Laura, Author at Intelistyle

Fashion AI In 2025: An Extensive Guide

Laura — Thu, 19 Jan 2023 06:17:00 +0000

Fashion AI In 2025: What is Trending This Year

Last Update Jan 2024

From business operations to e-commerce and marketing, every area in a fashion business can benefit from AI.

This is evidenced by popular fashion brands like Zara, H&M, Dior, Macy’s, and Nike, who all use AI in their business models. Whether through AI chatbots, personalisation, or trend forecasting, AI is already helping both small and big fashion companies promote goods, improve sales, and enhance the customer experience,

As AI continues to evolve, more and more companies will embrace these particular AI use cases. However, we will also see a growing diversity of fashion AI applications emerge. Below are three fashion AI trends we can are seeing taking shape in 2025.

1. AI-Generated Fashion Images

Producing around 13 million tons of textile waste every year, fashion is one of the most wasteful industries in the world. And although customers are increasingly turning to brands that offer sustainable clothing collections, and a growing number of brands are launching their own second-hand marketplaces, unsold clothes are still a huge problem. The good news is that it’s also one that AI can help solve.

Today, buying and merchandising teams looking to cut down overstock no longer have to rely solely on subjective opinions about what is going to fly off the shelves. Instead, they can use predictive analytics and trend forecasting to make smarter buying decisions based on historical product data (including detailed attributes, like the colour, print, and cut of trending items) and rich media from the web. On the other hand, through AI inventory optimization, fashion retailers can decide on the optimal geographical allocation and market drop calendar for a new product catalogue.

In the near future, fashion brands will be able to take this one step further. Thanks to advances in AI, it will soon be possible for businesses to create realistic images of virtual garments and accessories based on customer demands and fashion trends. Brands will then share these high-quality visuals with consumers through social media or e-commerce platforms to get their feedback before sending clothing designs to manufacturers.

In this way, fashion companies can use AI to forecast the demand for specific items more accurately, sending only the clothes that customers love to production and eliminating the problem of unsold inventory.

2. Virtual photoshoots

During the pandemic, brands were often unable to bring models in for photoshoots safely. For many e-commerce stores, this posed a serious issue as model shots can make items up to 60% more likely to sell.

Unsurprisingly, some of the most popular fashion brands came up with creative solutions. The British online fashion and cosmetic retailer ASOS, for example, used AI technology to hold virtual photoshoots where six real-life models had virtual copies of the brand’s clothes mapped onto their bodies, providing shoppers with realistic garment images. It is perhaps no coincidence that the company tripled its profits from the end of 2020 to the start of 2021.

The impressive capabilities of fashion AI are further demonstrated by a recently developed algorithm that can change a fashion model’s pose and even alter the clothes they’re wearing without losing important details.

As AI advances, we are also seeing the rise of an unexpected modelling development — the computer generated fashion model.

Some “well-known” models are already generated entirely by AI, such as the Brazilian-American “model” Miquela Sousa, who has 3.1 million followers on Instagram and has collaborated with Givenchy and Prada and appeared in a Calvin Klein video alongside the real-life model Bella Hadid. Without being told about her origins, few people can tell that she doesn’t actually exist.

Although digital models can’t yet offer posing variations on their own, that is bound to change. Tech companies are currently working on algorithms where digital models can mimic e-commerce and commercial models.

While the rise of AI models might seem slightly dystopian, the benefit of increasingly virtual photoshoots is that they can help reduce the environmental waste that physical photoshoots produce, as most of the samples that models shoot in tend to end up in landfills.

Consequently, for brands with the need to turn around promotional imagery quickly, virtual photoshoots provide the opportunity to not only improve efficiency but also decrease costs.

As more and more people shop online, we will see a growing number of brands this year experimenting with AI to showcase their catalogues on either real or AI-generated models and even shoppers themselves. That is because the same body mapping technology that is applied to models can also be used for “virtual fitting rooms,” where customers can “try on” fashion products on their bodies by uploading their own photos.

3. Fashion NFTs designed by AI

AI is already helping companies develop new products. Soon, however, more products will be designed entirely by AI — a trend we are seeing already in the fashion NFT space.

Short for non-fungible tokens, NFTs are non-interchangeable (meaning that they’re unique and can’t be swapped for something of the same value) digital assets stored on a blockchain (a digital ledger of transactions). NFTs can be anything, including videos, music, images, and even clothes.

The concept exploded in popularity in 2022, and since then, numerous fashion brands have created their own clothing NFTs, which customers can typically “wear” on their digital avatars.

In 2022, for example, the luxury Italian brand Dolce & Gabbana released a collection that, besides physical clothing, also included NFT versions of the physical pieces as well as purely virtual products (including “The Impossible Tiara,” made from “gems that can’t quite be found on Earth”) and sold for almost $6 million.

While there is no shortage of NFT creators, NFTs don’t necessarily require the human touch. Late in 2022, we saw the launch of NFT sneakers made 100% by AI — the first NFT sneaker line of its kind. The AI looked at thousands of existing sneaker pairs made by humans to recreate unique futuristic sneaker models that had never before been seen.

As this trend develops, we may soon see AI producing hyper customised and relevant products far more rapidly than human designers.

Fashion AI Is Already Powerful

By 2030, PWC has forecast that AI will add almost $16 trillion of value to the global economy annually. For fashion brands, leveraging the ability of AI to exponentially increase growth will soon be a vital operational task.

Even if AI-generated fashion images, virtual photoshoots, and fashion NFTs don’t go mainstream in 2025, we will undoubtedly see more fashion businesses experimenting with the three novel use cases of fashion AI this year, which will eventually pave the way for their adoption later down the line.

For the moment, however, there are plenty of other tried-and-tested fashion AI applications that every fashion brand can and should take advantage of. Starting with personalised recommendations and AI chatbots and ending with smart mirrors, AI can help any business increase customer engagement, boost sales, and stay relevant in a crowded market.

Interested in making fashion AI work for you? Get in touch with the team at Intelistyle today.

Book a free consultation

The post Fashion AI In 2025: An Extensive Guide appeared first on Intelistyle.

How Tagging Products Can Help Your e-Commerce Conversion Rates

Laura — Thu, 20 Jan 2022 17:03:22 +0000

How Tagging Products Can Help Your e-Commerce Conversion Rates

For any fashion brand looking to improve its e-commerce conversion rates, there is no shortage of strategies to explore. However, there is one fundamental process no brand can afford to ignore: tagging products.

If your products are not tagged appropriately, no amount of A/B testing, social proof, or discount codes will help attract — and keep — customers on your site.

So, what are product tags, why do they matter, and what’s the best way to create them?

What Is Product Tagging?

Product tagging is the act of adding keywords to items in your catalogue in order to categorise and organise your inventory.

Also known as fashion label tags or product attributes, these keywords can be broad (for example, “dress”) or detailed (for example, “sleeveless,” “v-neck,” “ruffled,” and so on, as per the example above.)

How Tagging Products Boosts Conversions

Tagging products is one of the easiest ways that fashion e-commerce retailers can increase their sales. Here’s how.

1. Improved store visibility

With so many fashion e-commerce stores out there, and mega-retailers dominating the online fashion marketplace, it can be difficult for smaller retailers to get their products in front of potential customers.

You could have the best product catalogue on the internet, but if it’s not easy for your customers to find what they want, then your conversion rate is going to be low.

The good news is that attaching appropriate product tags to your inventory can significantly improve your store’s visibility through SEO.

For instance, if people search for something specific on Google (like “white tennis skirt”) and your product tags accurately reflect a matching item within your catalogue, you are far more likely to rank highly on the results page.

Consequently, a shopper will more than likely end up clicking on your e-commerce store.

2. A direct route for customers to find products

Not every customer will find what they’re looking for through a search engine.

For example, someone might start their journey on Google, click on a product on the results page that seems to match their search, and then decide to explore your site further to see what else you have in your product catalogue.

On the other hand, others may go to your e-commerce store directly.

In either case, if your site doesn’t use product tags, customers will not be able to navigate your store through the filters/menu navigation/search bar.

Unfortunately, although many retailers use product tags, few use them well. This frequently creates an under-par experience for users, especially those who know what they want and go straight to the search bar function of an e-commerce store. Research shows that almost half of all users immediately go to an e-commerce store’s search bar. These users are also up to 3 times more likely to convert.

Provided you have an item that a user is looking for, and it has the right product tags attached to it, the user will be able to find it — and buy it — quickly. That’s because, with product tags, your store is optimally searchable.

For instance, if someone looks for a “blue v-neck jumper” on H&M, they will be shown all the blue, v-neck jumpers that H&M currently has in stock rather than every other jumper (or other products) in H&M’s inventory.

Used in this way, not only will product tags help you increase sales, but they will also aid you in building customer loyalty and increasing repeat purchase rates.

After all, if shoppers know that they can quickly and easily discover items they want on your e-commerce site, they will return to you before trying your competitors, who may not provide such a good search experience. In fact, more than 1 in 10 users go to a competitor’s site after a poor on-site search experience.

3. Better business decisions

Figuring out why certain products are in-demand, and others are collecting dust isn’t always easy.

For example, if the following graphic tee is always out of stock, retailers may wonder why that is so.

Is the t-shirt popular because it:

Is off-white
Has a scoop neck
Is a regular fit
Is made of cotton
Has a graphic
Has a graphic of a cat
Features “The Lucky Cat”

Product tags can help brands determine precisely what it is that makes some items attractive to customers based on product attributes. Retailers can then order more products that share similar features.

In doing so, brands can minimise the number of unsold items sitting in their warehouses and improve conversions by stocking only products with attributes that they know their customers already love.

4. Next level personalisation

By tagging products, brands can pinpoint their customers’ tastes and offer them personalised results, whether on the main page, product pages (for example, “Complete the Look” or “Visually Similar”), or through personalised marketing emails.

For example, Nordstrom shows returning users items that they are likely to be interested in based on their browsing history:

By using product tags to show customers items that match their style and preferences exactly, brands are more likely to:

Engage them, which will ultimately lead to a better customer experience.
Improve conversion rates.
Increase the average order.

Tagging Products: A Quick Guide

There are two ways to tag your products:

Manually
Automatically.

One of these methods — manual tagging — is totally outdated. Although some retailers still tag their products manually, doing so doesn’t really make business sense.

Manual tagging takes up a lot of time that could be better spent elsewhere. It also means that your products will not be searchable immediately, which could mean that retailers could lose sales on seasonal or trending products.

Particularly if you have a large inventory, manual tagging is also likely to be costly as you will need to hire additional people specifically for this task.

But even if you employ someone solely to tag products, inherent human bias means that mistakes are inevitable. This explains why around 30% of product data in retail catalogues is incorrect.

Product tagging mistakes cost retailers money not only in the sense that a missing tag can lead to a missed sales opportunity but also in that correcting catalogue errors can cost retailers anywhere between $60 and $80 per mistake.

In contrast, automated product tagging is foolproof. By using AI image tagging solutions, such as the one offered by Intelistyle, retailers can be guaranteed that every single product in their catalogue will be tagged with multiple relevant tags instantaneously.

Since Intelistyle’s automated product tagging tool can spot even the smallest details, you don’t need to worry that your customer will miss your products if they don’t use precise search terms that match your products’ descriptions.

And, because automated product tagging increases catalogue processing time by 90%, shoppers will be able to find new items as soon as they drop.

Best of all, automated product tagging doesn’t require any input from you or your staff. Automated product tagging can examine a product image entirely on its own, identifying specific features and assigning it keywords from a predefined fashion taxonomy.

Tagging Products In 2022

Every fashion retailer knows that one of the most important metrics for success is the conversion rate.

That’s why many fashion e-commerce retailers were less than pleased to discover that, overall, the conversion rate for the clothing and accessories industry fell by 0.07% between December 2020 and December 2021.

Of course, the conversion rate is constantly fluctuating, and there are many factors that can affect it. One of the easiest ways to boost it or, at the very least, keep it stable is to ensure that your product attributes are up to scratch.

Manual tagging won’t work here. The method is too prone to error and takes too long to complete. Instead, every retailer that is serious about increasing their conversion rate this year needs to invest in an automated product tagging tool, like the one that we offer here at Intelistyle. Not only will using an automated product tagging tool take a weight off your merchandising team’s mind, but it is also much more affordable in the long run.

Drop us a line today to talk more about how your fashion business can use automated product tagging.

Book a free consultation

The post How Tagging Products Can Help Your e-Commerce Conversion Rates appeared first on Intelistyle.

10 Different Examples of Image Recognition for Retail

Laura — Tue, 04 Jan 2022 16:21:57 +0000

10 Different Examples of Image Recognition for Retail

The market size for global image recognition in retail is expected to grow at a CAGR of 22%, reaching 3.7 billion by 2025.

Here’s why fashion retailers are investing in this technology, why you also need to get on board with this new trend, and how you can use image recognition to stay ahead of the competition.

What Is Image Recognition?

Image recognition is how software processes, analyses, and categorises people, places, animals, logos, and objects via algorithms and machine learning concepts.

Although it may sound pretty technical, chances are, you’ve already come face to face with image recognition technology in your daily life — maybe without even realising it.

It is thanks to image and facial recognition that we can:

Unlock our phones with our faces.
Organise photos on our phones by subject, plus automatically attach tags, labels, and captions to images.
Use face filters on social media apps like Snapchat.
Recognise objects with Google Lens (for instance, identify flowers growing in your garden or find out the brand of shoe your friend is wearing so that you can purchase them as well).
Discover ideas based on pictures you take with Pinterest Lens (for instance, take a picture of an ingredient to be shown recipe ideas).

Image Recognition for Retail: Use Cases and Examples

There are numerous ways image recognition can be used in retail. Here are some of the most popular use cases with examples:

1. Visual search

In the past, even if customers knew exactly what they wanted to buy, they still had to either:

Try to put whatever they were looking for into precise search terms.
Scroll through multiple pages of a retailer’s stock like everyone else.

Unfortunately for fashion retailers, both approaches would frequently lead to customers leaving their site without buying anything.

In the first case, shoppers may not know how to formulate their search queries. And even if customers manage to put into words whatever it is they want to buy, more than 7 in 10 e-commerce sites fail customer site search expectations. Even worse, at least 15% of sites have “broken” search experiences.

In the second case, very few shoppers actually have the patience to go through countless pages of a retailer’s inventory, especially considering that doing so doesn’t even guarantee that they’ll find the product they’re on the hunt for.

This is where visual search comes in. With visual search technology, customers can upload an image of an item they like (it could be a photo they took themselves or a picture they found online) to a brand’s website to see if it has something visually similar in stock.

For instance, the British online fashion and cosmetics retailer ASOS has a “Style Match” feature that lets shoppers take pictures or upload images to get similar results. So, if a customer uploads the following picture of someone wearing a pink skirt:

They will be shown the following results:

Similarly, if a user uploads a photo of a graphic tee to H&M, its visual search function will recommend more graphic tees that are similar in style:

However, users are not limited to uploading images with just one item. H&M’s visual search function can recognise numerous products within a picture. So, if a customer uploads the following image:

H&M’s image recognition technology will identify both the pants and blouse, like so:

Then, after the user chooses the items they want to see similar results to (in this case, both the pants and the blouse), H&M will recommend the following:

Notice how the results are a close match to the items in the original image uploaded by the user.

2. Product tagging

Shoppers are increasingly using visual search to find the items they need or want. However, that doesn’t mean that fashion retailers can now ignore the search function on their online stores altogether. Almost half of all users go right to the search bar on a retailer’s website.

The key, therefore, is to make sure that the results customers see are relevant to what they’re looking for. One way to do this is through accurate product tagging. Note the word “accurate.”

Traditionally, retailers would spend hours manually applying product tags to photos in their product catalogues. Not only does this take up a lot of time, but if an employee tasked with tagging the products makes a mistake, it will also lead to irrelevant search results for shoppers.

Luckily, advances in image recognition technology mean that there’s now a better way of doing this: automated product tagging.

Using advanced image recognition algorithms that take advantage of deep learning, automatic product tagging automates the process of tagging products with multiple attribute levels based on the product’s details.

The result? Retailers can tag their products without any human intervention — saving both time and money — and customers can more easily find whatever it is they’re looking for.

An excellent example of a fashion retailer that has its product tags down is Zara. Here’s what a user sees when they search for “stripey jumper”:

All of the results are of stripey jumpers. Most importantly, it doesn’t matter whether a user puts in “stripey jumper,” “striped jumper,” or “stripy jumper” — the results are still going to feature jumpers with stripes.

This is in direct contrast to H&M, which, if you misspell a query, will tell you that there are no matching items in stock. That being said, it will nevertheless show you products that it thinks you meant:

Another store that does product tagging well is & Other Stories. Whereas some fashion retailers will show you results that you want to see alongside results that you don’t care for, & Other Stories will only display items that match your query exactly. If a browser looks for an oversized grey blazer, they will only be shown oversized grey blazers and not also, for example, oversized black blazers:

3. Visually similar product recommendations

Sometimes, a customer may have no trouble finding a product that they really like — but they may still not be able to buy it because the item may be out of stock or it may be in the wrong colour or cut.

AI-powered image recognition technology can analyse the attributes in product images (like style, colour, and cut) to find items that are most similar to it in a retailer’s inventory.

For example, when a shopper views the “French Connection Cleo Turtleneck Pullover” on Evereve, they can also see other pullovers and turtlenecks in a similar style that the store stocks:

Lane Crawford does the same thing. However, notice how the below white dress is not yet available for purchase. In these types of scenarios, having visually similar alternatives can give customers who are unwilling to wait for an item to come into stock other options:

4. Personalised product recommendations

Fashion brands can use image recognition technology to identify product attributes within a selection of product images viewed by a customer to refine and customise product recommendations.

From a shopper’s point of view, this might appear as a “shop the look” or “complete the look” widget or an email letting them know about an item that they will likely love based on their previous activity.

Lane Crawford shows customers how to style a garment to make a complete outfit. Not all of the items suggested match the editorial image. This is because some of the products from the editorial shoot may be out of stock. Ideally, you want the “complete the look” widget to change depending on what’s in stock. In this case, it is obvious that the nude heels from the editorial photo are out of stock, so they were replaced with black heels instead:

Other suggestions that don’t appear in the editorial picture include a black shoulder bag and a scarf. However, these two items go perfectly with the dress and are a great way to increase customer spend through up-selling and cross-selling.

5. Smart mirrors

In physical stores, image recognition is often used to power smart mirrors (also known as smart displays or digital mirrors).

Through image recognition technologies, a smart mirror can detect when a customer stands in front of it and suggest outfit ideas based on the clothes the customer is already wearing. The customer can then see (via the mirror) how specific clothing items would look on them if they wore them, even turning around to look at themselves from all angles.

Most smart mirrors can also offer alternative options when a garment a customer is interested in is out of stock. Shoppers can also choose between different colours and styles, which eliminates the need for them to shuffle between the changing room and the shop’s floor. Some smart mirrors also let shoppers take photos of themselves “wearing” these outfits for future reference.

Take the iMirror, for example. With just a few taps on the screen, shoppers can create avatars of themselves to try on items, see product recommendations, and find out what’s in stock.

Customers can also take selfies and send photos of themselves wearing specific outfits to their friends for feedback.

Retail stores that have implemented the iMirror have seen their customer basket size increase by up to 20%.

Final Thoughts on Image Recognition for Retail

There’s no doubt about it — when it comes to keeping your customers happy, improving your bottom line, and getting ahead of your competitors, image recognition technology is hard to beat.

Whether you want to implement visual search, automate product tagging, give browsers better recommendations, or wow your in-store customers with smart mirrors, you’ll want to invest in image recognition.

The best part? It’s not hard to get started with image recognition for retail. Intelistyle’s solution takes advantage of AI to offer fashion retailers all of the above and more. Drop us a line today to talk about how, with the help of image recognition, you can take your store (both online and offline) to the next level.

Book a free consultation

The post 10 Different Examples of Image Recognition for Retail appeared first on Intelistyle.

How a Shop the Look Widget Can Increase Basket Size by 40%

Laura — Mon, 06 Dec 2021 15:58:54 +0000

How a Shop the Look Widget Can Increase Basket Size By 40%

Imagine this all too common nightmare scenario: Joanna, a potential customer, is scrolling through your e-commerce store when she comes across a t-shirt she likes. She clicks on it and is redirected to a product description page that features an editorial image of a model wearing that particular t-shirt with other items — jeans, shoes, and accessories — from your store.

Joanna likes the model’s outfit. In fact, she likes it so much that she decides to buy all the products from the image to replicate it exactly in real-life.

But there’s a problem: the items from the editorial image are scattered throughout your store. Consequently, Joanna has to scroll through each product category (jeans, shoes, and accessories) within your store to find the exact products from the image. Unfortunately, some products are out of stock, whereas others are not available in her size.

Halfway through trying to find the items she wants, Joanna grows tired, gives up, and leaves without buying a single thing — not even the t-shirt she was interested in initially.

Ultimately, Joanna ends up having a bad customer experience, and the store misses out both on a sale and her repeat business. Surely there’s a solution to this problem? Yes: it’s called shop the look widgets, and it can increase your basket size by up to 40%.

Shop the Look Widget: What Is It?

A shop the look widget is a widget that gives customers outfit recommendations for a particular product.

If a customer is unsure how to style a specific item, then a shop the look widget can provide them with inspiration. For example, if a brand uses editorial photographs, a shop the look widget makes it easy for them to replicate the outfits featured in these images.

Here’s how Farfetch does it:

What’s notable here is how easy it is for Farfetch customers to add an item that is part of an outfit to their basket. Browsers are able to not only check size availability right from the widget but also immediately add the product to their bag by clicking the “Add To Bag” button.

The best shop the look widgets can transition to complete the look experiences when specific items go out of stock. Otherwise, outfit recommendations can end up being incomplete.

Here’s how The Outnet does it:

Notice how the recommended top in the “wear it with” section is not an exact match for the top in the editorial image. In all likelihood, the top worn by the model is out of stock, hence why it was replaced by a similar option.

When almost all of the items worn by a model on a specific product page go out of stock, a brand may recommend a completely different look. Here’s how Lane Crawford does it:

Except for the shoes, the items suggested in the “how to style it” section (i.e., complete the look) do not correspond to the items in the image with the model. However, the recommended products nevertheless complement the blouse.

A complete the look widget can also give customers more ideas on how to style a specific garment. In the Lane Crawford example, a customer that views the blouse’s product page knows that they can style it with simple blue jeans and black high heels. However, as made clear by the “how to style it” widget, the blouse would also look great with those same black heels, a mini black skirt, and a black leather phone holder.

Nordstrom goes a step further by featuring several “complete the look” widgets for each product (in this case, a silver sequin side slit skirt):

Shop the look and complete the look widgets are not exclusive to product pages. Retailers can also add them to email marketing campaigns or even social media.

Why Your Store Need a Shop the Look Widget

There are numerous reasons why your e-commerce store needs a shop the look widget. Below are just some of them.

1. It’s a way to educate and inspire your customers

The number one mistake most fashion brands make is that they sell to their customers rather than styling them.

The truth is, many people shop for entire outfits rather than individual pieces. Instead of showing a customer a product — say, a dress — brands should therefore demonstrate how they could wear it.

Contrary to popular belief, editorial-style images of models wearing entire outfits are less important than shop the look widgets.

Take a look at this product page:

It shows a model in a maxi dress with what seems like knee-length brown suede boots. If you zoom in, you can also see that the model is wearing quite a few accessories, including large hoop earrings, several necklaces, a watch, and a few rings.

It’s a cool look, and there is undoubtedly someone out there who would like to copy it exactly. Unfortunately, because this particular shop doesn’t have a shop the look widget, they can’t — or at least not easily.

As mentioned earlier, to recreate an outfit featured on a store that doesn’t use shop the look widgets, a customer would need to look for the suede shoes in the “shoes” section of the shop and accessories in the “accessories” section.

In this particular shop, there are close to 10,000 shoes for sale. And even narrowing the selection down to “brown boots” still gives the customer just over 100 shoes to go through. The same can be said for accessories.

On the other hand, the store below does not use editorial images for all its products. As a result, customers can’t necessarily see how a particular item would look on a model as part of a complete outfit. However, because the product page features a shop the look widget, customers can nonetheless visualise the product — and how they could wear it.

By offering customers styling advice through shop the look widgets, brands can position themselves as an expert on fashion, especially if their suggested outfits are fashionable and “on-trend.”

Even if customers don’t buy the complementary pieces from you but are instead inspired to utilise items from their own wardrobes, with time, they will come to value your advice and will turn to you above your competitors in moments when they need a totally new outfit.

2. It can improve the shopping experience

For customers, one of the best things about shop the look and complete the look widgets is that they eliminate the tedious practice of having to scroll through a store’s entire catalogue to buy items that make up an outfit on a specific product page.

This vastly improves their overall experience — and time on site.

3. It allows you to create a VIP experience

Brands don’t have to show every customer the same product recommendations within a complete the look widget.

Instead, for repeat customers, retailers can personalise the widget based on their location, browsing history, and purchasing behaviour.

In this way, a complete the look widget resembles a personal stylist at a physical high-end boutique who would give a customer personalised advice on what to wear certain pieces with.

4. It helps you foster customer loyalty

Getting new customers can cost brands up to five times more than retaining existing ones. It is therefore vital that fashion companies work on building and maintaining customer loyalty.

Here, again, complete the look widgets can help. After all, customers are more likely to return to stores that inspire them, offer a seamless customer experience, and treat them like VIPs.

5. It increases revenue

Consider this: the likelihood of selling to an existing customer is somewhere between 60% and 70%. In contrast, the probability of making a sale to a new customer is only around 5% and 20%.

These statistics make it painfully obvious how important it is for retailers to engage in up-selling and cross-selling practices.

By showing customers how they could style a particular product, you are essentially engaging in up-selling cross-selling, a practice that allows you to sell more to existing customers.

If a customer likes the products recommended underneath an item that originally caught their eye, they will likely click on them and make additional purchases to recreate the suggested outfit.

For brands, this means increased average basket size and order value. One study found that the average basket size grows by up to 40% for users that are shown personalised recommendations.

Even if a customer doesn’t buy the suggested products, they are still more likely to buy the item they were initially interested in after seeing how they could wear it.

How to Automate Shop the Look Widget

While it’s possible to create shop the look and complete the look widgets manually, doing so doesn’t really make any sense.

To start with, generating product recommendations manually is time-intensive — just think of all the other things your staff could be doing with their time instead.

Moreover, with manual product recommendations, it’s almost impossible to cover all of the items in your inventory. As clothes and accessories go out of stock, keeping product recommendations relevant would also necessitate you or your staff to replace products that are no longer available with ones that are.

Importantly, creating product recommendations manually does not allow for personalisation — something that customers have come to expect of retailers.

The obvious solution is to automate shop the look widgets on your online store. With Intelistyle’s AI and Visual Search technology, brands can automatically replicate editorial photography in the form of shop the look widgets or create completely new styling recommendations.

In the latter scenario, retailers can create thousands of different looks from their inventory based on things like trending items, products that are in stock, and seasonal looks.

Automating product recommendations has been proven to increase operational efficiency by 30%. Furthermore, with AI, catalogue coverage is guaranteed to be above 90% for product recommendations.

Ready to start using shop the look widgets? Contact the expert team at Intelistyle today.

Book a free consultation

The post How a Shop the Look Widget Can Increase Basket Size by 40% appeared first on Intelistyle.

How to Generate Fashion Label Tags for Your Store’s Clothes

Laura — Fri, 26 Nov 2021 16:24:53 +0000

How to Generate Fashion Label Tags for Your Store’s Clothes

From having an active presence on social media to sending out relevant retargeting campaigns, there are many things brands can do to stand out from the crowd. However, none of these things matter if a store doesn’t nail down the basics of retail success. This includes generating accurate and rich fashion label tags for their clothes.

What Are Fashion Label Tags?

Fashion label tags are product tags (or product metadata) that allow customers to filter product results by specific attributes, such as a garment’s brand, colour, or fabric.

For example, if a shopper searches for a “yellow strappy linen dress,” their search query indicates that they want to buy a dress that is:

Made from linen
Is yellow
Has straps

If a retailer has created and assigned the right tags for their products, then the browser will see the following results:

On the other hand, if they haven’t, the customer may instead see the following results:

While the second example isn’t the worst — and there are a few yellow strappy dresses made from linen on the results page — it should not include dresses that have sleeves, nor should it include dresses that are any other colour than yellow. As it is, it does, which means that browsers are shown items they aren’t necessarily interested in buying.

Why Are Fashion Label Tags Important?

For customers, the main benefit of fashion label tags is that it’s possible to find the perfect product quickly.

If someone types in “yellow strappy linen dress” into an external search engine (like Google), they know that they’ll see results from brands that have items in stock that match these criteria.

However, if your products don’t have fashion label tags attached to them or they’re inconsistent or incorrect, then your stock won’t appear in these results.

Consequently, the shopper will assume that you don’t have what they’re looking for, and you’ll miss out on a sale. The same can be said for customers that look for particular items via the search bar/filters/menu navigation of your e-commerce store. In either case, customer experience suffers, and browsers that could have converted will more than likely end up shopping elsewhere.

On the other hand, for e-commerce sites, fashion label tags can help brands understand customer behaviour, which, in turn, can lead to better business decisions.

For instance, if a specific dress is selling incredibly well, retailers may want to know why that is so. Is it the silhouette of the dress that people like? The fabric? The neckline? The sleeves? Once they understand what makes the dress so popular, they can order other garments with the same attributes. Similarly, they can stop the production of items with attributes that are unpopular.

By knowing what specific attributes draw customers to particular items, retailers can also personalise each individual’s shopping experience. To go back to our previous example, if a person looks for yellow strappy linen dresses, the brand in question can assume that they like:

Dresses
Linen garments
Bright colours
Strappy sleeves.

The brand can then recommend visually similar alternatives underneath each product, which can be particularly helpful if the item a customer wants is out of size or stock.

Similarly, some brands may display product recommendations on their homepage when a browser returns to the online store in the future. If they can show items that are similar to the ones that the shopper was interested in the last time they were on the site, the chances of a sale are much higher.

How to Generate Fashion Label Tags for Your Store’s Clothes

When it comes to generating fashion label tags for a store’s clothes, retailers have two options: manual or automatic fashion label tagging.

Manual

Manual fashion label tagging is exactly what it sounds like: retail staff (usually the merchandising team) manually creating and applying fashion labels to photos within the brand’s product catalogue. A woefully outdated method, manual fashion label tagging comes with a number of disadvantages.

To start with, manual product tagging is time-consuming and difficult to scale. Even for retailers with small product catalogues, tagging items manually still takes up a lot of time and effort. For brands with hundreds of thousands of products and an inventory that is constantly changing, manual product tagging is completely unsustainable both from an operational and a financial point of view.

Even if a retailer hires more staff — which, depending on where they’re based, could be very expensive — getting products to market in a timely manner may still be a struggle. Unfortunately, this could have a negative effect on items that are seasonal or trending.

Furthermore, there is no way to guarantee that every product will be tagged appropriately or that staff won’t make mistakes (think spelling mistakes or incorrect category mapping) that could lead to lost sales.

Automatic

Thanks to advances in AI technology, AI can scan product images and tag clothes consistently, applying multiple attribute labels based on a predefined fashion taxonomy — all without any involvement from you or your staff.

However, that’s not to say that automatic fashion label tagging requires absolutely no thought on your part. To make it as hands-free as possible, you’ll want to make sure that whichever automatic attribute tagging tool you choose can do the following:

Tag products via a hierarchical image tagging system that supports mutually exclusive and non-mutually exclusive tags. This allows you to use higher-level categories (for example, “dress”) and subcategories (“slip dress”) that are mutually exclusive (i.e., they highlight the exclusive attributes of an item) as well as attributes (“strappy sleeve,” “yellow,” and “casual occasion”) that are non-mutually exclusive (i.e., you can add multiple tags to each item).
Detect and tag multiple products in product photos via visual search. Ideally, you want the tagging tool to be able to do this with both editorial images and user-generated photos.
Tag products with the most relevant data to your products and business. For this, you’ll want a tool that has fashion-specific data sets — like Intelistyle.
Give you the option to review tags. The best product tagging tool will allow you to evaluate and modify product tags created automatically.

AI not only makes the process of generating and applying fashion label tags faster and more cost-effective, but it also ensures more consistent, detailed, and accurate search results. The reason why is that AI-powered tagging tools tend to notice even the smallest apparel details and synonyms. So, even if a browser’s search query doesn’t precisely align with the wording in your product catalogue, AI-powered tags will allow them to see the item they’re looking for anyway.

Another advantage of automatic fashion label tagging tools is that they can tag your inventory in real-time. Customers can therefore see new or restocked items as soon as they become available. If you stock the same items as other retailers, this can give you a competitive advantage as shoppers will know to check your site first.

Generating Fashion Label Tags Just Got Easier

Great fashion label tags are the foundation of any successful fashion business. It doesn’t matter how sleek your online store is or how amazing your marketing campaigns are. If you fail to show customers the items they’re looking for or if you keep stocking items that no one wants, making it in today’s highly competitive environment is going to be an uphill struggle.

However, while having relevant product attributes is crucial, that doesn’t mean that your team has to spend hours trying to get it right. Luckily, there are plenty of tools out there that can do all the hard work for you — and do it better.

Intelistyle is one such tool, and it comes with many other benefits. Check out what else we have to offer, and get in touch with us today to talk about how we can help you bring customers into your store in 2021 and beyond.

Book a free consultation

The post How to Generate Fashion Label Tags for Your Store’s Clothes appeared first on Intelistyle.

Fashion Machine Learning Applications and Examples

Laura — Thu, 11 Nov 2021 17:58:41 +0000

Fashion Machine Learning Applications and Examples

In 2018, Reuters reported that the Spanish apparel retailer Zara was incorporating artificial intelligence (AI) into its business strategy and supply chain as a way to keep up with and beat its competitors. Coincidence or not, but the following year, Zara’s parent company Inditex posted strong profit growth. And even though 2020 — the year the COVID-19 pandemic shut down the world — was tough for everyone, so far, Inditex sales have topped pre-pandemic levels.

Of course, Zara isn’t the only fashion retailer leveraging AI and machine learning (ML) to stay ahead of the curve. Tommy Hilfiger, H&M, and Dior, as well as many other brands, some of them fast-fashion and others haute couture, are doing the same. Most recently, the clothing and accessories retailer GAP acquired the AI startup Context-Based 4 (CB4), which it hopes will help improve its customer experience by enhancing predictive analytics and demand forecasting.

According to a 2021 study by Juniper Research, 96% of surveyed retail executives plan to invest in AI in the near future. What’s even more interesting is that an NTT DATA and Oxford Economics survey found that 40% of executives think that AI is crucial to business success and that a failure to implement the technology will end up costing them their customers and employees, as well as hurt their bottom line.

Seeing these numbers, many retailers may feel compelled to explore how they can apply AI and ML to their business operations — and with good reason. From trend forecasting to personalised recommendations to automated customer services, the use cases for fashion machine learning are many and varied. Here are just some of them.

Designing and Manufacturing

Today, it is not unusual for a fast-fashion brand to have around 52 “micro-seasons” in a year. This means that, on average, customers can expect their favourite brand to drop one new collection a week.

Coming up with this many new apparel designs and then actually making them in a timely manner can be a real challenge for designers and manufacturers involved — especially considering that most fashion items can take anywhere between 6 months and a year to develop. And for retailers, the trend-driven nature of fashion can make estimating customer demand for their new line of products tricky, to say the least.

So it comes as no surprise that an increasing number of fashion companies are turning to AI and machine learning for help with the process:

AI-driven demand forecasting has been proven to reduce forecasting errors by up to 50%.
“AI designers” can analyse popular styles and design elements, such as cut and fabric, as well as their past retail performance and popularity on social media, to create completely new designs from scratch.
AI-enhanced garment manufacturing leads to improved efficiency and time-to-shelf due to automated quality control and defect detection.

Retail Strategy

One of the worst things that can happen to a fashion retailer is ending up with unsold stock. Unfortunately, this is a far more common occurrence than most people may realise.

In 2018 H&M’s lengthy supply chain lead times coupled with the brand’s inability to understand consumer preferences left it with $4 billion of unsold inventory. To overcome this issue, the multinational fashion retailer turned to machine learning.

Having analysed store purchase history at one of its stores in Stockholm, the company realised that its inventory stocking strategy was severely lacking. For example, in this particular store, H&M was stocking basics for men, women, and children — even though its customer base was mostly women. By removing its menswear line and instead focusing on stocking more trendy items for women, the store was able to reduce its stock by 40%. No doubt H&M replicated this same experiment across its other stores, too. With intelligent buying and merchandising planning, you can do the same.

Besides informing assortment, AI and ML can also help brands come up with the best product and pricing strategies based on what their competitors are doing.

Supply Chain Management

ML and AI can also make the supply chain more efficient, agile, and sustainable.

For instance, by highlighting the best and worst performers in real-time, AI and ML allow brands to stop the production of items that no one wants and increase the production of garments that are trending in real-time. This can seriously reduce the likelihood that a brand will end up with dead stock.

AI and ML can also improve shipping times. Take Amazon’s “anticipatory shipping,” which was patented in 2013, as an example. By utilising predictive analytics tools and Amazon customers’ data, the tech giant can pre-ship products a customer is going to order (but hasn’t already) to nearby warehouses. Then, when a customer actually purchases the item, they can receive it much faster than if it was coming from a traditional fulfilment centre.

Some brands are also using AI to determine suppliers, modes of transport, and delivery routes that are the most sustainable.

Product Discovery

The internet has provided customers with access to thousands of online stores, and in consequence, millions of products. However, as the paradox of choice theory teaches us, more is not always better.

One study found that the more shoppers search on an online clothes store, the more weary they grow, and the more likely they are to take a break from online shopping. Crucially, these breaks can last up to a week and may not always result in a sale.

For fashion retailers, then, helping customers navigate their product catalogue and surfacing the most relevant results is non-negotiable.

One way AI and ML technologies can make product discovery easier is by augmenting search. This could be through automating product tagging, a feature that enriches products with detailed and specific attributes (such as their colour, fabric, or print) and allows customers to find items that match precisely what they’re looking for.

Or, it could be through natural language search, which means that shoppers can search for items using the language they speak in their everyday lives.

For example, AI and ML are integral to Amazon’s “contexts of use” system, which helps Amazon understand the intent of a customer’s search query. Thanks to this system, Amazon can know whether a person is looking for “waterproof shoes” because they’re planning on going hiking or because they need to keep their feet dry when walking to the subway on their way to work, and can then surface relevant results.

For customers that may struggle to put into words what they want, visual search can be invaluable. AI-enabled visual search can identify all the products in a photo uploaded by a customer and then show items from the retailer’s product catalogue that best match them.

Fashion retailers can also use AI and ML to personalise recommendations on product pages.

When customers click on a product on Lane Crawford’s webpage, they are shown not only the item they’re interested in but also garments that are visually similar to it. That way, even if the customer doesn’t love the item they clicked on, or it’s out of stock, their product discovery journey doesn’t end — they have other, highly relevant options to explore.

For shoppers that may really like an item but struggle to come up with ways to style it, Lane Crawford offers “Complete the Look” suggestions. Not only can this inspire the customer to buy the item they originally looked at, but it can also motivate them to purchase items that go well with it, thus increasing their overall basket size.

Tommy Hilfiger takes this a step further by giving customers styling options based on different occasions. And Stich Fix, everyone’s favourite personal styling service, has developed an algorithm that generates outfit suggestions based on a customer’s own unique style.

Physical stores can also offer customers personalised recommendations, and not necessarily through sales associates. Rather, retailers can install smart mirrors that recommend outfits based on the style of the clothes that the customer is currently wearing. Customers could also use these smart mirrors to scan an item they come across to see how they could style it with other garments in the store. In the fitting room, if an item does not fit as expected, customers could potentially use smart mirrors to get alternative suggestions and have the items brought to them directly.

However, retailers don’t have to wait for customers to visit their site or store to show them personalised product recommendations. Instead, they can use personalised promotional and retargeting campaigns to help them complete a look or bring their attention to items they are most likely to engage with.

Nordstrom, for example, used ML to come up with an “inferred scoring” system based on data it collects from its customers (such as clickstreams, purchase data, and email campaigns) to show them items they most likely want or need. This technique improved Nordstrom’s marketing campaign effectiveness and grew its email-to-dollar conversion by a whopping 25%.

Fashion Machine Customer Service

A customer representative may not always be around to help, but by “employing” AI chatbots, retailers can be certain that their customers will always be taken care of.

In 2017, Dior launched “Dior Insider,” an AI chatbot that customers can enable through Facebook Messenger, and that can answer Dior product queries and share the latest Dior news.

That AI chatbots can be incredibly powerful is illustrated by Alibaba’s “AliMe” chatbot. During “Single’s Day” in 2017, AliMe answered 9 million queries and generated 56.7 billion personal recommendation shopping lists for shoppers. And in 2018, AliMe was upgraded to provide:

Pre-sales promotion (product recommendations and intelligent shopping reminders to finish transactions).
During sales and post-sales services (changing addresses and appointing logistic vendors).
Intelligent shopping assistant (AliMe can read a customer’s emotions to alert human customer support agents).

It was thanks to AliMe that Alibaba made an impressive $31 billion on Single’s Day in 2018,

Chatbots are not limited to a retailer’s website or Facebook. They can also be integrated into instant messaging apps like WhatsApp, WeChat, and iMessage.

Fashion Machine Learning: Where to Go From Here

All the big fashion brands are jumping on the AI and ML bandwagon. However, there is no reason why smaller brands can’t integrate the technology into their operations, either.

For more examples of how fashion businesses can use AI, check out our comprehensive whitepaper on the topic.

Ready to start using AI and ML? Whether you’re looking to personalise customer experiences or take advantage of advanced buying and merchandising reports, we can help.

Book a free consultation

The post Fashion Machine Learning Applications and Examples appeared first on Intelistyle.

How to Select the Right Product Discovery Platform

Laura — Fri, 22 Oct 2021 09:29:05 +0000

How to Select the Right Product Discovery Platform

Discovering and buying products has never been easier. Whereas purchasing something once meant having to leave your home and visit a physical store — or, in all likelihood, multiple stores — today, shoppers can browse hundreds of shops and buy whatever it is they want without so much as getting off their couch. Isn’t that amazing? In theory: yes. In practice: not so much.

As Harvard Business Review wrote all the way back in 2006, “more isn’t always better.” When customers have too many options, they don’t feel lucky to be able to choose. Instead, they feel overwhelmed and stressed. Case in point: more than half of consumers admit to having abandoned purchasing something from a brand because they couldn’t choose between all the options available to them.

For online retailers looking to overcome this paradox, having the right product discovery platform in place is a must.

What Is Product Discovery?

Product discovery is exactly what it sounds like: the process of shoppers discovering products. Although the process will vary from customer to customer based on their needs at that moment, it generally falls within one of the following categories:

Casual browsing, where a customer is not looking for anything specific but rather that “aha” moment for purchase inspiration.
Searching, where a customer has a fairly good idea of what they want to buy. For example, they may know that they want to purchase a pair of mom jeans. However, they may not necessarily have a specific product, like “Levi’s high loose taper fit women’s jeans,” in mind and may need some help narrowing down their options.

In either scenario, the key to ensuring that customers find — and buy — something from you is to show them the right products at the right time.

How you do this (through merchandising, reliable search functionality, or recommendation engines, for instance) will depend entirely on your strategy, objectives, and tools at hand. Nevertheless, every successful product discovery experience should include at least some level of personalisation. In today’s competitive e-commerce world, retailers need to cater to every customer specifically.

It’s also crucial to note that product discovery is not limited to a customer’s first search, either. The average consumer has about three touchpoints with a brand before they make a purchase, so retailers should optimise the entirety of a customer’s journey for better discovery. From personalised recommendations on a product page to highly customised and relevant retargeting emails, the product discovery experience should not end when a customer types in their search query into an online store’s search function and is brought to a results page.

What Is a Product Discovery Platform?

A product discovery platform is a platform that makes the process of product discovery quicker and easier for customers. The reason for that is that by using a product discovery platform, online retailers can expose customers to the products that they will actually like and want to purchase.

3 Things Every Effective Product Discovery Platform Needs to Have

Not every product discovery platform is created equal. Here are three things that retailers should make sure the product discovery platform they’re looking to invest in provides:

1. Advanced search features

For customers that come to your online store to find something specific, the importance of having great on-site search functionality cannot be understated.

Research shows that as many as one-third of all online shoppers ignore the navigation menu and instead focus on a site’s search functionality. Notably, between 30% and 60% of site transactions result from sessions within the search function.

Unfortunately, despite advances in technology, many online stores continue to provide a poor site search experience. According to a 2018 Marketing Charts study, customers’ three biggest frustrations with retail site search include:

Irrelevant product results (28%)
Inability to find the item they’re searching for (24%)
The search function doesn’t recognise the words used (18%).

The good news is that all of the above can be solved with a product discovery platform that comes with advanced search features.

Take product tags, or product attributes, as an example. Product attributes, the unique features that make items stand out (like their fabric or cut), are an essential part of e-commerce because they allow customers to filter their search using specific characteristics (i.e., “denim dress” instead of just “dress.”) Yet too many retailers still tag their products manually. Not only is this time-consuming, but it can also lead to costly mistakes where products are tagged inappropriately, and customers never see the items they’re searching for. An easy fix is to automate product tagging, increasing the accuracy of customer search results while also improving your bottom line.

However, using AI to automate your product tags doesn’t necessarily mean that your customers will find everything they search for. The fact is that 61% of e-commerce sites won’t return results unless the user types in the exact same product type jargon used by the site, and 27% won’t show results if a user misspells just one character. To make sure that prospective buyers don’t encounter the same problems on your site, you need to optimise your on-site search relevance with a natural language search. Doing so will allow shoppers to carry out their searches using their everyday vocabulary.

Finally, not every customer will be able to (or want to) put their search queries into words. Visual search, a feature that surfaces similar products to the ones that appear in images uploaded by customers, is already a growing trend, and it’s only going to become more critical in the future. Already, 36% of consumers have used visual search when shopping online, with surveys showing that this feature is most commonly used when shopping for clothes.

2. Personalised recommendations

With a monthly traffic average of 3.68 billion users, Amazon was the most visited online retail website worldwide in 2020. To put that into perspective, eBay came in second, but its traffic was “just” one billion, and Rakuten, a Japanese electronic company, was third with less than 800,000 monthly visitors.

So what makes Amazon stand out? Why do most shoppers turn to Amazon when they need to buy something? While competitive pricing, fast shipping, and excellent customer service certainly have something to do with it, at least part of the company’s success can be attributed to its personalisation efforts. More than 1 in 2 internet users recently ranked Amazon as the platform with the best personalised experience. Significantly, almost the same number of marketers said that customers spend more money with a brand that uses personalisation. That makes sense. When customers are shown products that are relevant to them, they are more likely to buy them and return to the store that “understood them” later down the line.

Personalising the product discovery experience is, therefore, essential. Luckily, there are many ways you can do so, and the best product discovery platform will provide multiple customisation options, including:

Personalised product ranking. The order in which you rank your products doesn’t have to, and shouldn’t be, random. Instead, you should use AI to display items from your inventory that the customer is most likely to be interested in and buy.
Visual similarity recommendations. When a customer clicks on a product page, that doesn’t mean that they’ve come to the end of their product discovery journey. They may like the product but want something slightly different, or their size may be out of stock. By showing shoppers multiple products that are visually similar to the item they’re viewing but for some reason can’t or don’t want to buy, retailers can reduce lost sales and prompt customers to continue exploring their product catalogue. For example, while the “Denny Vegan Leather Blazer” by ALICE + OLIVIA is not yet available on Lane Crawford, users can see items that are very similar right underneath it.

Complete the look recommendation. By offering complete outfit recommendations under their products, retailers can encourage customers to buy items that they may otherwise find difficult to style at the same time increasing their basket size.
AI styling advice. Make your online customers feel special by providing a VIP service that uses AI to hyper-personalise recommendations for each individual.

Fashion chatbot. Available 24/7, a fashion chatbot can make product recommendations and offer styling advice before, during, and after a sale both on your e-commerce site and social media.

3. Mobile and email retargeting campaigns

Online retailers can’t rely on customers coming to them during their product discovery experience, especially once they’ve moved beyond on-site search. Rather, they have to meet customers where they are, regardless of whether that’s social media or their email inbox.

An effective product discovery platform will allow retailers to do just that. For example, with Intelistyle, e-commerce brands can run successful email and WhatsApp campaigns that:

Offer customers personalised recommendations or even personal lookbooks that match their style and past purchases rather than promoting more of the same product they have already bought.

Give them discounts on items they have in their cart based on their previous behaviour.
Let them know when an item they had been eyeing comes back in stock.

Intelistyle: The Only Product Discovery Platform You Need

It is estimated that there are up to 24 million e-commerce sites online, with most online stores boasting hundreds, if not thousands, of items. It’s no wonder then that customers often feel swamped with options to the point where they have to take week-long breaks or “search gaps” to recuperate before making a purchase.

To cut this information overload and stop shoppers from leaving their store empty-handed, smart online retailers need to refine their customers’ product discovery experience. Doing so isn’t hard, nor is it time-consuming — that is, as long as you are working with the right partner.

In this blog, we outlined three things every effective product discovery platform should have. Intelistyle has all of the features we talked about, plus much more. Ready to improve your product discovery? Talk to us today.

Book a free consultation

The post How to Select the Right Product Discovery Platform appeared first on Intelistyle.

What Is Searchandising and How to Get It Right

Laura — Fri, 08 Oct 2021 12:16:22 +0000

What Is Searchandising and How to Get It Right

E-commerce is still on the rise. Last year, more than two billion people bought goods and services on the internet, with online retail sales exceeding $4.2 trillion worldwide. Although these are already huge numbers, if expert predictions are anything to go by, online sales will only keep growing.

For existing online retailers, these may seem like terrifying statistics. After all, no one wants more competition. But if you play your cards right, that won’t be an issue.

Although more brands are selling online than ever before, in many ways, the customer experience they offer hasn’t improved. While most shoppers visiting an online store expect to be able to find products they’re looking for with ease, the reality is often very different. Customers frequently can’t find the product they want to buy, or it takes them too long to locate the item they like because they are bombarded with totally irrelevant search results.

The good news is that you don’t have to be a mind reader to show your customers the items they want to see — and buy. All you have to do is utilise a growing trend in e-commerce: searchandising.

What Is Searchandising?

Searchandising, or search merchandising, which is what the term stands for, is the practice of promoting specific search results via a website’s search function to achieve business goals.

Although the term is still fairly novel in e-commerce, in brick and mortar stores, retailers have been trying to guide shoppers’ attention to the products they want them to notice and purchase for decades. Curating search results through searchandising allows online stores to do the same.

With searchandising, retailers can do two things:

Display products that are relevant to a user’s search query, helping them find whatever it is they’re looking for faster. So, if a shopper searches for “black dress pants,” they are shown only black dress pants (not also, for example, black dresses).
Incorporate promotions and items into searches that align with business goals, like highlighting trending black dress pants or dress pants with higher profit margins.

From faceted search to auto-complete to recent and related searches, searchandising can comprise many elements, some of which may be manually set while others may be algorithm-driven. For instance, retailers may manually set boost-and-bury rules depending on stock availability, margin, and other attributes. At the same time, they may also use behavioural data and AI to automatically sort products based on personal taste.

Why Is Searchandising Important?

For many shoppers, the search-site function is far more critical than the navigation menu.

Research shows that about half of users go straight to the search function on an e-commerce site, and buyers are more likely to use this function than browsers. The reason why is that people who use the search function tend to be at the later stages of their “buyer’s journey.” They have done their research, and they know exactly what they want. For them, all that’s left to do is actually find the item they’re looking for.

The search box promises quick and relevant results. But it doesn’t always deliver, which often leads to these highly qualified buyers moving on elsewhere.

The fact is that most customers cite irrelevant product results as their top frustration. Unsurprisingly, about a third of visitors leave a site if they can’t find the product they’re looking for. They’re probably not going to return, either.

The opposite is also true. When browsers see relevant search results, they are not only more likely to convert but also to come back and recommend your store to others. The result is loyal customers and higher lifetime value.

For retailers, searchandising makes it easier to adapt to business objectives by boosting or hiding products for a particular term.

How to Make the Most Out of Searchandising

When it comes to searchandising, there are many strategies you can use. Here are some of the most effective ones.

1. Automate product tags

If a customer can’t see it, they won’t buy it. Seems simple enough, yet many brands still fail to successfully implement the one thing that makes product discovery easier: product attributes.

A unique set of features and characteristics, product attributes allow customers to filter their search by things like fabric, cut, or print, which leads to more accurate results and a higher chance of a sale.

While the vast majority of retailers understand the importance of product attributes, many still tag products manually. Unfortunately, this can often result in costly mistakes where an employee tags a product incorrectly, making it difficult for a buyer to find what they’re looking for.

Take a look at the below red and white striped t-shirt by Monki. If the product had been tagged incorrectly, it probably would not have surfaced when a user typed in “striped t-shirt.” If a t-shirt like this is precisely what a customer is looking for and they can’t see it, the next place they try might well be your competitor.

Automating product tagging leaves no room for mistakes. It also lowers costs, improves processing times, and allows staff to focus on more important tasks.

2. Keep it relevant

A customer’s searches may not always be specific. This is where Natural Language Processing (NLP), a computer program’s ability to understand daily human language, comes in. Retailers can use NLP to bring up results that match what the customer is looking for, even if their intent is not very clear.

To take this a step further, retailers can combine NLP with autocomplete to correct misspellings and typos, detect synonyms, and identify word importance (for example, if someone searches for a “white dress-shirt,” they probably don’t want to see dresses or shirts, but rather dress-shirts).

Notice how when you search for “soks” on Free People, you still get results for “socks.” The algorithm is smart enough to know that a customer made a typo.

3. Use search rules

Search rules allow retailers to determine the order in which relevant results will be shown to customers. By boosting or burying specific results, you can promote certain types of products based on things like:

Availability: Boost items that are in stock or slow-moving and excess products and bury items that are low-in-stock or out-of-stock.
Time of the year: Display seasonal items.
Profit margin: Show products that will increase your revenue.
Popularity: Highlight items that are popular with other customers based on the number of clicks, favourites, and sales.
Release date: Show newest items first and bury products that are discounted and therefore not as profitable to sell.
Product ratings: Boost products that get great buyer reviews.
Brand: If you sell multiple brands and have your own brand products, you may want to display these first.

Notice how one of the first results for “t-shirt” on RedBubble during October is a ghost shirt because the query was carried out close to Halloween:

As time goes on and you figure out what works and what doesn’t, you may want to optimise and refine some of the search rules you’ve implemented.

4. Make it personal

Even if you show browsers items that match their intent, there is only so much time they will spend scrolling through a search results page. That means that if the item they’re looking for is at the bottom of the page, they may never actually see it. As such, it is essential that retailers personalise product rankings.

Using AI, retailers can make better guesses about what each customer wants to buy from a retailer based on their previous behaviour and preferences. They can then display search results in a personalised order while also taking into account factors like margins and depth of stock.

For example, if a customer previously bought a jacket from a specific brand, next time they’re searching for a dress on that same e-commerce site, it would make sense if they were shown dresses from the brand the retailer already knows they like.

Personalised recommendations don’t necessarily have to appear on just the search results page, either. Massimo Dutti displays trending products based on visitors’ browsing history as they’re about to start typing in their query:

5. Promote it

Retailers can also promote other related products or offers by displaying promotional banners on the search results page.

Asos does this really well:

6. Go visual

Browsers may not always know how to describe what they want. In this scenario, having a visual search function can be helpful as it allows users to search for items using pictures of clothing they like.

AI can identify clothing in a photo uploaded by a searcher to bring up the closest match for each from the retailer’s inventory.

Here are the search results H&M returns after a user uploads an image of a graphic tee they like:

7. Add badges

Promotional badges are a powerful tool in communicating something to customers without altering the search results. Retailers can use badges to announce things like:

New products
Trending items
Discounts
Limited availability
New colours or sizes

Urban Outfitters uses badges to promote top-rated items:

8. Don’t disappoint

You may not always have the exact item the customer is looking for in stock, but that doesn’t mean that you should show them a “no results” page that has little else on it. If you do, you are bound to lose a sale.

Although typically considered bad for business, the “no results” page can also be an opportunity, as long as you use it wisely.

For example, while Zara has no results for “fleece jumper,” it suggests that users check out results for “jumpers” or “fleece” instead.

Retailers could also use the “no results” page to showcase selected favourites or recommend similar items the user may like.

Searchandising Is Here to Stay

You’d think that with so many retailers going online in the last few years, the e-commerce search experience would have improved. However, other retailers’ loss is your gain. As demonstrated above, searchandising is not difficult to implement and immediately makes your customer experience stand out.

As competition heats up, e-commerce stores that make it easy for customers to find what they’re looking for while also meeting their own objectives will inevitably thrive. On the other hand, retailers that continue to surface irrelevant results will undoubtedly find themselves in hot water.

To future-proof your retail business, get in touch with Intelistyle today. A leading AI fashion platform, Intelistyle can help you implement some of the most essential searchandising techniques, including product attribute automation, Natural Language Processing, and personalised product ranking, among other things.

Book a free consultation

The post What Is Searchandising and How to Get It Right appeared first on Intelistyle.

The Ultimate Guide to Fashion Digital Transformation

Laura — Mon, 04 Oct 2021 13:19:57 +0000

The Ultimate Guide to Fashion Digital Transformation

In fashion, just like in almost every other sector, the COVID-19 crisis has supercharged digital transformation. “We’ve accomplished two years of digital transformation in two months,” said an executive of a leading apparel company in a McKinsey & Company report.

Even before the pandemic, increasing globalisation meant that some fashion companies were already investing more than ever in their digital capabilities. However, COVID-19 induced lockdowns and the closure of many physical stores turned digital transformation from a future goal into an immediate necessity.

For most fashion brands, the choice was simple: digitise or perish. The stats make this clear.

At the start of the pandemic, consumer purchase intent decreased by between 70% to 80% in physical stores in both Europe and North America, according to the above-mentioned McKinsey & Company report. Although e-commerce did not quite make up for this sharp fall in offline sales, online sales did help most companies avoid going bankrupt — and reach new customers. With most people stuck at home, the pandemic prompted consumers to try out new buying channels.

Surveys show that almost half of the consumers who said they didn’t buy fashion items online before the crisis have since started doing so. Most customers have grown so used to shopping online that they expect their e-commerce activity to stay the same or increase, even as things return to normal.

Unsurprisingly, brands that may have been wary of investing in new technologies are now eager to adopt digital solutions to harness these changing customer trends. As a result, understanding how digital technology is transforming fashion is now critical for every brand.

What Is Fashion Digital Transformation?

Fashion digital transformation describes how fashion brands use digital technologies to improve existing business processes and customer experiences or create new ones.

At the most basic level, digital transformation could mean something as simple as launching an eCommerce store. Ideally, however, brands should digitalise their entire value chain, from planning to shelf rather than digitalising specific processes or areas (like sales or marketing).

Done right, digitalisation can help fashion companies to find new ways to acquire customers, predict and manage inventory, and enable new logistics and sales fulfilment options (such as “click and collect,” a delivery option that has skyrocketed in popularity during the pandemic).

Why and How Fashion Digital Transformation Works In Practise

How can you implement a digital transformation, and why would you want to do so in the first place? Here are some of the benefits of digitalisation in fashion and how AI can help you get there.

Put the customer at the centre through personalisation

Thanks to digital technologies, consumers have gone from passive observers to active participants in the fashion world. They no longer want to just buy items. Instead, they want to interact with, influence, belong, and even be their favourite brands.

Part of putting the customer at the centre is making it easier for them to find and buy products from the brands they love. Retailers can accomplish this by shifting from a “you found me” to a “we found you” mentality, advises Karin Tracy, Head of Industry at Facebook and Instagram.

Brands should also remember that the customer journey does not end when a customer buys something. To ensure that shoppers return time and time again, retailers need to connect with them on a personal level. This means figuring out where their fashion friction points are and using data to remove them.

Whether that means personalising product ranking, showing relevant discounts and offers, or communicating with customers on platforms like WhatsApp will depend on the brand. Whatever they do, retailers need to remember that hyper-personalisation is the way to go. From homepages to product recommendations to email campaigns, customers want to feel like brands care about them as individuals.

For brands that have offline premises, digital transformation should extend to in-store locations, too. Examples of how brands can do this include using remote clienteling to provide a VIP fashion consulting service to every customer and offering shoppers personalised garment and outfit recommendations through smart mirrors.

Reduce mistakes and give back employees time by automating mundane tasks

Despite advances in technology, the way some fashion retailers carry out specific tasks is woefully outdated.

Take product attributes, for example. Although they can make or break the customer experience, many retailers still tag products manually. This both takes time and can lead to costly mistakes that may be hard to remedy. For example, if an employee doesn’t match all product categories or makes a mistake, the chances that a customer won’t find what they’re looking for in a shop are unfortunately very high.

Automating product tagging doesn’t just reduce the likelihood of human error. It also allows staff to focus on other, more important tasks.

Improve time to market by optimising your workflow

The traditional process of designing a garment and then getting it to a shop floor is lengthy and typically involves multiple stages. AI can speed this process up by:

Making 3D apparel designs and sewing patterns.
Automating quality control, including pattern inspections and defect detections.
Detecting best and worst-selling items ahead of time. This allows retailers to stop the production of products that remain on the shelves months after being introduced and increase the output of bestsellers.

By increasing their time to market, brands can seriously improve both their competitive advantage and environmental impact. Although an extreme example, no consumer wants to read about their favourite brand burning unsold stock.

Make better decisions through predictive AI

Fashion is a fast-moving space, so it’s not always easy to know what will sell and what won’t. Unsurprisingly, this can make the design process extra challenging. However, by using actual data and insights, designers can understand what design elements, such as colour and cut, will likely appeal to customers based on their past and present performance.

The same challenge applies to buying and merchandising teams. Here again, buyers can take advantage of predictive analytics and trend forecasting to base their buying decisions on:

Products that are selling, plus their attributes like prints and sleeves
Changing trends
Potential future fashion trends based on data from social media, e-commerce, and the runway combined with data on consumer behaviour and past performance

Increase your bottom line by reducing excess or obsolete inventory

Not every customer is willing to pay the same for the same product.

With AI, operations teams can discover the geographical areas where their products will be most appreciated. AI also allows teams to decide on an appropriate pricing strategy, automatically taking competitor pricing into account.

With a product-pricing mix that makes sense, brands can put the nightmare of dealing with excess inventory or offering heavy discounts behind them.

Fashion Digital Transformation: Join In or Risk Being Left Behind

With thousands of chain stores closing in 2021 alone, it’s fair to say that the COVID-19 pandemic hit brick and mortar stores particularly hard.

However, while the last year and a half have been particularly tough for the fashion industry (Primark CEO said the offline-only retail company went from making £650m each month to nothing), surviving the last 18 months doesn’t necessarily guarantee business longevity either. Future crises could well arise, and businesses that did alright this time round may suddenly find themselves underwater.

Digital transformation can help overcome existential threats — but only if brands see digitalisation as more than just launching an eCommerce website. In particular, personalisation is a huge trend that is only going to grow in importance. Brands that customise their messaging to each customer will thrive, crisis or no crisis.

While digital transformation may seem like an overwhelming process, companies should see it as a long-term effort that can take up to several years to achieve. Having a clear road map with concrete milestones is vital. On the other hand, focusing on transforming areas where you will see the most impact first, such as personalising the customer experience, can lead to quick wins and sustained progress.

Ready to digitalise your fashion business? Get in touch with the expert team at Intelistyle today.

Book a free consultation

The post The Ultimate Guide to Fashion Digital Transformation appeared first on Intelistyle.

How Hyper Personalisation Can Increase Conversion

Laura — Thu, 23 Sep 2021 08:14:49 +0000

How Hyper Personalisation Can Increase Conversion

Personalisation is not a new concept. Brands have long known that tailoring their offerings and communications towards individuals can dramatically improve customer retention and increase conversions. More than 9 out of 10 companies saw 3x ROI and increased profitability after implementing personalisation.

As a result, almost every successful brand in existence today has some kind of personalised marketing strategy in place. Features like customised subject lines or customer names in emails are now expectations rather than exceptions. However, with basic personalisation now ubiquitous, the downside is that boilerplate personalisation alone is no longer enough for a brand to gain an edge over its competitors. Instead, to stand out and boost ROI, marketers need to evolve how they use personalisation. This means embracing hyper personalisation.

A more advanced form of personalisation, hyper personalisation is the new standard in customer-facing marketing. From banking to hospitality, every sector is taking advantage of this new trend. Because they rely on staying ahead of the same consumer trends that shape these industries, fashion retailers can’t afford to be left behind either.

What Is Hyper Personalisation?

Traditional personalisation looks at a customer’s personal data, such as their name, demographic details, and purchasing history. Hyper-personalisation goes a step further by taking into account a customer’s data in real-time. This is usually done via big data sets and AI algorithms.

Using data, AI, analytics, and automation, companies can provide customers with hyper personalised experiences, i.e., contextualised messages directed at specific individuals at the right time and on the best channel. This means that brands can send customers emails that correlate exactly to their specific actions and interests. For example, Amazon frequently sends its customers emails recommending products based on items they recently viewed or bought on Amazon’s site.

However, Amazon’s hyper personalisation efforts extend beyond just email. The tech giant also personalises on-site content based on who is visiting the site. Here is what a returning visitor may see when they scroll down on Amazon’s homepage:

Another excellent example of a company using hyper-personalisation to its full potential is Spotify.

Last year, Spotify updated its home screen to show users the content they’re already familiar with (like their workout playlist) on the top and recommendations on the bottom. Crucially, the content on the top changes as the day goes on to match user interests and activities. So, if a user always listens to a specific podcast in the morning, that’s where they’ll find it first thing when they get up. On the other hand, their workout playlist won’t appear on their home screen until later in the day or whenever they’re most likely to exercise.

Other hyper personalised features that Spotify has introduced over the years include:

“Discover Weekly”: A personalised list of songs that fit a user’s profile.
“Spotify Wrapped”: A yearly round-up of a user’s listening data.
“Only You”: A feature that gives users a musical astrology reading based on their listening tastes.

Why Hyper Personalisation Matters

There are many reasons that make hyper personalisation an essential ingredient in retail. Below are just some of them.

It meets customer expectations

Research shows that consumers find impersonal shopping experiences frustrating. Fortunately, the opposite is also true. Personalised shopping experiences delight consumers to the point where they will almost always choose a brand that tailors their content specifically to them versus one that doesn’t.

However, while shoppers crave customisation, the vast majority of brands still fall short in meeting, let alone exceeding, their demands. According to one study, only 22% of customers are happy with the level of personalisation they receive from companies they buy from.

By hyper personalising their offerings, brands can show customers that they understand and care about their unique needs and wants.

It fosters loyalty

Customers appreciate brands that remember who they are. Almost one in two consumers say they are likely to shop again with a brand that provided them with a personalised experience.

The odds of a customer leaving a good review, telling their friends and family about their experience, and commenting positively about a brand on social media are also higher following a customised experience. That is because experiences that are tailored to a specific shopper put that shopper at the centre of the experience and give them more control over the interaction.

It boosts sales

One of the best things about hyper personalisation is that brands can figure out what customers desire before customers can do so themselves. This drives impulse purchases, which in turn increases sales and leads to higher revenue.

In one study, almost 1 in 2 consumers said they bought something they were not expecting to buy because of a great personalised recommendation.

The downside of impulse purchases for retailers can be a higher rate of returned items. Not with hyper personalised recommendations, however. Only 5% of all impulse purchases triggered by personalised recommendations are returned, and 85% of impulse shoppers are happy with the products they purchased.

It dramatically increases marketing ROI

When brands stop wasting customer time on products they’re not interested in, customers can focus on items they actually care about. Unsurprisingly, brands that use advanced personalisation, mainly real-time personalisation (aka hyper personalisation), can expect a $20 return for every $1 spent.

It reduces costs

Brands that keep showing e-commerce store visitors products they’re not interested in will inevitably lose customers. In contrast, brands that tailor the shopping experience to every single visitor will gain customers — at a fraction of the cost. Research has shown that personalisation can slash customer acquisition costs in half.

How Fashion Retailers Can Hyper Personalise the Customer Experience

To create better shopper experiences through hyper personalisation, retailers should prioritise hyper personalisation within the following areas of their customer journey:

1. Websites

Some of the ways brands can hyper personalise their websites include:

Personalised homepage. From previously viewed items to customised bestsellers to relevant discounts, the content on the front page can either draw a customer in or make them leave a brand’s webpage within seconds.
“Complete the Look” and “Visually Similar” prompts. Personalised recommendations such as these can account for almost half of what customers buy.

Visual search. Customers may not always know how to describe what they’re looking for, but they may have a photo of the specific item they want to buy. Visual search lets them do just that. By using visual search, customers can see the right results for them instantaneously. Crucially, as they interact more with the brand, the results returned by the visual search function will become more personalised.

Personalised product ranking. No customer wants to waste their time scrolling through products that are irrelevant to them. With AI, brands can identify the items a shopper is most likely to buy and prioritise these products over other items.
Fashion chatbot. Available on a brand’s site as well as social media, AI chatbots offer 24/7 personalised services and can answer specific customer queries in real-time.

2. Loyalty programs

Just because you have a rewards program doesn’t mean that it’s working for you.

In one study, 96% of people said that loyalty programs could be made better. A further 75% admitted that they would switch to a different brand if offered a better loyalty program. Obviously, none of these people had ever had the pleasure of participating in a hyper personalised loyalty program.

Through AI, brands can improve their understanding of each customer that shops with them and tailor their recommendations and rewards accordingly.

3. Retargeting and promotional campaigns

Wouldn’t it be amazing if instead of sending customers retargeting campaigns that recommend a similar product they’ve already bought or looked at, brands could offer every shopper access to exclusive styling services?

With Intelistyle’s AI Styling Platform, this is not only possible but incredibly simple — and cost-effective.

Our AI Styling Platform creates outfits that suit the needs and desires of each shopper, taking into account their past behaviour, preferences, style, season, and predicted purchasing behaviour, scaling fashion advice to previously unseen levels:

These outfits can then be shared with customers via email. Alternatively, brands could also add them to the customer’s shopping basket online or even send the custom outfits in a fashion concierge styling box.

The same is true for promotional campaigns. AI can hyper personalise recommendations based on factors such as product categories a customer is most interested in, time of the year, and offers that are likely to pertain specifically to them.

4. Social media

For tailored brand experiences on social media, AI-powered chatbots are hard to beat.

With fashion chatbots, brands can stay in touch with customers before, during, and after a sale — regardless of where their customers hang out. And considering that there are about 3.78 billion social media users worldwide right now, many of them can be undoubtedly found on social networking platforms like Facebook as well as instant messaging apps such as WhatsApp.

Besides answering customer queries and arranging exchanges and returns, AI chatbots can also notify customers when items come back into stock, make personalised product recommendations, and offer styling advice.

AI chatbots can even facilitate direct sales by taking pre-orders and reserving items for collection in-store.

5. In-store

Customers don’t just expect a more personalised experience from e-commerce stores. They want to see in-store retailers offering more intuitive and tailored experiences, too.

One simple way to achieve hyper personalisation at a physical store is by adopting smart mirrors.

On the shop floor, AI-powered smart mirrors can scan a customer and make hyper personalised outfit recommendations based on the clothes they are already wearing. Customers can also scan a particular product they like to see how the smart mirror would style it with other products currently available for sale in the shop.

In the fitting room, shoppers can use a smart mirror to find products similar to what they are trying out but that doesn’t quite fit and have them brought directly to them.

If the customer doesn’t have to go through the hassle of leaving the fitting room to find an alternative and then return to the fitting room to try it out, they are more likely to continue with their shopping journey and buy something.

In addition to smart mirrors, in-store retailers should think about equipping their staff with remote clienteling tools. With AI clienteling, sales assistants can double as personal stylists and recommend customers products that would best suit their physical features, body types, and individual preferences.

Hyper Personalisation Is Here to Stay

As the world of retail becomes more competitive, brands need to find new ways to stand out. While traditional personalisation is ubiquitous, hyper personalisation is still a fairly novel experience. According to one study, only about one in two organisations currently use advanced personalisation. That is surprising, considering that hyper personalising the customer journey is not that difficult and can yield significant results.

The key to creating more individualised experiences? Finding the right partner to deliver them. At Intelistyle, we understand the importance of putting the customer first. With our AI styling platform, retailers can customise their visitors’ customer journey from start to finish, regardless of whether they’re an in-store or an online-only shop. Rather than just surviving, Intelistyle allows fashion retailers to thrive. Ready to implement hyper personalisation into your store? Get in touch with us today.

Book a free consultation

The post How Hyper Personalisation Can Increase Conversion appeared first on Intelistyle.