Use Visual Search To Detect and Tag All Products On Your Full Body Images

How Automated Product Tagging Can Reform Your Fashion Business

On-demand fashion has irrevocably changed the retail landscape – the backstage operations at design, buying-merchandising, production and all touchpoints in the customer journey. Retailers have more variety on their product catalogue to meet rapidly changing customer demand. And customers love variety, don’t they? Yes and no.

Yes, having abundant choice allows each shopper to find exactly what they are looking for. This can be a winning strategy, but how you process and present your catalogue makes or breaks the customer experience. Imagine you are searching for a pair of tapered dark wash jeans. By page five, you have seen tapered jeans and you have seen dark wash jeans. Yet, no sign of what you are actually looking for. At this point, you are likely to give up and look elsewhere. In other words, the retailer’s horror story: Site abandonment.

This is where you want to turn to automated product tagging.

 

What Are Product Tags?

Product tags are a set of additional attributes, known as attribute metadata. They add detailed definition and information to enrich your base-level data. In this case, your product title and description would be base-level data, such as “Floral-Print Crepe Midi Dress”.

By adding attribute metadata to your products, you improve search functionality, product ranking and product recommendations for the customer. On the business side, you streamline your inventory management process. This can be done in one easy step through automated image tagging.

Attribute metadata is:

Faster Non-biased Searchable Retrievable
Scalable Standardised Sortable Layer Multiple Values

 

For multi-brand retailers, automated product tagging standardises different tags and product information coming from each brand’s garment taxonomy. This way, your entire catalogue is collected under one consistent roof that reflects your brand definition.

 

How Does Product Tagging Work?

Retailers have traditionally labelled their catalogue with tags that facilitate sorting and searching through inventory. Manually. Manual tagging requires excessive labour, takes time and lacks depth. Additionally, it is subject to human bias. This is where AI and technology enter the scene to change the product tagging game.

Using AI image tagging, each product on your catalogue is automatically tagged with multiple attribute labels. Attribute labels add improved, in-depth and fashion-specific product information not covered by generic tags.

Automated image tagging is completed at a fraction of the time and labour cost of manual tagging. In other words, you will increase operational efficiency and your catalogue processing time by up to 90%. Additionally, you will improve the quality and coverage of your product tags. This way, your merchandising teams have more time to focus on the areas of your business where their expertise and human touch make a real difference.

 

What Are The Use Cases and Measurable Benefits of Automated Product Tagging?

1- Online Storefront

Improve product discoverability and increase search engine click-through rate by offering immediate, accurate search results. Online customers can browse your entire catalogue faster and without friction to find product recommendations targeted to their needs. By layering multiple tags per product, you enable colloquial categorisation of your entire inventory that reflects a customer’s real-life description.

 

2- The Shop Floor

In-store sales and styling teams can use product tags to search through the in-stock inventory with speed and accuracy.

This way, your teams spend less time trying to find the right products and more time servicing and delighting the customer. These memorable, personalised experiences drive footfall to your physical store.

 

 

3- Inventory Management

Group and manage your entire inventory for different customer segments, customer tiers and geographical locations by using regional and segment-specific product tags. These tags are flexible, adaptable and responsive to real-time changes in your customer base.

Use micro and macro trend attributes to sort your catalogue. Always stay relevant by addressing the seasonal shifts that affect your customer’s top search words.

 

4- Personalisation

Use your product tags to identify and understand each customer’s individual taste and style. Then personalise search results, product pages and similarity recommendations through their favourite attributes.

Easily create and layer attribute ‘filters’ with your product tags. Use unique ‘filters’ to give personalised ‘Complete The Look’ outfit recommendations for every product on your catalogue at a quality that can outperform your top human stylists.

Personalisation can be integrated into multiple customer touchpoints:

  1. Online recommendations to increase AOV (Average Order Value) and customer engagement.
  2. In-store clienteling solutions to supercharge your store teams. With the right tools, they can make personalised styling recommendations and up-sell while delivering value to your customers.
  3. Re-targeting and promotional campaigns that only show personalised, relevant content. Addressing each customer’s unique preferences will increase the ROI and efficiency of your campaigns.

 

 

 

5- Predictive Analytics: Inform Future Business Decisions

Utilise the power of machine learning to identify and understand past performance per product and per attribute.

Understand customer behaviour, style and preferences at individual, customer-segment and regional/geographical layers. Capture and analyse top trends and shifting trends at micro and macro levels.

By spotting all these real-time patterns in your data, you can inform future business decisions at design, buying-merchandising and pricing levels with a lowered risk of bias.

This means you will significantly lower price deductions, overproduction and product waste, benefiting your company financially and helping you create a more sustainable business strategy.

 

What To Look For When Integrating Automated Product Tagging To Your Business

 

  • Multi-Tiered Product Tagging Structure:

Tagging each product through a hierarchical image tagging system enriches your data with hundreds of available tags at each tier. Using higher-level categories, subcategories and multiple attributes allow better control of your entire stock.

 

  • Mutually Exclusive and Non-Mutually Exclusive Product Tags:

Mutually Exclusive product tags are single tags that highlight the exclusive attribute of a product. This makes it easier to divide your catalogue into higher-level categories for speed search and sorting. For instance, a product can only have one category tag e.g. Dress and one subcategory tag e.g. Wrap Dress.

Non-Mutually Exclusive product tags are multiple tags that can be layered for each product on your catalogue. They will enrich your product data and add information that is not included in your product description. The same Wrap Dress can have multiple attribute tags e.g. Midi Dress, Puff Sleeve, Short Sleeve, Cotton-Poplin Fabric, Asymmetric Hemline, Plunge Neckline, Ruffled, Checked Pattern, Purple, White, Slim Fit, Smart-Casual Occasion.

 

  • Visual Search and Multi-Product Detection:

Detect and separately tag all products on your editorial or user-generated full-body photography using visual search.

Use Visual Search To Detect and Tag All Products On Your Full Body Images

  • Fashion-specific Data Set:

Use a fashion-specific data set that is trained to address your individual business as a high-street or luxury retailer. This approach ensures each product on your catalogue is enriched with the most relevant data to your inventory and your business model vs. using a generic list of attributes that will not give you this level of precise detail.

 

  • Optional Control:

If you want to have further control over your product tags, look for a product tagging tool. Speed editing enables your merchandising team to approve/edit automated product tags at a fraction of the time, effort and cost of manual product tagging. Cut down your operational costs while still overseeing the final level of approval.

 

  • Attribute Groups:

Work with attribute groups created for tagging fashion products across different segments such as womenswear, menswear, footwear, accessories, jewellery. Attribute groups break down your tags for each product segment in a categorical way. Fabric attributes, pattern attributes, hemline attributes, neckline attributes are just a few examples. This approach makes sure each product is tagged with optimum coverage.

 

Automating your product tagging process can reform your operational efficiency from the supply chain to value chain and improve your customer journey to deliver an exceptional shopping experience online and in-store.

ai fashion styling

How it Works: AI Fashion Styling

This article has been updated for the latest in AI Styling for 2020 and was first published in October 2019

A question that we often get asked when attending events or explaining Intelistyle for the first time to people is “Is this the real thing. Is it real AI styling?”. Which used to surprise us but then we realised that there are so many companies out there that claim to do Machine Learning, but what they really do is package up a couple of Amazon, Google or Microsoft cloud ML APIs, combine them with a freely available dataset, and voila, here’s a service that they can sell to customers as AI styling.

 

So we decided to do a write up on our technology to help our customers understand how this really works.

 

How We Collect Our AI Styling Data Set

We constantly crawl the web, very much like google’s search engine does. Instead of indexing generic information though, we focus on fashion data. We have particular data sources that we prefer, like fashion magazines, social networking websites, retail websites, editorial fashion platforms and blogs. That process allows us to collect thousands of outfits put together by human stylists. We use images and text to get the most complete and accurate information.

Now as you can probably imagine most of the web’s images are quite noisy. How do you extract the individual garments that are included in an outfit with varied backgrounds, different poses and models? The approach we took was to create a bounding box model that can create bounding boxes for each garment. Using that approach we were able to create a unique dataset of millions of outfits that we could use for training our AI styling model.

 

ai fashion styling

 

How We Keep Our AI Styling Data Set Up to Date

That dataset is constantly updated and quality controlled by our team. That allows us to keep up with the latest trends across different regions. We have clients in China, Europe and the Middle East and as you can imagine, the trends in each of these regions are very different. What is considered fashionable in one region, isn’t necessarily in another.

Our Machine Learning team uses the latest academic research to craft a proprietary, bespoke set of AI models that analyse images and text. Each garment in our database is described using a 128-dimension “signature” or embedding. You can think of this as a very similar process to what Shazaam does for music tracks. Each of these signatures describes the important characteristics of each garment and leaves out the noise.

 

However to create an AI styling intelligence that can perform as well as actual stylists, a good dataset and embeddings was not enough. While talking to our clients we realised that there are fashion rules that can make or break an outfit. For example, an off the shoulder top with puff sleeves should not be styled with a skinny-fit blazer. Our model could not predict these rules as good as humans yet. 

 

How Our AI Styling Quality Outperforms Human Stylists

The solution to that was to train another model that can detect rich attributes such as it’s fabric, cut, style, colour as well as other unique characteristics and categories for each garment of our dataset. 

We also work with stylists with experience in brands such as M&S, Topshop, Prada and Vogue to create a unique set of ‘guidelines’ for our model to give preference to specific attributes when creating an outfit. And of course, because no two regions are the same, we can customise those guidelines to particular trends. For example, in the Middle East shorter hemlines are always paired with a longer overcoat and in Asia slip dresses should be layered over shirts.

The result? A proprietary set of AI and data, that outperforms all published academic research to deliver outstanding styling quality, trusted by the world’s top luxury brands such as D&G, MaxMara and Lane Crawford. 

 

We even tested it against real stylists and fashion influencers at London Fashion Week. As Forbes reported, 70% of respondents unwittingly chose the looks created by our model. 

 

Blockchain, A.I. and Other Game-Changing Fashion Technology in 2020

This article has been updated for the latest developments in fashion technology as of 2020 and was first published in May 2019

The world’s most significant and profitable industries are facing massive changes thanks to advances in technology. More specifically, blockchain, artificial intelligence (AI), new types of financial transactions and a number of other big leaps in tech are responsible for these ongoing changes to how most industries do business.

Focusing on the fashion industry in particular, these are some of the technology we expect to transform businesses on both online retail and in-store operations.

fashion technology blockchain

 

Blockchain Will Fight Counterfeit Products & Streamline The Fashion Supply Chain

The distributed ledger technology called blockchain is mostly known for being the foundational tech behind cryptocurrency. More generally, a blockchain is basically a string of chronologically arranged code. Each new block of code on the chain requires every computer with access to the blockchain to approve the addition of that data via a shared cryptographic solution, which translates to peer-verified security in every data transfer, addition or modification. It all happens nearly instantaneously, without any of the red tape commonly attached to security protocols. And this same technology can be used to protect products and ensure quality in the fashion industry.

Label Tags Will Redefine Origin and Ownership

Each individual fashion item can be tagged with labels attached to blockchains, which allows everyone in the supply chain to verify its origin, ownership and every time it has changed hands. The LuxTag Project took to Medium to detail how some fashion designers are already taking advantage of this potentially revolutionary technology as a product integrity solution. Back in 2017, Londoner Martine Jarlgaard produced the first smart label-tagged garments. Scanning the tags gave users time-stamped info on everything about the garments – from raw material acquisition and factory information down to how the finished products were packaged and delivered. Similarly, fashion retailer Babyghost used near-field communication (NFC) chips to tag its 2017 summer and spring collection. This allowed customers to use the NFC tags to find out everything they wanted and needed to know about every Babyghost product.

Supply Chain Transparency

Apart from ensuring product integrity, this can also push fashion labels to be more honest about where they get their raw materials, how they conduct labor practices and everything else customers in 2020 (and beyond) will be concerned about. Essentially, information sharing and supply chain transparency are about to become realities in the global fashion industry. This technology is a potentially huge boon to managing the fashion supply chain, the fight against counterfeit goods, and corporate social responsibility.

 

Competitive Payment Options Will Dictate Retail Preferences

In recent years, payment platforms such as Paypal, Amazon Pay, Payoneer, Venmo and Dwolla have played increasingly large roles not just in online retail, but in various other spaces such as vacation rentals, gaming and service industries. The reason is simple: these payment options offer the convenience and security that some more traditional methods lack. By and large, newer, more innovative payment platforms are now making things even easier for both customers and product and service providers.

A payment technology that has already changed the way customers shop is the buy-now-pay-later services that allow customers to buy a product today and pay for it later. Klarna is one of the service providers at the forefront of this technology in the fashion industry.

Another example is the service called Paysafe Pay Later allowing customers to delay payment until after their ordered products have been shipped, all in a way that doesn’t impact the company’s cash flow. A New Zealand-based gaming company details how online casinos now use the Paysafecard, which is similar but slightly faster than using VISA and other debit/credit cards. Independent of bank, card or other personal financial information, Paysafe instead relies on a single, 16-digit pin to credit money to a customer’s account and verify transactions. It’s currently seeing heavy use in gaming and retail – two of the biggest revenue sources on the web. And it’s just one example of a new payment service that could prove to be a deciding factor in how customers choose which fashion retailers to shop from in the future.

fashion technology smart mirror

Nobal’s smart mirror solution for fitting rooms

 

Artificial Intelligence Will Change Everything

There are several reasons why Intelistyle cannot help but cover how AI will change the face of retail. For one thing, AI algorithms are responsible for well-informed and relevant product suggestions for online retail customers. For another, AI can be used to predict trends and product demand, allowing retailers to be better at managing inventory and catering to customers’ needs.

Though they may seem simple, even those small perks can help retailers avoid serious problems – such as the massive surplus Under Armour faced in 2018 when it overestimated product demand and wound up $1.3 billion the hole! And even this is only scratching the surface of the benefits A.I. can provide to retailers.

When integrated into a chatbot, AI can help fashion retailers scale their personal styling services and improve their omnichannel customer service experience.

Examples could go on and on, but even the relatively brief write-ups above provide a picture of how a small handful of tech innovations can and will revolutionize the fashion retail business.

How AI Is Set To Change Fashion Retail

 

This article has been updated for the latest developments in AI fashion retail as of 2020 and was first published in December 2018.

The past, present, and future of AI technology in fashion retail.

An exclusive interview with Paul Kruszewski, Artificial Intelligence (AI) technologist, serial entrepreneur, founder and CEO of WRNCH – a leading AI computer vision software engineering company based in Montreal.

The “machines” are coming! As AI technology is fast becoming an integral part of fashion retail, the reaction of brands and retailers remains mixed. With increasing pressure from consumers for a more personalised retail experience, fashion retailers are turning to scalable AI solutions.

When a technology is projected to revolutionise the industry, the possibility of feasting on the early adopter’s advantage captures the most sceptical hearts… on the other hand, scepticism takes the reins and demands to know:

Will AI be the end of human creativity and jobs in fashion retail?

While the answer is more complex than a single word, it is still a solid: No.
The good, the bad and the ugly speculations surrounding AI should all be taken with a grain of salt.

Darwin’s foresight that it’s neither the strongest nor the smartest but the most adaptable to change that will survive the test of time rings true. The fashion industry is dipping its toes (or legs as of 2018) in the waters of artificial intelligence.

We sat down with a man who took a deep dive into these novel waters 20 years ago: Paul Kruszewski, AI technologist and serial entrepreneur. During our exclusive interview on his personal journey to success, he shared valuable tips for companies looking to incorporate AI into their omnichannel fashion retail business. Come and take a look at the past, present, and future of AI and its implications for fashion retail.

 

ai fashion retail experience

 

From Farming To AI Fashion Retail

Paul’s first encounter with technology dates back to late 70s, when he was raising pigs for pocket money as a 12-year-old kid at his family farm in Alberta. “I really wanted a computer, that was the cool thing” he recalls. After selling his pigs for $250 and combining it with his brother’s $250 input, he was still only halfway there. “I go to my dad and said, programming is the future. We need to buy a computer. You match us up to $1,000”. $500 later, he was writing his first program on a Radio Shack TRS-80.

 

radio shack desktop from 1970s

Radio Shack TRS-80 is one of the first desktop microcomputers launched in 1977

 

Fast forward to 1998, having completed his bachelor’s in computer science at University of Alberta, his MA and Ph.D. at McGill University, Paul was recruited by a company called My Virtual Model: “They said, e-commerce is going to change everything. We’re going to create your body on the internet and we will put clothes on you and we will charge”. With just a powerpoint and a $30 million capital, he took the team from 1 person to 60 in 9 months, built a chip while the founders sold the software. Looking back at the business, he maintains the vision was “conceptually great” but “the promise of trying clothes on the internet and making a buying decision from that… It was 20 years too early. The technology wasn’t there!”. Regardless, this was one of the earliest application attempts of AI personalisation in fashion retail.

 

ai smart mirror for fashion retail

See how Nobal is using iMirrors to enhance the in-store customer experience in our interview with Thomas

 

Fashion Retail: Right on The Cusp of Change

When asked where he sees the technology today in means of enabling that vision, he says “We’re right on the cusp”. Paul goes on to predict that in 5 years it will be pretty standard for people to stand in front of their TV and try on basic clothing. He is confident that AI is going to transform everything about fashion retail… the supply chain, design, consumer experience: ‘The design process of clothing will be semi-automated in 10 years. On the other side of the spectrum, AI is going to completely change materials and 3D printing”.

 

ai wearable technology in fashion retail

Electronic textiles known as “Smart Fabrics” enable digital components to be embedded in them for a variety of benefits from customised fit to weather adaptability

 

Business of Fashion’s collaborative report with McKinsey & Company confirms Paul’s prediction, stating that 20-30% of current fashion jobs will become automated. But, don’t brace yourself for a disaster scenario just yet. Rather than replacing humans, AI will be supplementing existing fashion retail jobs and will be creating brand new ones. The bottom line is that fashion still does and will persist to need the human touch. Prominent fashion schools of the world like The Fashion Institute of Technology in New York and The London College of Fashion are already incorporating AI enabled skills training into their degree programs to raise the next generation of fashion retail leaders.

“…who controls the customer experience? Will it be the clothing manufacturer, the retailer or the person behind?”

Speaking of leaders, back to Paul Kruszewski, who was next recruited as the CTO of a video games company. “But I realised that I’m a technologist and an entrepreneur. In 2000, I started my first company in AI”. Shortly after, he was approached and persuaded by Rick McKenzie, a professor, and military researcher, to collaborate on military simulations. It didn’t take long to start getting results…

 

The Technological Power Play in Fashion Retail

When once he was struggling to secure a place in the market, he was now getting calls from companies and corporations including the US Marine Corps. But what is the secret to his commercial success? “No matter what you think is the right call at the time, you have to listen to the market” he reveals, “There will always be that interesting tension of who controls the customer experience. Will it be the clothing manufacturer, the retailer or the person behind?”. While we will have to wait to find out the definitive answer to that question, he expects to be interesting to watch as completely different possibilities for fashion unfold over the coming decade.

 

The Vital Questions To Ask Your Business

As with all virgin territory, incorporating AI into your fashion retail business comes with the innate possibility of mistakes along the way. “All of a sudden everyone is an AI-powered company. Why?” he inquires and then proceeds to highlight that AI is a tool to enhance your existing operations and the customer experience, not a product in itself. In this uncharted land, where there isn’t yet enough history to go by and map out a fine print to success, Paul encourages companies to go back to 3 basic questions for a seamless integration process:

1- What are you trying to solve?
2- What is your pain as an organisation?
3- What are you doing that you want to get better at?

And find out where you can be unique!

 

How to Incorporate AI into Your Fashion Retail Business

A lot of traditional fashion retailers do not have the expertise to work with their data. In such instances, Paul recommends collaborating with an external AI expert specialised in fashion retail who will curate your data for optimal results. Which brings us to one of the most immediate challenges in AI, collecting a good data set: “There are widely available datasets but your competitors also have access to them” he says. Bespoke, customised data is the way forward for finding and capturing the unique competitive advantage that your business needs to differentiate and come into prominence. Once again, delegating the task to AI experts specialised in your industry, who can help you build your own data sets is key: “Some organisations initially freak out because they have never heard of that. And that’s fine!”

 

ai data fashion retail

Companies are encouraged to work with an AI expert on curating their own datasets

 

The Evolution of AI in Fashion Retail

Talking about the evolution of AI as it relates to fashion retail, Paul acknowledges that we have come a long way from the early simulators that were essentially just a pose with no interactivity. “We generate synthetic data: virtual humans, virtual clothing. We dress them, put them in virtual environments and take virtual pictures of them”.

See the full interview here.

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