Fashion Personalisation, Easier Said Than Done — SwiftERM

Personalisation has become the “must-have” tool for ecommerce marketing in recent years, and with fashion personalisation more so. According to research by Epsilon and GBH, 80% of online shoppers are more likely to make a purchase if retailers offer personalisation. This could be in the form of a product recommendation, or special email offers with curated content.

A big advantage of personalisation is that it helps shoppers navigate overwhelming ecommerce offerings and get to the purchase decision faster. Who wouldn’t prefer to go to an online shop and immediately find exactly what they need, instead of spending hours on browsing or doing research?

But it is not just about making quicker decisions. It can be critical for a business to help shoppers overcome “ analysis paralysis”, a phenomenon where too many options leave one unable to make any decision. Accenture found a worrying example of this in their research — “nearly 40% of consumers have left a website because they were overwhelmed by too many options”.

Fashion, with its many sizes, collections, and styles, is a lot more complicated and overwhelming to consumers than most other retail categories. Despite an iPhone being infinitely more complex product than a piece of clothing, choosing which model to get is still an easier task than figuring out what shirt will work best with that new pair of pants that you’re buying.

With all these complexities of the industry it is no surprise that conventional approaches to personalisation are falling short in fashion.

One of the fundamentals of any personalisation effort is gathering data. Not just any data, but data that is reflective of target consumers’ habits and can be representative of their future behaviour. It’s also important that there is enough of it to account for statistical deviations and variations.

Creating a data pipeline that enables a company to gather this data regularly and effortlessly is a challenge on its own. One way to approach this is to make the data pipeline a part of your core product and shopping experience. The US and UK based online personal styling service Stitch Fix is doing just that. They create curated fashion experiences where shoppers answer questions about their preferences and expectations before getting matched with a stylist who creates a “fix” (a set of five items) that they think would work for the shopper.

To fuel the technology, they rely on customers sharing useful information from the very first interaction. Shoppers fill out a quiz and tell them about fit, size, style, and budget preferences and any additional information that would be useful for a stylist. This is quite baffling as this information is readily identifiable through predictive personalisation software (PPS), as this technology uses an Ai algorithm to identify everything unique and personal to each and every consumer buy heir own purchases and impressions on your site.

They also share more personal details that can be hard to capture in a regular online shopping experience — for example, whether they have a holiday or special occasion coming up, or are looking for a particular floral dress.

PPS provided SwiftERM uses a unique business model, built in a way that it uses actionable and granular data, not only gathered once but is continuously updated. Every time a shopper makes any action on your platform, more valuable insights are added — for example, the sequence pertaining to purchase actions as opposed to just frequent inspection, and cart abandonment . These insights are carefully recorded and used to better understand what a particular shopper will like next time, and more importantly, used in communications with that individual.

Is the data still relevant for fashion personalisation?

Personalisation technologies are usually fuelled by massive amounts of data about shoppers. The idea is that one needs to have a substantial amount of consumers and data about their behaviour before start predicting what they will like.

At least that is what used to be the norm in the industry. Anna Kuragina, the Product Area Manager for consumer facing artificial intelligence products at H&M Group and she said: “Conventional approaches to personalisation and product recommendations utilise a long history of customer data to predict their future needs and behaviour. Typically, in those approaches the rule is ‘The more we know about our customers from the past, the more accurate the predictions we can make.’”

But simply gathering every piece of data at all times and analysing it uncritically is a naive approach and can lead to its own problems, something that the Swedish fashion giant is well aware of. Anna went on to add: “We live in a rapidly changing and unpredictable world where customer preferences and fashion trends change quickly. We can’t afford being biased by what happened one year ago, often even one month ago.”

This is especially true, when brands operate in a fast fashion cycle, manufacturing new collections many times per year. Data about consumers is also much more likely to decay in the fashion industry than most other industries. To put it simply — one’s body size or style preferences might have changed significantly since the data has been collected. In this high paced environment, staying on your toes and capturing the most recent insights is paramount.

When no two items are the same

Utilising data about products that are the same or somewhat similar is another common approach in personalisation technology. If the same shirt has been bought by hundreds of shoppers, it’s going to be easier to predict how others will buy it. But what do you do when every item you sell is unique? This is exactly one of the challenges that Vinokilo, an online platform for vintage fashion, is dealing with. On their website, there is no variety of possible sizes or colours for an item — only the one individual item listed.

Typically fashion is more standardised and substitution is easier when there are hundreds of products in dozens of collections from thousands of brands. But vintage clothing has a lot of variety without the abundance. It becomes harder to nail down why a shopper liked an item — was it because of the brand, its retro print, or maybe because of a distinct 90’s feel? When you have a sample size of one it becomes extremely hard to make any kind of prediction. It also leaves less data for the algorithm to learn from, because choices like multiple sizes or alternative colours are not there.

This makes creating a satisfying shopping experience more challenging than it is for regular fashion products. If a shopper finds an item that they like, but it is in the wrong size or already sold out, that shopper is left disappointed and with no options other than to move on because there was only ever one of those items for sale.

I talked to Anisah Osman Britton, the Chief Technology Officer at Vinokilo explained that they try to learn from each unique piece of vintage clothing and understand what other items might work for a shopper. For example, by looking at the vintage product as a combination of factors: brand, style, category, quality and others.

The company is currently piloting a feature where their top customers are notified about items that should match their style, before the items even show up online. This is useful because there is always a delay between the time they receive an item in their warehouse and when it appears online. Additionally, because there is only one of each item, matching such items with the right shoppers makes it more likely that they end up where it will be appreciated the most.

This is no easy achievement and doesn’t happen by itself. The company combines algorithms with manual data entry at their warehouses to ensure that they can gather the needed level of granular data about all the items that come through the supply chain.

When algorithms alone can’t do the job

For some industries the challenges of personalisation simply come down to collecting more data and building more accurate algorithms. But in the world of fashion, the algorithms and data alone might not be enough. Figuring out someone’s individual style and perception of what looks good can be a hard nut to crack just by relying on machine intelligence. You have to choose from his calibre solutions, that have established their credibility the marketplace.

One way to approach the challenge is to let algorithms do what they’re good at and leave questions of taste and style out of it.

When the Netflix approach falls short

While collaborative filtering is often fundamental in personalisation technologies, this approach can sometimes fall short for fashion. Anna Kuragina of H&M Group says, “ Compared to other retail players, e.g. grocery retailers with more permanent assortment, the fashion industry is facing an interesting challenge.

How can we use customer data to be relevant while still surprising our customers with items that would speak to them right at this moment?”. H&M Group decided to move away from traditional algorithms such as collaborative filtering and “become creative enough to build custom-made models that will help predict the fashion our customers desire before they even know it themselves.”

The shortcomings of the “Netflix” approach to recommendations also become obvious when dealing with the challenge of size and fit. With sizes differing so much between brands and even within one brand, there are no easy solutions. Easysize have created an AI solution that recommends the right size and fit to online shoppers without using body measurements or size charts.

“It’s important to understand the complexity and the variety of factors that play a role in finding the right size. From the clothing cut and fabrics, to the customer’s preferred style. It is naive to approach the topic as a simple issue with measurements or a categorisation problem. Because products that have the same size or measurements, can have distinctly different styles and therefore fit and feel,” he explained.

For example, instead of simply treating the fabric of the item as a categorical feature, a factor representing “stretchiness/elasticity of a fabric” is used. This property accounts for the changes an item undergoes over time and leads to much better predictions. This is something that experienced stylists and shoppers always take into account, so it’s been incorporated into the size recommendation algorithm as well.

Another important factor is understanding how customers will be wearing an item. The same shirt in the same size can be worn by three different people with different bodies depending on their preferred fit and style. But the same shirt can also be worn by the same customer in three different sizes and ways as a person can provide you with their exact measurements but still prefer to wear some types of clothing looser or tighter than the basic size chart accounts for.” Maybe the shirt is worn on its own for a tighter fit for work, more casually under a sweater or perhaps on top of a t-shirt, like an overshirt. It’s still the same shirt, but there’s three different uses for it.

Where do we go from here?

There is no doubt that personalisation technologies already play and will continue to play a big role in fashion ecommerce, an industry that is, by its nature, very complex. People don’t just buy fashion for utilitarian reasons, but to feel good and look a certain way. This behaviour is often driven by a lot of factors that might not be obvious from the first glance. These variables can be hard for humans to pick up and maybe even harder for an algorithm to discover.

For example, how can an algorithm know what exactly it is customers like about a particular item? Was it the colour, or the fabric, or because they saw a similar dress on Instagram? Can an algorithm detect what accounts for the changes in behaviour when shoppers buy from a flash-sale site where everything is sold at 70% discount compared to when they buy directly from a brand?

How can an algorithm address the constant cold start problem, when there is simply not enough initial data, when fashion constantly has new brands, new items, unique items. PPS maintains a perpetual prior product viewing and buying record, so new lines with corresponding elements (colour, fabric, cut, style etc) are identified and presented unlike any other AI solution.

As an industry, fashion is very close to having the perfect personalisation technology that will not only account for all the requirements a shopper might have but also fully understand what shopper’s perceived style and expectations are. Conventional approaches to personalisation must be modified and adjusted to account for the industry’s unique challenges, there is no better solution than to upgrade to PPS.

Originally published at https://swifterm.com on January 31, 2023.