in fact, only 10% of the general population shares this profile. 

If this happens, the industry will be creating a fashion inventory – and a set of fit tools – for a largely imaginary consumer base.  Leaving it in exactly the same highly unsatisfactory situation as it is now, in fact.

There is evidence, possibly because they vary from the ideal to a more exaggerated degree than their smaller equivalents, that those who are not ‘conventionally attractive’ or 'balanced', physically, are far less likely to come forward to be tested by a professional, nor can they be expected to enter correct measurements into any fit system, even in the privacy of their own homes.  They may be slow to volunteer to be scanned, and extremely reluctant to want to know their own metrics.  They are likely to be very concerned about privacy, and many of them will not even possess the tools (a tape measure, or a weighing machine) with which to gather their data, choosing to enter invented measurements if pushed.

If developers are not careful, this is a situation that may be carried forward into the new generation of e-commerce retail fit tools and scanning devices, causing a diminution of effectiveness.

It’s not all doom and gloom, however.  Forewarned is forearmed, and, with anticipation of these issues, strategies can – and will – be put in place to collect an accurate, representative sample of fashion consumers, and the development of effective fit tools.  In order to do this, it is necessary to abandon the wishful thinking, the 'common sense' (that is not backed-up by empirical knowledge), the prejudice, myths, or the incorrect extrapolation that has plagued the plus-size fashion sector for too long.

Most of all, we should see the end to imaginary plus-size, standardised female consumers, and replace them with the rich diversity of real women.

We can only solve the issue of apparel fit by rising above simply thinking of it as returns problem

Apparel fit and inclusivity

With consumer apparel purchasing increasingly moving online, the subject of apparel fit is at the heart of fashion e-commerce.  In this article, four industry insiders come together to merge their differing viewpoints: 

Mark Chalton:

‘Diversity inclusion’ is a term used frequently by corporations intending to ensure everyone has a voice and that there is equal representation of gender, race, religion and other human variations. Equally important is diversity of thought.



So how does this concept relate to the fit of apparel?

Each week brings fresh potential technical solutions to bear on the current apparel fit problem.  This is a Good Thing, as the tech geniuses are recognising fit as an area where technology can offer a significant contribution.

It’s our opinion that most of these advances are instigated and developed within the somewhat rarefied environment of the tech industry – employing one very specific way of thinking.  We note – not as a criticism, but as an observation – that there is an opportunity to redress any imbalance of reasoning by introducing some art into the science.

This observation is not a novel one: for example, it is supported in principle by The Medici Effect (Harvard Business School Press, 2004), which explores why the most powerful innovation happens at the intersection where ideas and concepts from diverse industries and disciplines collide.

Apparel fit is part art/emotion and part science/tech
Think about the last time you purchased a garment that fitted amazingly... how did it make you feel?  Apparel fit speaks to, and stimulates, the senses.  It creates an emotional connection greater than the sum of its parts: much more than mere body dimensions and garment measurements.

So what’s raising the age-old problem of apparel fit among the tech solutionists?

E-commerce apparel return rates are eroding brands’ and retailers’ margins and profitability.  As e-commerce continues to grow, this erosion can no longer be sustained... or masked.

But as a consumer, what do I care?  If I don’t know what size I am, know for certain that I will like a certain product, or that it will suit me, I have the option to order it anyway – perhaps in multiple sizes – hoping to figure out for myself whether it will work.

We all know that so-called ‘free shipping’ and ‘free returns’ are, of course, nothing of the kind.  It’s these delivery costs, coupled with the task of processing returned products back into inventory, and attempting to balance stocks when over half of demand is returned, that are causing margin erosion and higher prices to the consumer.

Reasons for high returns
Apparel e-commerce return rates on average hover around the 50% mark –  70% of which are attributed to poor fit.  It’s a cliché, but for such a tiny word, ‘fit’ is a very complex process!

To put it in a nutshell, ‘fit’ is where individual consumers’ body measurements meet brands’ sizing and garment specifications; designers’ fit ideas meet consumers’ fit preferences; real-life material properties meet consumers’ fabric expectations; and designers’ styling decisions meet the pace at which consumers are willing to adopt trends. 

Emma Hayes:

Many of us are aware that in future we will be able to take 3D scans of ourselves from our mobile phones or similar devices.  These will generate accurate avatars of our bodies, complete with all our measurements, upon which we will be able to virtually ‘try on’ potential purchases – checking our images on-screen in three dimensions for how good the fit is, and whether the style suits us.

At the time of writing, all over the world, many apps, devices and methods are being developed that are advancing rapidly towards this dream.  For example, there is an app on which you can see a three-dimensional avatar of your body – complete with measurements – after simply taking front and side view photographs on your phone.  Another app allows you to upload pictures, and your virtual-reality self will then try on the clothing of your choice – draping naturalistically.  There is a clever hand-held device that takes your measurements by scanning you.  There are even smart body suits and scanning pods, which offer the promise of the gold standard of human measurement: a perfectly accurate rendition of your entire body in three dimensions. These all exist today at various levels of development.

Such devices are exciting and headline-grabbing, but it’s unlikely that most of the companies selling us apparel online will opt for them quite yet – partly for technical reasons, but also because they need to be integrated into the systems currently employed in the fashion industry.  In the early stages, retail companies will need to ‘grow out’ their operation to merge with the technology – and many changes will be required.

Fit tools are clever online algorithms that work out which sizes of apparel need to be ordered, based on ‘inputs’ – and it is these tools that are making the big inroads right now.  Inputs are various pieces of customer information – weight, height, age, perhaps body measurements, ordering/returns history, and body shape – which the consumer loads into the tool.  In the near future these will also include personal preferences. A vital ingredient of these tools is profound apparel knowledge, allowing them to match the consumer with the optimum garment.

Even at this early stage, this tech is proving to be effective – the best tools boasting a considerable reduction in the number of product returns.  They also have the advantage that they are already doing a lot of the heavy lifting required for the digital transformation of the fashion industry.  This is what is building the infrastructure that will plug into all the extra data that’s collected.

The human angle
However, like all new technologies there are going to be issues surrounding adoption by the public.  Predictably, the tech people may think that the problems are all centred on the technology, but there are considerable social, psychological and emotional difficulties to overcome.  As consumers, we have to learn how to travel around this new technology.

Whatever tools we use, we are asked to take some time gathering – and inputting – information.  But there are problems with asking people to do this, and they fall into two categories... 

The measurement problem
Studies show that our measurements are in a state of constant flux, so measuring will not be a one-off activity.  We are being asked to continually monitor our measurements and weight – possibly on a monthly basis – regardless of whether we use a tape measure or scanning device.

There are confidentiality issues to think about. If we are not going to have to keep repeating ourselves with every company we buy from, we will have to develop methods whereby our information can be shared between various organisations.

Our experience is that people only substantially change their behaviour and attitudes when there is something in it for them, and that something often has to be more important to them than a new pair of jeans – even if they fit beautifully.

The phrase 'conform to new habits' fills consumer experts with a mixture of dread and concern.  Can we consumers really be expected to be 'educated' into new habits?  In our leisure time (and shopping is supposed to be that) most of us want to undertake enjoyable activities with an instant reward, rather than toiling through worthy chores in the hope that something better will come along later. 

We need to create usable, enjoyable tech that will draw everyone in from the inception; ideally, fun tech that we don’t even notice we are using.

The revelation problem
The second problem is revelation.  Many people don’t know, don’t want to know, don’t believe and/or would never tell you their accurate measurements. 

We need tech that is 'unconscious': having given our permission for the data to be collected, we should have the right not to have to have any interface with our body metrics unless we choose to do so. 

Jessica Couch:

The future of fit technology
Fit is becoming a buzzword and everyone has an answer to the online returns problem, but the best solutions have two qualities:

1.   Ease of use – How simple and convenient the solution is: mobile phones vs. specialist devices for example?
2.   Ease of integration – How easy it is for brands to integrate the technology into their current systems?

The best technologies do not try to train users to have habits that are not simple or natural.  They allow end-users easily to add technology into their everyday lives. Accuracy is key, and the less effort required the better.

Neither do the best technologies try to do everything.  Instead they connect to existing technologies and enhance outcomes.

Many smaller brands find it difficult to integrate fit technology because their current 'solutions' are unable to connect to other solutions, and buying an entire suite of IT products is not an affordable option.

Expensive, rigid technologies are out.  The best technologies are those which integrate easily with existing platforms and create more efficiency.  Because tech has not existed in fashion in the past, many departments are siloed and are not properly integrated for it.  Great technology companies have to take this fact into consideration before they can succeed.

How fit is your competitive advantage?
Fit and fit technology are customer experience tools – A lot of brands believe that implementing more lenient return policies can somehow impact the quantity of returns.  In our view this is similar to putting a Band-Aid® on a gash... it simply doesn’t treat the real issue of customer expectation. 

According to an article on online apparel returns myths:
  • Most returns are made by one-time buyers.
  • Good returns policies do not affect sales.
  • Most shoppers don't think about returns before buying.
  • Most people are not concerned with free return shipping.
  • Bad returns policies don't affect sales, and a returns policy won't impact  future sales.

By the time a customer has had to return an item, you have lost them for future opportunities.  Customer expectations must be met and returns avoided. This can be done through building confidence with consumers, whether in-store or online, and helping them understand what to expect in regard to fit.

Fit and fit technology are loss management tools – Implementing fit technology helps to increase consumer confidence in products. $62.4 billion worth of apparel and footwear is returned every year due to incorrect fit. That works out to about 57% of footwear and 64% of apparel purchases, according to a recent Footwear News study.  The same study found that if fit were not a concern, 51% of respondents would purchase footwear more often, both online and in-store, while 58% would purchase clothing more frequently.

Excellent communication around fit is important because it helps build confidence with the shopper – increasing sales and generating fewer returns.  Implementing fit technology tools that create directive shopping experiences and manage expectations can help to reduce the amount of unsold inventory.

Fit can help reduce fashion’s carbon footprint – A recent op-ed piece published in The Business of Fashion revealed that dead inventory (unsold clothing) costs the US retail industry $50 billion a year.  Although brands may be able to absorb some of these costs through write-offs on the balance sheet, the environment (through landfills, toxin pollution, etc.) cannot.

Newsweek published an article stating that Americans alone produced 15.1 million tons of textile waste in 2013 and around 85% of that ended-up in landfill, according to the Environmental Protection Agency.

Fit technology allows brands to create better-fitting clothing for shoppers, and helps to match them to their products – so clothing is not created unnecessarily, quickly ending up in landfill.  Although changing the shopping habits of consumers is a difficult task, brands have to take more responsibility for their impact on the environment.  Implementing fit technology can help to fix fashion’s misaligned supply and demand issues.

Fit is inclusive: more people shopping equals more money – In a survey conducted by Fung Global Research, some 72% of respondents did not believe that fashion designers create their designs with the average American woman in mind.

Approximately 78% of people would be willing to spend more money on clothing if more designers offered plus-size options.  Some 68% are interested in participating in fashion trends, but 67% feel that there are not as many fashionable clothing options available in their size as they would like. This isn't just a plus-size issue.

According to a Business Insider report on petite people, over 70 million US women fall into the 'special' size category, and 50% of the population is under 5' 4" tall, but brands' size offerings do not reflect this.  In addition to these categories, there are also tall women, big and tall men, petite men, and people with physical handicaps that are also opportunities for brands to target.

Richard Irons:

Fit tool desired output
When thinking about creating a fit tool, firstly it’s necessary to think about what is needed from that tool.  For instance, whilst producing a custom-made dress, a pattern with all the correct measurements will be required from the outset. 

However, in this piece we’re not talking about bespoke garments, but clothes that are already manufactured, and are available in a finite number of sizes.

Best size
When shopping in a store for clothes, most consumers who are not sure what size to pick opt to try them on – and when a size doesn’t fit correctly they may examine different sizes until either finding a good fit, or deciding that none of the available options is suitable.  It’s this process that we want to duplicate in a fit tool – essentially the algorithm 'tries on' every available size on a body, selects the best size for that body, or concludes that none of the sizes are any good. 

So really what is being asked from a tool is 'best size, if any'.

Ideal garment measurements
In future, if manufacturing processes change so that fit plays a greater part, we may want the tool to provide us with a list of 'ideal' measurements for a garment.  This could, for example, be used as input into some sort of electronic manufacturing system that makes every garment to order. 

But perhaps this is jumping ahead.

Required inputs
In order to get the best results from a tool it needs consumer information to work with. To return to the analogy of trying clothes on in a shop, there are two things involved: a body and a garment. A tool needs information about both.

Clearly, a fit tool needs the body-in-question’s measurements, and the most obvious way of obtaining them would simply be to measure with a tape, the way a tailor would. This is actually the best way to get accurate metrics, if it were a professional who was undertaking the measuring. However, for a customer at home, it’s not a great system. Firstly, the subject needs to possess a tape measure, and secondly, they need to be willing to stop in the middle of shopping in order to take measurements.

These issues are problematical in themselves, but worse, the majority of people don’t know the correct measuring method, so will ultimately supply inaccurate metrics. And if the data is inaccurate, there’s no way the tool can give a good result.

AI method – 'pertinent questions'
An easier and more reliable way to get the information needed is to ask the customer some pertinent questions – age, weight, height – simple information that people already know about themselves. Once it has this information, the tool can use a neural net, armed with a great deal of knowledge that has been previously collected, to deduce that user’s measurements surprisingly well. This method is usually significantly more accurate than asking consumers to measure themselves.

Garment info
The information that is required about a garment is a little more complicated. It’s not enough to simply know the physical dimensions (although these are necessary), since other considerations, such as how closely the garment is meant to fit at certain points, and how stretchy the material is, must be taken into account.

The easiest place to get this information is from the manufacturer. All the details about the apparel’s dimensions, the fabric’s 'handle', and the design’s 'preferred fit', are known to them, because this information is needed for the manufacture of the garment.  However, sometimes the retailer doesn’t have a direct relationship with the manufacturer and won’t have access to that information.

Without these details, it’s necessary to use one of a number of methods. The most accurate would be for a garment technologist to acquire the apparel in each size and undertake accurate measurements, using their expertise, along with product photography to judge the preferred fit.  However, with a large number of products, this approach becomes prohibitively expensive. Other available methods include generic size charts, information from similar garments, and artificial-intelligence inference from product descriptions and photography.

Ideal future
Manufacturers who want to make sure that an accurate fit could be calculated for all their products would be best advised to make all the measurements and design information easily and freely accessible. 

If this became an industry norm, customers would find obtaining a good fit much easier, and the level of expectation and competition would ultimately cause manufacturers to raise their game with regards to fit.

Checks
To make sure a tool is reliable, developers need to check that the results make sense. There are certain ways to do this.

One simple method is for a specialist to test tools by entering lots of different measurements and then see if the recommended size 'looks right'.  Of course, this method can be subjective and inaccurate, as, for example, it depends on the manufacturer’s idea of 'size 10' broadly agreeing with the technologist’s.

More accurate testing can be done, albeit more expensively, by buying garments in the recommended sizes for many people of different shapes and sizes, and judging the fit when trying them on. Information from this process can then be fed back into the tool to improve its accuracy.

In conclusion... Mark Charlton:
The diversity that exists across the human race meshes with the complexity of each fashion brand's design aims, layered to the multiplicity of fabric properties and fit preferences, both of designers and consumers. These issues create a mind-bogglingly intricate problem of achieving the perfect fit.

But this is only part of the challenge: for example, optimal fit can also differ across POMs (points of measurement). An instance of this would be where stretch jeans would require greater elasticity in some areas than in others, so that there is flexibility on the hips, but a snug fit on the waist: a combination of variable body shape, but also of preference.

No individual company, however great their resources, can solve the fit question in isolation: one brand can hope (at best) to supply a solution for their own apparel – which only represents a fraction of their consumer’s overall fit needs. 

We need the vision to collaborate with fit solutions across the entire fashion industry, whilst still competing in this space.  A necessary step towards this is to understand that we must solve the issue of apparel fit by rising above simply thinking of it as returns problem.  It is far more important than that.


 

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