AI Styling Game

Intelistyle’s AI Stylist Outperforms Zalando By 3 Times

One afternoon at the height of Covid-19 Lockdown, the Intelistyle team decided to create a fun fashion game that benchmarks our AI outfit recommendations against that of an E-Commerce giant: Zalando.

 

What started off as an internal game returned such impressive results that we thought our team must be subliminally biased somehow – as it is a blind test where the players don’t know which outfits were created by which company. So, we decided to take the ultimate challenge and open up this blind fashion game to the wider industry. And asked fashion industry professionals to choose the outfits they preferred for styling the exact same products.

 

The Participants

The participants consisted of Influencers, Buyers, Stylists and Senior Executives of global fashion retailers. 

 

How The Game Works

The game was simple: On a dashboard of products from Zalando’s own stock, select any product you want styling advice for and choose your favourite one from the two anonymous outfit options.

AI Dashboard

 

The Results

A total of 181 outfits were liked by participants: 131 likes went to outfits generated by Intelistyle’s AI and 49 likes went to those generated by Zalando’s AI.

AI Styling

This translates into the end result of the game as:

73% of participants preferred outfits styled by Intelistyle’s AI.

 

Of course, we didn’t stop there and call it a win. Our team was curious to dig deeper into the results to analyse performance by detailed metrics including styles, trends and a rich number of fashion attributes.

 

Outfit Richness

First, we wanted to compare the richness of outfit recommendations. Intelistyle outfits had 4 products on average, while the Zalando average was 3 products.

AI Outfit Richness

Intelistyle was able to use jewellery and accessory categories like hats, glasses and belts successfully in recommendations to style as many as 6 products in one outfit. Zalando’s richest outfits consisted of 4 products and overall didn’t enrich styling with complementary fashion categories.

AI Styling

Another interesting finding was related to the definition of an “outfit’”. All outfits generated by Intelistyle’s AI were complete – meaning a customer can wear the recommended look and be ready to step out the door. Whereas Zalando’s recommendations had many incomplete looks that consisted of just 2 products, which wouldn’t be classified as a finished outfit. 98.8% of the time, Intelistyle recommended richer outfits with more products compared to Zalando.

AI Styling

Yet, Zalando’s AI had its strengths as well.

 

Styling Approach

When analysing performance by product categories and subcategories, we found out that Zalando’s AI was better at styling more conservative and traditional outfits, thriving at product categories such as office shirts, polo shirts and oxford shoes.

Intelistyle’s AI on the other hand, performed significantly better in fashion-forward styling – due to self-evolving through monitoring micro and macro fashion trends in real time. In best-selling product categories that are currently on trend such as exaggerated sleeves, ruffled tops, slip dresses and mules, Intelistyle scored higher.

AI Categories

 

Global Prints

Looking at macro and micro print trends, the results revealed that Intelistyle outfits were preferred by 78% both when mixing prints and styling a single print. However, Zalando outperformed Intelistyle when styling animal prints.

AI Styling Prints

Sportswear and Athleisure

For sportswear and athleisure categories, 100% of liked outfits were generated by Intelistyle’s AI.

AI Styling

Another global trend, chunky trainers, were styled up and down for both casual and smart looks by Intelistyle, while Zalando supported a more classic styling approach. 95% of participants preferred Intelistyle’s fashion-forward approach.

 

Denim and Jeans

In denim and jeans, Intelistyle outfits collected 76% of all likes and Zalando outfits reached 24%. When styling flared jeans and tapered jeans the two AIs were at a tie.

AI Styling

Colour Trends

Finally, in global colour trends, both Zalando and Intelistyle performed best at styling monochromes and worst at styling neons. In metallics, 75% of participants chose Intelistyle’s styling to 25% that preferred Zalando. In pastels, the divide grew slightly where 79% of liked outfits were by Intelistyle to 21% by Zalando. When styling colour-blocks, Intelistyle’s AI outperformed Zalando’s AI by 89%.

AI Styling

 

If you are one of the participants, we would love to hear your feedback.

 

If you haven’t played the game yet and want to see the experience for yourself, the game is still available – please get in touch!

omnichannel retail solutions

Omnichannel Retail: Challenges & Solutions

Find out the challenges retailers face when building their omnichannel retail strategy and the best solutions to overcome them

Building an omnichannel retail strategy can be a difficult land to navigate through. We will look at the 3 main challenges retailers face and the simple solutions to overcome them for a successful omnichannel retail operation and a winning customer experience.

45% of retail executives say they don’t have the data or means of using their data for effective omnichannel personalisation and 63% say lack of skilled customer teams is stopping them from adopting an omnichannel retail strategy.

Not realising that the real challange is of perspective – not resources – that is stopping them from optimising their existing resources in a time and cost effective way.

And the solutions are simple!

 

Omnichannel Retail Solutions 1: The Data

omnichannel challenge 1

How You Can Make Data Collection Rewarding

Facilitate the collection of data by turning it into an added value for the customer with immediate reward as opposed to a burden or invasion of their privacy for no clear return.

PwC finds 63% of global customers who don’t share data currently are very open to sharing their data for a rewarding service.

Reward Your Customer By Peronalising Their Experience

By personalising their experience in real-time and letting your customer clearly know this is why you need that information, you can make data exchange a win-win for both your business and the customer. Starting from the moment they tell you about their likes, dislikes and lifestyle, curate the experience to your individual customer. For instance, if you ask a customer about their lifestyle and find out they are in a formal line of work, show them how they can style products for a smart office outfit.

omnichannel data collection

Remember What Your Customer Preferred and Use It

Always remember! When your customer connects with you at any point in the future, use their past data to provide them a service that is fully personalised to their needs and preferences.

If your customer repeatedly filters their search to see floral patterns on your online store, remember this when they shop in-store and show them products in floral print.

There are a number of simple steps you can take to initiate data collection:

Collect Data Online

> You can do this online by using a brand chatbot that interacts with your customer and asks them if they would like to answer a few quick questions to see personalised search results and recommendations.

Collect Data In-Store

> In-store, you can do the same via a sales assistant empowered by a clienteling tool. They can ask your customer the same quick questions you have online and the clienteling tool will show the store assistant personalised recommendations for the customer right in front of them.

instore data collection

> You can use smart mirrors on the shop floors or the fitting rooms for a self-service experience where the mirror shows your customer products and outfits based on their answers.

> Offer them an e-mail receipt when they shop in-store. Most customers really appreciate and opt for a paperless option that is better for the environment and easier to store and retrieve. Knowing a customer’s e-mail address opens you the door to reach out to them with personalised retargeting campaigns between visits.

combine online instore data
Combine Online and In-Store Data Collection

> Give a first-time discount code to customers who create a profile – both online and in-store. Rewarding them with a discount code encourages customers to take a few extra steps and share additional information. Of course, make sure to let them know it’s redeemable from any channel of their choice, at any time. 

 

Omnichannel Retail Solutions 2: The Customer

omnichannel challenge 2 

How You Can Become Customer-Centric

Use a technology solution to help you create a customer profile that can be integrated into your digital and physical brand channels – regardless of the channel it was created on. Make sure this profile gets updated and enriched with every new interaction and can be accessed from every brand touchpoint.

You can easily enable individual customer journeys through identifying your customers with key attributes and characteristics.

Let Your Customer Tell You What They Like

You can let the customer save their favourite products and recommendations on their profile and create ‘omnichannel shopping baskets’ that can be viewed and purchased from any channel of their choice at any time.

For instance, your customer can save an outfit recommendation they like on their mobile app and one week later go in-store. By accessing their shopping basket in-store, a sales assistant can easily find and bring the products they have saved for your customer to try on.

omnichannel customer profile

Know Your Customer At Every Touchpoint

Essentially, this means when an online customer or the customer of a different country’s branch walks into a physical store, all they need is their profile to be instantly recognised and continue their shopping experience where they’ve left off with no interruption.

Having a mobile brand app is a really good way to enrich the brand experience and identify your customers in the physical world, by simply scanning a unique barcode on their app that links to the customer profile.

 

Omnichannel Retail Solutions 3: The Integrated Experience

omnichannel challenge 3 

How You Can Create A Brand Ecosystem

There is no final destination when it comes to an omnichannel retail experience: Meaning a brand ecosystem that is always alive to keep the experience ongoing.

This requires a highly skilled in-store team, a frictionless online journey with expert shopping assistance and 24/7 access to a personal experience at the touch of a button.

The challenges with the above statement are:

> the time and cost of upskilling teams

> the feasibility of offering one-to-one expert shopping assistance round the clock to accommodate the volume of every brand channel

This is where technology becomes more than a data collection and processing tool to supercharge all your omnichannel touchpoints at scale.

Omnichannel Retail Instore

How To Upskill Your Teams At The Touch Of A Screen

An in-store clienteling tool will supercharge your sales teams in 3 easy steps through a tablet:

> Access to the customer’s profile gives your sales assistant context on the individual customer they are serving in a visual, easy to digest format. In under a minute, they know your customer’s likes, dislikes, past preferences, personality and lifestyle

> The clienteling tool will recommend the next best action for your sales assistants and show them the best products and outfits to recommend for the customer they are serving, ranked by highest selling opportunity

> The clienteling tool will give your sales assistants quick styling tips so they can explain the reasoning behind their recommendations and add real value to the customer’s experience with your brand

The same technology can be used to upskill online customer service teams.

Omnichannel Retail Chatbot

How To Provide A 24/7 Experience To Your Entire Customer Base

A brand chatbot integrated into your website and social channels will be ready to provide one-to-one shopping assistance to every single customer from any device, at any time during their shopping journey: find the products they are looking for, show them how to style different outfits and even give them personal styling tips.

Facebook Messenger, iMessage, WhatsApp and WeChat are the platforms where your customers are, as part of their daily routine. Integrating your brand chatbot into these platforms replicates the in-person intimacy of asking a friend for shopping advice and the convenience of having your own personal stylist on-call 24/7.

 

Key Takeaways

omnichannel solutions takeaways

Here, you can find inspiration from 5 outstanding omnichannel retail experiences executed by top fashion retailers.

Omnichannel Retail Cover

Omnichannel Retail Strategy Simplified

Find out everything you need to know about omnichannel retail strategy and how it will benefit both your business and your customers

The question of ‘where’ a fashion retailer needs to be leaves its place to a statement in 2020: Everywhere. That is, everywhere their customers are.

The Business of Fashion and McKinsey’s “The State of Fashion 2020” report forecasts that in the next 3 years, nearly 100% of retail growth will come from omnichannel sales.

Let’s look at the what, the why and the how of an omnichannel retail strategy to grow your business.

What Is An Omnichannel Retail Strategy…Truly

Omnichannel retail strategy is a cross-channel organisational approach to marketing, sales and customer service that creates an integrated and cohesive customer experience no matter how, when or where your customer reaches out to your brand.

The keyword here is ‘cross-channel’ as opposed to multi-channel: Because while all omnichannel experiences use multiple channels, not all multi-channel activities are omnichannel. Cross-channel entails the combination and sharing of data across all channels to create an ongoing and seamless customer experience.

 

Single Channel vs Multi-Channel vs Omnichannel

You can actually spot the difference by just looking at how the words gradually become more integrated.

omnichannel retail vs multi-channel retail

Let’s break down the terms:

Single Channel Retail: This means you are selling your products via one sales channel only. The sales channel can be a brick-and-mortar store, your online store, an online marketplace like Amazon or even on Instagram.

Multi-Channel Retail: This means you are selling your products on multiple different channels. While this can be multiple online channels, it usually refers to both online and offline. You have a strong online presence and your customers know where to find you offline. You are reaching out to your customers on social media and via e-mail. Multi-channel is a great first step to get your customers more engaged with your brand.

Omnichannel Retail: Omnichannel takes place on multiple channels, just like the multi-channel retail strategy. There can be no omnichannel without multi-channel. The key difference is that omnichannel retail strategy connects all channels for a fully-integrated shopping experience and a seamless transition across all platforms. The customer data and insight from your multiple channels come together to form a unified customer profile. You offer the customer exactly what they need, the moment they need it, anywhere and from any device – in both the digital and physical worlds.

Now, let’s look at the tangible benefits of an omnichannel retail strategy for your business.

 

What Is The Value Of An Omnichannel Retail Strategy

Value 1: Gain Competitive Advantage

Your customers are already omnichannel, even if your brand is not. 4 in 5 customers shopping in a physical store browse the internet before or during their purchase decision to compare different products and offers. Reversely, customers who complete a purchase online visit a physical store to get a feel of the product and fit. In fact, an impressive 73% of customers use more than one channel and one device during a single shopping journey.

Omnichannel Shopping Percentage

Being present on all channels that your customers are gives you a competitive advantage and ensures that you don’t lose a sales opportunity because your customer saw a competitor’s offering on a platform where your brand isn’t present.

Value 2: Better Customer Experience Means Higher Customer Retention

What do your customers expect? 9 in 10 global customers want an omnichannel service and expect companies to know their past preferences regardless of the channel of communication. Also, they want that experience to be convenient as well as consistent across all channels.

And good customer experiences are becoming increasingly important. In the past three years, the number of customers who ended their relationship with a loved brand after just one bad experience went up to 33%. The most stated reasons for the experience being ‘bad’ were: ‘wasn’t personalised enough’ and ‘wasn’t convenient‘. Remember that keeping your existing customers is significantly more profitable than acquiring new ones. Every percentage increase in customer retention correlates to x5 times increase in profit.

Omnichannel Retail Customer Expectation

Breaking down the barriers between different channels of a business empowers the customer to create their own definition of a ‘good experience’ by using the channels that feel natural and convenient to them.

Value 3: Increase Sales and Engagement

Making your sales and marketing strategy omnichannel might require some initial effort but it will be worth your money and time.

The more channels a customer uses, the more valuable they become…with every additional channel used, shoppers spend more money with a brand both online and in-store.

A Harvard Business Review study of 46.000 shoppers established just how much: Omnichannel shoppers spend 4% more on every shopping occasion in-store and 10% more online than single-channel shoppers. And the best part is, each channel you add directly translates into additional spending. For instance, for shoppers using 4+ channels, the in-store spending jumps up to 9% more.

Omnichannel Retail Spending

On the other hand, omnichannel marketing campaigns that use 3+ channels outperform single and 2 channel campaigns by up to 250% more engagement.

This means that adding just one more channel to your strategy can be the simple step that drastically increases your revenue.

Value 4: Improve Operational Efficiency and Reduce Costs

Moving to an omnichannel retail strategy will make your business operate more efficiently with reduced operational costs.

65% of customers find a positive connected experience with a brand to be more influential than great advertising. When your brand is fully connected with customers, the experience will be your advertising and word of mouth will do new customer acquisition for you. Deloitte reports that omnichannel operations directly result in increased effectiveness and lower costs for marketing.

omnichannel operational efficiency

Allowing the use of shared data on all channels means you need to collect and process a customer’s data only once, significantly lowering data collection and processing costs compared to multiple efforts per customer on every touchpoint.

Through connecting all your different channels under a master omnichannel operation centre instead of a different strategy and implementation for every channel, you will improve the efficiency of your head office operations.

Value 5: Higher Customer Lifetime Value and Loyalty

Customer Lifetime Value is the net profit attributed to the entire future relationship with a customer.

Not only do omnichannel customers spend more per single transaction, they also shop more often and are more loyal to your brand. And loyal customers who feel a connection to a brand spend twice as much as those who don’t.

Omnichannel Value Funnel

Omnichannel customers have five times higher lifetime value for your business: You will sell more without the cost of acquiring them meaning a greater profit.

Value 6: Better Data Collection

Retailers who track their customers across different channels have more opportunity to collect data and feed that data back to the customer immediately by providing a better and more personalised experience.

Better data on your customers lets you gain insights on:

> how to create content and offers that will end with a purchase to inform your marketing decisions

> who your different customer segments are, which channels they favour and their detailed shopping behaviour to inform your customer service strategy

> the products that are getting more customer engagement per channel to inform your buying & merchandising team on product mix and allocation planning

Omnichannel Retail Data Use

 

How To Build Your Omnichannel Retail Strategy

> Get to know your customer: Research your target audience’s interests, behavior and needs. Leverage social media and your existing touchpoints to identify your different customer segments

> Select the right channels: Find out the preferred channels your customers are already using and what they are doing on those platforms

> Connect all channels: Choose the right technology that can be flexible and integrated into all your channels for a consistent experience on every touchpoint

Become omni-device: Let your customers move not only across channels but also between different devices to complete a single shopping journey. Instead of starting from scratch, let them pick up where they left off from any device – and ensure your content displays well on all screen types.

omni device retail

> Prioritise social integration: Customers increasingly buy products directly from their preferred social media platforms. Integrate social media content into your online store and direct links to your product pages into social media content

> Customer service 24/7: Being open 24/7 is not possible in the physical world, but it is possible to offer 24/7 experience and customer service access. Build an exceptional in-person, online and mobile customer service program for your customers to reach you whenever they choose to.

> Maintain your channels: Always keep improving your strategy and the functionality of your channels according to customer demand and behaviour. This will build your loyal customer base who comes back for more

Here, we break down the top 3 challenges retailers face when building their omnichannel strategy and the simple solutions to overcome them.

 

What An Omnichannel Customer Experience Looks Like

Day 1

The customer visits your online store and they are greeted by your brand chatbot offering them a first-time discount code if they create a profile.

omnichannel chatbot discount

The customer creates their profile, browses the store and looks at a bucket bag 3 times. They see personalised outfit recommendations on every product page. But they leave without making a purchase.

Omnichannel Retail Outfits

Later in the day when browsing Instagram on their phone, the customer sees an editorial image with that bucket bag which is more focused on the lifestyle behind your brand. This first retargeting aims to educate and inspire the customer on your brand ecosystem instead of pressuring them to make a purchase.

Omnichannel Retail Inspiration

Day 2

The customer likes what they saw on Instagram and visits your online store again. Your brand chatbot reminds that their first-time discount is still waiting for them and offers them a personal styling session.

Omnichannel Retail Styling

After doing a short, guided style interview with the brand chatbot, the customer sees how they can style the bucket bag they checked out the day before, but they end up only purchasing a blouse from that recommendation with their discount code.

 

Day 3

Your brand chatbot sends the customer a WhatsApp message to say hi and shows them different colours the bucket bag comes in, accompanied by 3 different ways to style an outfit with the bag. This highlights the other product categories your brand has that are relevant to that customer. Also, informs the customer that there’s a physical store near them where they can check out the bag and shares the store location.

Omnichannel Retail Strategy Whatsapp

Day 4

The customer visits your store and is instantly recognised by checking in with their customer profile on the brand mobile app. For big flagship stores, the app shows them a map of the store highlighting where they can find each category and the offers in-store that day that are relevant to the customer’s profile and browsing history.

The customer scans the bucket bag’s barcode on their mobile app to see product details and reviews.

A store assistant empowered by a clienteling tablet knows that the customer has shown interest in that bucket bag and offers to show them different bucket bags from the same brand. When the customer chooses the bag they like best, the store assistant recommends an outfit with in-stock items to compliment the bag.

Omnichannel Retail Instore Barcode

The customer goes into the fitting room and tries on the clothes, but the skirt doesn’t fit as they had hoped. A smart mirror shows them similar skirts that are in stock and in their size. The customer presses a button to order the skirt they like to be brought to them, without leaving their cabin.

The customer decides they really like the whole outfit but need time to think. So, they add the look to their omnichannel shopping bag on the mobile app and leave the store without making a purchase.

Day 5

The retargeting evolves and introduces the customer to new media formats and layers. While on Facebook, the customer sees a short video that features the bucket bag they saved.

omnichannel retargeting layers

Day 6

The customer logs in from a desktop to view their omnichannel basket on the online store and adds on a necklace that is recommended to compliment the outfit. But they are running short on time to get to an appointment and need to leave the house.

Omnichannel Retail Baskets

On their way, the customer texts your brand chatbot on WhatsApp to order the bucket bag and accompanying outfit. The chatbot completes the sale and your customer receives their order the next day.

Day 7

You know that your customer is in a formal line of work, so you send them an email showing how they can style up the bucket bag they just purchased for a more smart office look. And invite them to an in-store styling workshop you have on ‘How to Style 5 Quick Looks For A Work Week”.

omnichannel email retargeting

Curious about how brands are executing their omnichannel retail strategy in real life? Jump here to see 5 outstanding omnichannel experiences created by top fashion retailers.

How The Future Looks For Omnichannel Retail

Online retailers are now investing in physical experience stores, like Nordstrom’s ‘Neighbourhood Hubs‘ that curate local offerings for local customers. Or Canada Goose’s Toronto flagship with ‘cold rooms’ that simulate adverse winter weather conditions to test their coats but has no inventory to purchase. Meanwhile, physical retailers are strengthening their online accessibility and presence.

The future of omnichannel retail strategy is going to be more and more about creating an exceptional and personalised customer experience. Instead of being everywhere for all people, brands will be there for the right customer when and where it matters to them as an individual. And technology will be the enabler of a better brand experience, not the end goal.

In the future of omnichannel retail, the customer is the channel.

Omnichannel Retail Best Examples

5 Outstanding Omnichannel Retail Examples In Fashion

See 5 best in class examples of fashion retailers executing winning omnichannel retail strategies in real life

Even if you haven’t noticed consciously, chances are you have been successfully exposed to an omnichannel retail strategy. As the path to purchase involves increasingly more channels and more devices, some retailers are getting very creative to target their audience and keep them constantly engaged.

An omnichannel retail strategy seamlessly integrates different sales and communication channels that the customer uses to combine the strengths of each channel and deliver a customer experience that is convenient, consistent, cohesive and holistic.

omnichannel retail retention

For fashion brands with a strong omnichannel presence, their efforts are really paying off! Retailers with a strong omnichannel strategy enjoy an average retention rate of 89% compared to 33% for those with a weak strategy.

To better understand what omnichannel success looks like in real life, we examine the five best omnichannel retail examples the fashion industry has seen by 2020.

 

Omnichannel Retail Example Topshop

Omnichannel Retail Examples #1: Topshop

The LFW Billboards

Topshop is a UK-based high street retailer that offers both women and men on-trend product mixes, with a core target audience of young women between 16-30. Topshop is the only high street brand to ever appear in London Fashion Week.

As part of their LFW campaign, they launched ‘Digital Billboards’ that were displayed around the UK throughout fashion week.

The billboards were connected to the company’s Twitter account, so when followers tweeted an emerging trend with the hashtag #LFW, the billboards displayed the tweet accompanied by a relevant ‘outfit look’ from their catalogue.

Topshop Omnichannel Retail Experience

Billboard to In-Store in 10 Minutes

Each billboard was located within a 10-minute walk to a physical Topshop store, so viewers could easily go in-store to check out the products displayed on the billboards.

This meant that their core audience of fashion-conscious young women would immediately know what and where to shop for the season’s latest trends.

The company reported a 25% increase in their sales across all categories featured on the billboards, with a 75% boost on items related to their ‘modernism’ hashtag.

 

Omnichannel Retail Example Neiman Marcus

Omnichannel Retail Examples #2: Neiman Marcus

Snap. Find. Shop.

U.S. multi-brand luxury retail chain Neiman Marcus was awarded the 2017 IRT Retailer Innovation Award in Customer Engagement and with good reason! Jeff Rosenfeld, the company’s VP for Customer Insight and Analytics says that at the heart of their strategy is a strong belief in the tangible benefits of personalisation.

Their core focus is to remove the barriers between how their customers interact with the brand on different channels by using technology that gets ‘a bit smarter’ every time the customer interacts with it.

Know Your Customer – And Remember

For example, if a customer always searches for size XS in tops, 6.5 in shoes and size 6-8 in dresses, the company will remember that. When the customer visits their online store next time, the site will show results that are available in those sizes. They won’t stop there and also show the customers the nearest brick-and-mortar store that has the items in their size.

Using geolocation, they include information on relevant local events or new drops for their brands the customer has expressed interest in. All of these are incorporated into their retargeting emails and printed direct mail campaigns.

Memory Mirrors

Neiman Marcus is also one of the best examples of innovative in-store experiences with tools like their “Memory Mirror.” The smart mirror allows users to record 360-degree videos of themselves trying on clothing, which they can then save in the brand’s mobile app and take a look later when they’re ready to buy online or in-store. They compare still images and videos of different outfits they try, side by side on the mirror or text those images and videos to a friend to get a second opinion.

Omnichannel Examples Visual Recognition

Shop Inspiration

Their “Snap. Find. Shop.” app allows customers to upload a photo of any product they like, and use smart recognition to show them a similar product from the Neiman Marcus catalogue. They can buy it on the spot or reserve it to try in-store.

 

Omnichannel Retail Example Oasis

Omnichannel Retail Examples #3: Oasis Fashion

360° Integration

Oasis is a well-known fashion retailer based in the UK, operating online and from 80 brick-and-mortar stores in the UK, along with 96 other retail locations around the globe.

Their different touchpoints aren’t just stand-alone sales channels. Every touchpoint is integrated with the others in real-time to create a truly holistic experience.

In-Store Trends to Online Store

Their online store has a carousel on product pages that shows the hottest items being purchased in-store at the physical store that is nearest to their customer.

Right under that, they have an ‘inspiration gallery’ of user-generated looks shared with the company’s Instagram account @OasisFashion.

Omnichannel Retail Examples Oasis Web

Social Commerce At Its Best

And the company Instagram account is a lesson on social commerce in itself:

Their profile is directly linked to their mobile-optimised InstaStore. The entire feed on their account is also shoppable looks and they have dedicated stories to different trends and collections that plug into their mobile app.

Oasis Omnichannel Retail Example 1

The best part is, it’s connected the other way around as well. So app users can explore stories and get outfit inspirations just like on Instagram. But on the app, in addition to buying it on the spot, they can add it to their shopping bag to purchase later online or in-store. The ‘Find in Store’ feature lets the customer locate the nearest store they can find the item in their size.

Omnichannel Retail Oasis 2

Clienteling Empowers In-Store Teams

Now, when you walk into a physical Oasis store, a store assistant empowered by an iPad gives the customer on the spot styling advice and product information – in consideration of their online activity, past preferences and saved items. If the store assistant recommends something that is not available in-store, they can order it for the customer on their iPad and have it delivered to their home the next day.

If the item isn’t what the customer thought, they can return it to any store, a collection point or arrange a free home collection.

This integrated strategy resulted in a 48% profit increase in the same year.

 

Omnichannel Retail Examples Nordstrom

Omnichannel Retail Examples #4: Nordstrom

Convenience Is Key

Nordstrom is a U.S. luxury fashion retailer who recognises that the foundation of an omnichannel strategy is customer convenience – and they know how to make a convenient experience.

This is why they launched their ‘Neighbourhood Stores’ initiative to strategically place local stores in the neighbourhoods where they have a strong customer base. These stores are experience hubs and showrooms where the customer can touch and feel the products they browse online. They can also make returns in-store or collect orders on the same day.

Omnichannel Retail Example Nordstrom

In-Store to App, Social Media to Online Store

Their New York store has an integrated app where customers can check stock availability before they go in, check product information while they are shopping or book one-to-one complimentary styling sessions.

Their Instagram and Pinterest accounts allow users to click on a posted photo and take them straight to the Nordstrom online shop where they can order online or reserve the item in-store. 

Whether the customer shops online, in-store or through a third-party outlet, they have access to personalised loyalty rewards and services.

 

Omnichannel Retail Example Canada Goose

Omnichannel Retail Examples #5: Canada Goose

Experiential Is Redefined

Canada Goose – a Canadian luxury outerwear brand – literally redefined ‘experiential retail’ when they opened their Toronto flagship store as a brand experience hub.

A Store With No Stock

Before we start, you should know that the store has a lot of coats – but they hold no inventory for the shoppers to buy and take home. Sounds a bit counter-intuitive? Not really, because that doesn’t mean you can’t make a purchase.

The brand wants to take the shoppers on a multisensory experience they call “The Journey” to feel why their outerwear is worth the hefty price tags upwards of £1,000.

The store is made up of multiple ‘Cold Rooms’, each one made to “re-interpret” adverse weather conditions from a seed vault in Norway to a room replicating Arctic conditions where it snows.

Canada Goose Omnichannel Experience

There’s a “Thermal Experience Index,” which is rating the outerwear based on the temperature range for which it’s best suited – and consequently the experience room it should be taken to.

Try In-Store, Order In-Store, Collect At Your Doorstep

After the customer leaves the cold rooms, they are directed to the retail area, where brand ambassadors are waiting to answer questions and help them make a purchase on the kiosks connected to the Canada Goose online store. The customers get their coats delivered on the same day or next day if it’s after 2 pm.

 

What To Takeaway From These Examples For Your Brand

There is no one-size-fits-all omnichannel strategy that can be copy and pasted to every brand. A successful omnichannel experience will look different for every retailer.

These omnichannel retail examples are here to give you the inspiration to identify what your unique customer base needs in order to make their experience with your brand more rewarding.

And the secret is in taking multiple baby steps, instead of one giant leap: Try out small, additional feautures on different channels that you already have or if your channels are not integrated, start by connecting two of your touchpoints.

But perhaps, the most important ingredient of a successful omnichannel strategy is to really know who your customer is, listening to what your customers are saying and tailor each step to place them at the core of your strategy.

Of course, there will be challenges down the road but how you approach and tackle these challenges will differentiate your omnichannel experience.

Here, we break down the top 3 challenges retailers face when implementing their omnichannel strategy and the simple solutions to overcome them.

Omnichannel Retail Examples Key Takeaways

 

e-commerce chatbot

Fashion E-Commerce Chatbots: Recreate In-Store Experiences Online

Find out how you can use a fashion e-commerce chatbot to create exceptional online experiences and services that outperform in-store

Back to Basics: The In-Store Experience

Remember how it felt walking into a new store to shop for clothes?

You see racks and racks of clothing all around you. And no sign of that striped blazer you are looking for…you feel tired and lost.

Then you hear those four magical words: Can I help you?

And the cramped, claustrophobic shop floor all of a sudden looks brighter.

The store assistant shows you a couple of striped blazers and a mono-colour alternative she thinks will look gorgeous with your hair colour. She also brings down the dress trousers they have in your size and suggests a blouse and heels to complete the look for your important meeting next week.

Was that a good experience for you?

Sure, you found just what you were looking for quickly without lifting a finger. And you don’t even have to worry about styling your new clothes because the store assistant showed you how to wear them.

Was that a good experience for the retailer?

Definitely! You are leaving their store with a big shopping bag.

This is exactly why the iconic Parisian department store Galeries Lafayette is arming their shop floors with a 300-people team of personal stylists who will be trained in a ‘retail academy’. All in the selfless interest of delivering a better shopping experience to their visitors? No. Because personal styling recommendations are proven to increase spending.

So why don’t online retailers follow suit and apply this rewarding experience guaranteed to increase sales to their e-commerce store?

 

The Manual Cost of Personalised E-commerce

Because it is extremely costly to have an army of stylists sit in front of a screen all day and assist your every online customer from home page to basket page. Let’s say you decide to train your existing customer service team in styling…that is just as costly on top of being highly time-consuming.

What if you had a super-stylist who doesn’t eat, rest or sleep that can serve an indefinite number of customers at the same time? While we’re at it, also give her a magic wand to turn your existing customer service agents into expert stylists overnight.

No, this is not the plot of a sci-fi film hitting the box office this year. This is your one-way ticket to the future of retail, today: Experiential e-commerce that goes beyond practical and impersonal conversations.

 

e-commerce chatbot charts

 

Conversational Commerce: What Went Wrong

Fashion retailers have dipped their toes in conversational commerce in the past. Because scaling your services with an e-commerce chatbot makes commercial sense. The head of customer experience at LVMH-owned Eluxury says AI-powered automation eases agents’ burden allowing them to provide a better service to an increased number of customers. So why did conversational commerce never quite stick with fashion retailers?

Because 84% of customers make it crystal clear that they won’t buy from a retailer unless they are treated as an individual, not a number. 96% of retailers agree, saying they feel the demand and pressure from customers for a more personalised service.

Both customers and retailers are on the same page.

Yet, so far conversational commerce in customer service has been about ticking off another number on a long list of problems to solve: FAQs, delivery enquiries, order and return issues.

Add the intrinsically personal nature of a fashion purchase to the equation and seeing why conversational commerce never kicked off in this industry is a no-brainer.

 

The New Age of Retail: Experiential E-commerce

Retailers who want to scale their services to increase revenue have to reframe their perception of customer service from just problem-solving to delivering a personal experience with additional value. Experiential commerce that exceeds the confines of product and price to put exceptional services as a retailer’s main value proposition achieves genuine connection with the customer. Harvard Business Review found that connected customers (highly satisfied + perceives clear brand differentiation) spend 52% more with an online retailer. Similarly, the Wall Street Journal concluded that customer experience is the key competitive differentiator in the digital age.

According to the Adobe Experience Index – a benchmark that retailers can leverage to understand and quantify customer demand – the top three value points for digital customers are:

  1. Know Me and Respect Me

  2. Delight Me at Every Turn

  3. Speak In One Consistent Voice Across All Interactions

 

This is why an overpowering 90% of consumers still shop in-store despite being perfectly comfortable with online shopping: they prefer an in-person experience and expert advice before making a purchase.

Now, let’s look at how an AI e-commerce chatbot specialised in fashion can tick all these value points for your retail business to outperform an in-store experience.

The e-commerce chatbot assists your customers in finding their way around a store’s entire stock and carefully leads them to the right products for them that result in a purchase.

 

E-commerce Chatbot Value Point 1:

Know Me and Respect Me

e-commerce chatbot stylist

 

The AI Stylist Who Knows Your Every Customer

An e-commerce chatbot welcomes your customer to the online store. The AI chatbot stylist knows who that customer is and what they like down to detailed attributes, such as colours, prints, sleeves, dress shapes and cuts they have preferred in the past. It also knows which attributes they don’t like without the customer having to repeat themselves every time and respects those preferences when making product and outfit recommendations.

A Guided Styling Interview and Customer Profile

The chatbot stylist asks your customer the same questions that a human stylist would, in the same language that a human stylist uses at a guided styling interview. The AI then builds a personal style profile for the customer that is enriched with every interaction.

Personalised Recommendations: Physical Features, Personal Taste, Occasions

In addition to making personal recommendations to complement the customer’s unique features – their body type, skin tone, hair and eye colour – the AI stylist also shows your customer how to style the same product for different occasions they are interested in. These occasions can range from a date night to a formal business meeting to a casual brunch with friends.

An E-Commerce Chatbot for Every Style Persona

What’s more, you can have a selection of AI stylists with different style personalities – just like specialised human stylists. Your fashion-forward customers can speak to a fashion-forward stylist who will put together looks reflecting the latest trends in real-time, while your conservative customers are served by a stylist with traditional taste.

Essentially, each of your customers is shown products and outfits that are hand-picked for them every time to match their physique, taste, personality and intended occasions.

They will even get personal styling tips on how to wear particular garments to flatter their characteristics.

 

 

E-commerce Chatbot Value Point 2:

Delight Me at Every Turn

e-commerce chatbot stylist

Personalised Home Page and Online Store

The e-commerce chatbot will not stop at personalising the one-to-one interaction with your customer. It will go on to curate the entire website to each customer’s individual needs. Instead of showing all your customers the same generic home page, every customer will land on a home page entirely personal to them. This will be carried over at every turn they take on your online store from search results to product pages and basket page.

e-commerce chatbot personal crm

Increased Purchase Frequency and Customer Retention

Even after the customer leaves your website, the AI stylist will keep curating every brand interaction. For instance, instead of generic CRM and retargeting emails pushing irrelevant products, your customers will open their inbox to only see the products matching their personal taste. Additionally, each of these products will be selected by their AI stylist to complement their past purchases, increasing purchase frequency and customer retention.

 

E-commerce Chatbot Value Point 3:

One Consistent Voice Across All Interactions

e-commerce chatbot personal profile

Always On Brand Tone-of-Voice

Unlike human stylists, who might have good and bad days influencing their performance and customer interactions, the e-commerce chatbot stylist will always speak in your brand’s tone-of-voice, delivering the same level of exceptional service quality every time. Through the omnichannel customer profile created by AI, your brand interactions will be consistent across all customer touchpoints, regardless of which ones the customer has used in the past.

 

At this point, you might be thinking: This all sounds too good, isn’t there a catch? Well, there is.

AI Technology and Human Intuition

While an e-commerce chatbot can do wonders for your business at scaling exceptional personal shopping services in a cost-effective way, it lacks the empathy and communication skills of a real-life person. While an AI stylist outperforms a human stylist at the speed and accuracy of personalised recommendations, it can’t match a human in picking up emotional cues to understand a customer’s mood.

This is why the best-practices of experiential e-commerce will emerge from the marriage of AI technology with human intuition. A seamless online experience will integrate a smooth hand-over to a real-life person to handle more complex and sensitive cases. However, this person will be a customer service agent with little or no styling skills, bringing us back to the cost and time of required training. Right? Not if you support your customer service team with a tool that handles the styling part so all they need to do is bring in the human touch.

 

E-commerce Chatbot Value Point 4:

AI Styling Platform Empowering Your Team

e-commerce chatbot ai styling platform

An AI styling platform lets your customer service agents access a detailed style profile that has been created by your e-commerce chatbot for every customer they are serving.

Agents can filter a retailer’s entire stock through attributes a particular customer favours and block the ones they don’t to only see the products that are relevant for each individual they serve.

The AI platform will also show products and outfits they can directly suggest to the customer, accompanied by styling tips so that the agent can explain the reasoning behind their recommendations and add real value to the experience.

These outfits can be added straight to the customer’s shopping basket, shared with them via email or even put in a box and delivered to their home for a VIP fashion concierge service depending on the retailer’s preference.

The entire customer service team can provide customers with an expert personal styling experience, meaning they can upsell and cross-sell effectively even if they have limited fashion knowledge.

 

Key Takeaways

 

e-commerce chatbot customer benefits

e-commerce chatbot business benefits

 

AI Outfit Recommendations

Increase Your Revenue With AI Outfit Recommendations in 4 Steps

Incorporating a ‘Complete the Look’ AI Outfit Recommendations System into their shopping experience is increasing revenue for top fashion retailers around the globe. Find out how you can easily apply it to your online store step-by-step and start reaping the revenue-driving benefits. 

 

Going into the new decade of fashion retail, one thing is already clearly established: Consumers have never been as overwhelmed by choice or overstimulated by brands competing for their attention as they are in 2020.

In a market where product, price, and availability are 100% transparent, consumers are bouncing between retailers with similar product offerings and differentiation is becoming increasingly more challenging. This growing competition is eating sizable chunks into the revenues of retailers who fail to stand out.

 

New Age of Retail: Experience As A Product

Product-driven retail is fast approaching an end as 80% of customers say the experience a retailer provides is at least as important as their products.

The future of fashion retail is Experience as a Product. Case in point, experiential e-commerce has been named one of the top four retail growth trends of the new decade.

For retailers who play their cards right, this promises a great opportunity to turn the online experience into a clear customer value proposition and gain solid competitive advantage to stand out from a myriad of competitors.

When it comes to creating an exceptional online experience, the way forward is taking a step backward and revisiting the basics of traditional retail: the brick and mortar stores where the concept of ‘shopping as an experience’ originates from.

 

AI Experiential Retail

 

Back To Basics: Learning From The Brick and Mortar Experience

Customers were firmly loyal to a brand, moreover to a certain branch of that brand, because they could always count on walking in and being greeted by someone who personally knows them and their taste.

Someone who can get them the products that match their need and style without the hassle of looking through racks of clothing.

Someone who shows them how to style these products to compliment their unique features.

Fast forward to 2020, the clothing racks have turned into product pages and the demand for expert styling advice stands as strong as ever.

When cleverly presented, styling advice can increase spending per transaction by up to 7 times. The Business of Fashion confirms that personal shopping assistance makes people spend significantly more with a brand. Why not take this proven recipe for higher sales numbers and apply it to your online store?

The store assistant who knows your customers on a personal basis can be an AI Stylist built into an outfit recommendations system that accompanies the customer throughout their online shopping journey.

 

Building The Future: AI Outfit Recommendations Strategy

The most common reason consumers point out for not shopping online is craving the positive aspects of the in-store experience rather than perceiving online shopping as a negative one.

Fashion retailers who recreate this offline narrative, preserving the traditional value points of brick and mortar, with the added convenience of online shopping will build brand loyalty and future-proof their business. Instead of digitising processes or isolated features, top industry players of the future will be the retailers who digitise the shopping experience as a whole to reinforce their value proposition.

Building an AI outfit recommendations strategy and executing it intelligently will increase your revenue from day one by cutting down the manual operational cost of personalised recommendations while delivering all the benefits that increase overall sales.

AI Personalised Landing Page

 

STEP 1: Welcome With A Personal Greeting – Personalised Landing Pages

 

Welcome Your Returning Customers

Customers do more window shopping online than they do offline! Convert digital window shoppers into paying customers by displaying targeted content and a personalised product curation based on their individual style, past purchases, and browsing behaviour.

This will be the warm greeting your returning customers are accustomed to receiving when they shop at their ‘home’ branch of a favourite retailer. A personalised welcome into the online store at the first touchpoint instantly increases engagement by promoting click through to product pages and results in a conversion uplift.

 

Welcome Your First-Time Visitors

For first-time visitors, landing pages can be curated to display a regional product selection based on the location of a visitor. This is a powerful way of connecting with consumers as trends, styles and hero products can significantly differ across geographical locations. Deloitte’s study on digital transformation backs this strategy by reporting that geographical location is still the cornerstone for capturing retail demand.

Alternatively, landing pages targeted at first-time visitors can be designed as ‘trend rooms’ to showcase a dynamic product selection that reflects rising micro and macro trends of the season in real-time. 

AI Revenue Results 1

 

The customer has walked into your online store, now it’s time to help them find what they are looking for.

 

STEP 2: Find The Right Product – Improved Product Search and Discovery

 

In a traditional shopping narrative, the customer would easily describe or simply show the store assistant what they are looking for. Customers agree they often feel overwhelmed by the variety of options when shopping online. This is known as “choice fatigue” where the abundance of choice leads to negative feelings that mimic anxiety and tiredness, resulting in site abandonment.

 

Make sure you don’t lose potential customers at this early stage before you have the chance to show them your personalised product offering. This first crack in the online shopping experience where a sales opportunity falls through can be easily avoided by making product discovery just as convenient and relevant as the in-person help from a store assistant.

 

A positive experience at this stage increases engagement and sets the tone for the rest of the customer journey. Think of product discovery as your trailer to capture and hook the customer to stay for the complete shopping experience.

 

AI Outfit Product Discovery

 

 

Natural Language Search

Natural language search allows the customer to search for products using language and descriptions they would normally use in their daily life. Returning accurate and relevant results for text search minimises friction and gets the customer closer to finding the right products that result in a purchase.

Through automated product tags, customers can narrow their search results further at this stage by detailed attributes such as cut, fabric, pattern as well as occasions and trends.

However, sometimes it is just easier to show what you are looking for…

 

Visual Search

Fashion is visual by nature and images can speak louder than words. Millennial and Gen-Z consumers, in particular, are gravitating towards image for self-expression.

Visual search fills the gaps where words fail to describe what the customer is trying to find or when they have a very clear idea of the complete look they want to imitate. The customer can ‘show’ you what they are looking for by uploading the image of a product or a full outfit. AI extracts detailed attributes from the image and shows the most similar-looking options available on a retailer’s website, linking inspiration directly to product.

 

Personalised Product Ranking

Retailers who want to make the most of product discovery and translate inspiration into a purchase must consider how search results are presented.

According to the Infosys consumer study on the future of omnichannel retail, presentation will be key in obtaining a watertight fit between the product and the customer.

This final stage of discovery, where the customer meets your offering for their search, is crucial in inducing them to click through to the product page. By returning search results in a prioritised order of products a customer is most likely to buy, you ensure the product that will seal the sale never gets lost in a crowded page.

Scrolling through pages of irrelevant products is the second crack in the online shopping experience that is a surefire recipe for site abandonment. Instead, show your customers exactly what they want on the first page they see. Matching the right product to the right customer with minimum additional steps on the customer’s part optimises sell-through rate.

 

AI Recommendations Revenue

 

The simple touch of making product search and discovery effortless will be a significant performance differentiator that sets your experience apart and gives you a competitive edge.

 

Now that the customer has found what they are looking for, they click on the product to make a purchase, high on the excitement of this new addition to their wardrobe.

But the product is out of stock, not available in their size or the colour they like. What now?

 

This is the third crack where retailers lose the highest number of sales opportunities to frustration. If the disappointment and the unexpected comedown from a shopping high are not replaced with a positive experience immediately, the customer is very likely to leave your online store with a negative association instead of a delivery confirmation.

 

AI Recommendations Benefits

 

Also, this happens to be the point where a good store assistant would show the customer alternative products that are similar to the one that is not available. Offering a solution to a negative situation re-engages the customer and turns the experience around. So why don’t you?

 

STEP 3: Show the Best Alternatives – Visual Similarity Recommendations

 

Behavioural recommendations – showing ‘related products’ based on what other customers looking for the same product went on to purchase – dominated the online recommendation landscape in the last decade. Or as you may know them: “Customers who bought this item also bought”. This approach may work for some industries where purchase has no emotional attachments to product.

However, in fashion where every purchase is incredibly personal, the assumption that customers who liked one product in common will have identical taste and needs is unrealistic at best.

Don’t play a game of chance with your sales numbers when there is a far more effective option of addressing product alternatives: Visual Similarity Recommendations.

 

AI Visual Similarity Recommendations

 

To Stop Missed Sales Opportunities

By suggesting visually similar alternatives to out of stock products, you can redirect the customer to multiple relevant product pages on your online store. This means that you never miss out on a sales opportunity and keep traffic moving around your website instead of away from it to competitors.

Visual similarity recommendations can be personalised to each customer based on their individual style and the attributes they favour. For instance, let’s say a floral print t-shirt is out of stock. By knowing that your customer likes puff sleeves, you can show them different styles of puff sleeve tops in floral print as well as the closest alternatives for a simple floral t-shirt.

Customers at this stage are more likely to click on products with their preferred attributes that also match an attribute from their original search.

 

To Extend Product Discovery

Remember that some visitors on a product page will not be convinced to make a purchase decision yet. While they are still in search mode, take this opportunity to keep them engaged and inspired by promoting a selection of similar products that match their original search with a variety of additional attributes you know they like.

Customers typically struggle to form a precise description of what they are looking for at the search stage. Often, this is because they need some visual inspiration to solidify their want. Placing a selection of tailored alternatives in front of them visually, you increase the chance of them discovering a product that does just that. The additional attribute on one of the visually similar products might be what achieves the perfect match for their demand that they go on to purchase.

 

To Upsell

Visual similarity recommendations promote upselling and increase Average Order Value (AOV) by inspiring customers with more than just one product per page. Every single page on your online store is transformed into a hub of capsule product discovery that does not entail the friction associated with restarting a search. Customers who have already added the main product to their basket are prompted to increase their basket size.

Having multiple relevant products on every page starts a click-through chain that increases the visibility of your inventory. A visual similarity chain buys you extra time with the customer to keep showing them more products for upselling.

Visual similarity recommendations convert a missed sales opportunity into a purchase by creating transferable new demand that is met on the spot. Simultaneously, they boost upselling through fresh inspiration tailored to the customer’s style and taste.

 

AI Recommendations Results

 

Now that multiple products are safely in the basket, it’s time for the customer to checkout. Right? Not if you want to carry on upselling!

In a brick and mortar setting, this is where the store assistant would offer styling advice and recommend other clothes that would complement what the customer is buying. The highest upselling rates are hit with outfit recommendations that increase the basket from just one product to a complete look. Keep inspiring the customer and increasing basket size by showing them how they can style every product on your online store.

 

AI Outfit Recommendations

 

STEP 4: Delight with Expert Styling – Complete The Look Recommendations

 

‘Complete The Look’ or Outfit Recommendations use the latest trends and top styling principles to suggest a full outfit for every product on your online store.

The AI recommendation engine gives expert styling advice and creates editorial-grade complete outfits that outperform human stylists in resonating with the customer.

This is due to multiple layers of superimposed rich data insight the human mind does not have access to or the capacity for that come together in creating AI outfit recommendations.

 

Layer 1: Core Principles of Fashion Styling

When it comes to fashion, every expert stylist knows that there are a number of objective do’s and don’ts that make a good outfit regardless of how one interprets contemporary trends. AI ‘knows’ these core and globally-accepted standards of the fashion industry from processing a vast amount of data on detailed styling elements.

 

Layer 2: The Latest Trends in Real-Time

AI crawls the web and fashion-specific sources of inspiration to spot the latest trends and keeps up-to-date with ascending/descending trends that dominate customer preferences in real-time. Machine learning allows AI to additionally foresee upcoming trends and adjust outfit recommendations to always meet emerging customer demand. A retailer’s ability to anticipate customer expectation is a major point of differentiation that directly increases market share

 

Layer 3: Your Brand Identity

Every brand has a unique fingerprint that defines them against other industry players. Keeping all customer interactions ‘on-brand’ and unified with your authentic brand identity enhances brand loyalty in the long run. Genuine brand loyalty is the easiest and most cost-effective way of customer retention.

The AI stylist can be calibrated to embody your brand’s styling guidelines, meaning the outfit recommendations follow visual aesthetics that match your brand identity.

For example, if you don’t use more than one statement garment in your editorial styling or never style red and black together, the AI stylist applies these rules to outfit recommendations. On the other hand, if you have seasonal style favourites that you would like to push, such as mixing prints, the AI will favour those attributes when creating outfit recommendations.

 

Layer 4: Your Unique Customer 

With the ability to automatically learn, adapt and improve from customer interaction, AI genuinely gets to know your individual customers and decodes their taste, action triggers, decision drivers, likes, dislikes. This is not limited to past purchase history or the personal data they provide.

An AI outfit recommendation system builds a unique and detailed profile of every customer through analysing a complex combination of valuable data that goes unnoticed or unused. Unlocking this information is the key to understanding who your customer is now and likely to become in the near future.

Outfit recommendations are created and updated based on this unique profile to push the products that have the highest probability of being added to the basket at a particular point in their journey.

 

Personalised AI Outfit Recommendations

Layer 5: Your Stock and Margins

AI takes into account inventory management metrics that are directly linked to your revenue including depth of stock, margins and individual product performance, to put together outfit recommendations that increase your sell-through rate while minimising price reductions.

Let’s assume a neon fishnet top is not performing well and remains higher in stock than anticipated. AI combines this data with the insight that neon tops will be ascending in popularity soon and pushes this product more often in outfit recommendations to the right customers.

 

Layer 6: Hyper Personalisation

Retailers who want to create a true personal styling experience that has traditionally been reserved for top-tier VIP customers and scale this to all customer tiers can incorporate hyper-personalisation into their outfit recommendations.

A personalised shopping experience has been proven to increase spending and give you a revenue uplift.

 

Outfit recommendations can be tailored for every customer based on:

AI Personalisation Recommendations

 

Complete the Look recommendations placed on the product page deliver the same ROI to your business as the top-performing team of in-store assistants do to a brick and mortar retailer: Upselling and cross-selling to increase AOV for you while providing tangible value to your customers.

Unless you add and keep reinforcing a unique value proposition to the customer journey, your customer retention will suffer for it. Retailers who count on customer acquisition alone for their sales numbers only enjoy short term profits that never translate into sustainable long term revenue.

An outfit recommendations system creates real value for your customers at multiple touchpoints and delivers measurable benefits to your business.

AI Outfit Recommendations Revenue Uplift

By 2028, more than half of retail sales will take place online. With so much potential waiting to be realised, be an early adaptor in the experiential e-commerce landscape, position your business as one of the top players and start increasing your revenue today.

AI Retail Data

To find out more about how global retailers are utilising their AI Outfit Recommendations Strategy, you can download our case studies here.

AI Value Chain

AI in Fashion: An Extensive Guide To All Applications For Retail

Let’s have a comprehensive look at all current applications of AI in the fashion industry that can be integrated into a retailer’s value chain:

Artificial Intelligence (AI) has swept into many industries with the potential to revolutionise businesses through higher speed, lowered operational costs and access to a depth of consumer and market data that promise a strong competitive edge.

AI is a branch of computer science that stimulates intelligent behaviour in computer-controlled machines to perform tasks associated with human reasoning and capabilities.

It was no surprise that the fashion industry’s initial reaction to AI was a curious eagerness to be at the forefront of innovation mixed with reluctance. After all, automating an industry built on human creativity does come with understandable concerns.

At this point, it is important to highlight that AI uses human reasoning as a model but the end goal is not replacing it.

Best use cases in fashion look at AI as “Augmentative Intelligence”: Utilising machine learning, algorithms and rich data to augment the capabilities of humans and businesses.

The key here is to understand different applications of AI in fashion and adopt a tailored approach that plugs only into the areas where it can add value to your business model.

 

AI in BUSINESS OPERATIONS

AI in Fashion Business Operations

Design and Product Development

AI to Assist New Product Development

By tracking design elements like colour, fabric, patterns and cut as well as their past retail performance and future performance indicators, AI reinforces and gives credibility to the creative decision-making process of product development in fashion.

AI as Designer

Using a powerful algorithm that analyses past designs and future trends, AI makes new apparel designs complete with sewing patterns. Retailers can choose to send AI-designed apparel straight to manufacturing or incorporate this as an additional step to automate the pattern making and fit process.

Human designers can then make alterations on these pre-designed garments to significantly speed up design to market time.

AI in Textiles

Garment Manufacturing

AI scales the manufacturing process in fashion through automating fabric quality control, pattern inspections, colour matching and defect detection. Manual manufacturing processes are completed by AI at a fraction of the time with increased accuracy.

Wearable Technology and Smart Fabrics

Although still at an infant stage, AI-controlled smart textiles promise clothing that delivers added value to the wearer: increase performance, enable communication, conduct energy and even grow with the wearer.

On the other hand, as more fashion brands shift towards environmentally responsible practices, AI in biotech facilitates the production of alternative materials that are cruelty-free and fully biodegradable.

Buying and Merchandising

AI empowers fashion buying and merchandising teams to make future buying decisions grounded in smart analytics with reduced risk of wrong predictions.

Traditionally, buying decisions have been based on the past performance of products and human instinct. This is not an accurate projection as sales are influenced by many dynamic factors like new trends.

Predictive Analytics

Through automated product tagging, AI analyses market performance on a per attribute level. Buyers are informed not only on the products that are performing well but also on detailed attributes like colour, prints, sleeves, necklines and more.

Additionally, instead of looking at trends and product performance as a snapshot at the end of each season, AI provides real-time data to observe shifting trends and stock performance as they are happening. Hence, buying and merchandising teams can adopt a proactive strategy to address consumer demand as it arises and always stay relevant.

Trend Forecasting

AI collects, analyses and interprets rich data from social media, e-commerce platforms and the runway to spot future fashion trends for each product category.

This information is then combined with the data on past performance and customer behaviour to establish the optimum product assortment mix that would resonate best with a retailer’s consumer base.

 

AI Trend Forecasting in Fashion Retail

Styling and Merchandising Platforms

Personal Styling Platform

Retailers can build, track and manage comprehensive customer profiles using a flexible AI Styling Platform that automates and scales exceptional quality styling services. AI generates hyper-personalised recommendations for each client that can be speed edited on the platform. Finally, these outfits can be put into the shopping basket, shared via email or sent directly to the customer as a fashion concierge styling box.

Visual Merchandising Platform

Visual merchandising teams can use an AI-powered platform to speed up product page curations for the online storefront. AI curates bespoke pages to address different trends, customer segments and geographical regions more effectively. Using a drag and drop interface to edit automated product pages cuts down the time and cost of manual product presentation. 

Performance Analysis

Merchandising managers can use an AI-powered internal dashboard to track detailed performance analysis of each member on their team. Allocating team members to the customer/product segments they are best at optimises team resources and improves overall performance.

 

Head Office Operations: Retail Decision Insight

Inventory Management

AI provides detailed analytics that enables fashion retailers to accurately determine best geographical allocation and market drop calendar for their inventory while tracking and managing the entire product life-cycle in real-time.

Product and Pricing Mix Strategy

Retailers can use AI-powered rich data to address each customer and market segment with a tailored product and pricing strategy.

Making data-informed business decisions to achieve the ideal product-pricing mix for each market means less surplus inventory and fewer price cut-downs.

AI increases stock turnover by forecasting the need to “move” older stock and analysing it against demand forecasts. Inventory can be reallocated to targeted locations in order to meet demand and prevent store clustering. Markdowns and promotional strategies can be planned and prioritised accordingly to resonate with the right value-seeking customers at the right time.

Competitor Analysis

Monitoring competitor pricing, AI recommends ideal price points to optimise revenue by gaining a competitive advantage. Retailers can spot the best seasonal timing to keep lower prices while retaining minimal margin and when to slightly increase prices to maximise profitability.

 

Supply Chain: Improved Efficiency, Agility and Sustainability

Understanding the customer and market better reduces the production of styles that won’t sell which directly translates into lowered product waste output.

AI can also be implemented into the supply chain management process at earlier stages to improve efficiency, agility and sustainability.

Improved Efficiency

Combining historic timeline information with real-time data such as weather reports, AI can make accurate Time to Market (TTM) forecasts. 

Improved Agility

Equipping businesses with real-time data on inventory performance and rising trends, AI enables agile supply chain practices. Retailers can spot the best and worst performers in advance to stop scheduled production of products that don’t sell and increase orders on hero products.

Improved Sustainability

Analysing complex factors that contribute to carbon footprint, AI can identify the most sustainable supply partners and modes of transport for a business that still makes financial and operational sense.

Retailers are able to create a more sustainable business model without compromising on profitability.

 

AI in E-COMMERCE

 

AI in Fashion E-Commerce

Online Product Recommendations

Visual Similarity Recommendations

AI uses visual detection and key product attributes to recommend visually similar alternatives for each product on a fashion retailer’s online store.

When a product goes out of stock or size, customers are redirected to multiple relevant product pages on the retailer’s website. They can easily find what they are looking for without the hassle of restarting product search, which often leads to frustration and site abandonment.

Using similarity recommendations increases customer engagement and reduces sales opportunities lost to competitors.

 

Complete The Look Recommendations

AI uses the latest fashion trends and top styling principles to show complete outfit recommendations for each product on a retailer’s online store.

Customers often want to try out new clothes but simply don’t know how to style them. Outfit recommendations show customers the ways they can wear different products together.

Inspiring the customer with editorial quality AI styling means upselling and increasing basket size from just one product to a complete look.

 

Personalised AI Styling in Fashion Retail

 

Personalised Recommendations

  • Personalisation Per Region

AI can personalise recommendations to show different results for different regions a retailer operates in. Trends, style identities and personal taste can significantly vary between different geographical locations.

What constitutes a ‘region’ can be tailored to every retailer depending on their own regional strategy.

For example, one retailer might use Europe and Asia as macro-regions. Meanwhile, another retailer might want to further personalise results for micro-regions within Europe to show different recommendations in Northern Europe, Central Europe and Southern Europe.

  • Personalisation Per Customer Segment

Retailers can address their different customer segments with personalised recommendations that connect best with each segment.

Additionally, AI enables retailers to focus on different strategic goals for different customer segments they want to address. For one customer segment, it might make more sense to show recommendations that promote upselling, while for another one the goal might be to cross-sell.

AI combines different customer identities with different strategic business goals to show recommendations that help retailers achieve their business objectives on a per-segment basis.

  • Personalisation Per Individual Customer

The power of machine learning and AI can be used to make visual similarity and product recommendations that are hyper-personalised to each customer, taking into account their individual:

Body Type, based on complimenting their figure
Colouring, based on the combination of hair and eye colour, skin tone and undertones
Occasions they want to dress for
Style Persona, based on their taste and identity such as fashion-forward or traditional

and merge these with data on the individual customer’s past browsing and purchase behaviour.

Each customer is shown a different similarity and outfit recommendation customised to them for every product on the online store.

This brings online recommendations to the same quality standard of a bespoke personal styling service.

Retailers can scale personal styling services provided in-person to top tier VIP customers down to all customer segments without the cost of dedicated stylists.

 

AI personalised outfits

 

Product Search & Discovery

Automated Product Tagging

AI-automated product tags are attribute labels that enrich each product on a retailer’s inventory. This detailed and fashion-specific information enables customers to filter their search by specific attributes they want to see: such as colour, print, fit, fabric.

Product searches return results with increased accuracy and relevance that bring the customer closer to a buying decision.

Automating manual product tagging with the use of AI means faster processing times, lowered cost, richer data and improved consistency free of human bias.

As previously mentioned, once a retailer’s inventory is enriched with product tags, their own stock becomes a powerful tool to understand performance, customer behaviour and inform future business decisions. This insight can be incorporated into multiple stages of the value chain.

Natural Language Search

Natural Language Processing (NLP) is the ability of a computer program to understand daily language as spoken by humans.

AI allows customers to search for products using the language and descriptions that they would naturally use in their daily life.

Simplifying product search reduces e-commerce friction and improves the customer experience.

Visual Search

AI-enabled visual search lets customers use pictures of clothing they like or influencer/celebrity styles they want to imitate to search for products. AI identifies all products in the photo and finds the closest match for each of those products from a retailer’s stock.

Visual search helps customers find exactly what they are looking for even in cases where putting it into text is difficult.

Letting the customers show what they want gives retailers the ability to inspire them with styles that match their taste. In addition, they gain a deeper understanding of changing consumer demand.

 

AI Visual Search in Fashion

Personalised Landing Pages

A landing page is the entry point to a website or a particular section of a website built to drive traffic for a specific marketing goal.

AI personalises landing pages by displaying targeted content and a tailored product selection based on the marketing campaign: merging data of past customer behaviour and preferences with factors like campaign time and season.

Personalised landing pages increase lead generation, conversion rates and ROI of marketing activities.

Personalised Product Ranking

AI identifies what each customer is most likely to want and buy from a retailer’s inventory. Products are displayed in a personalised order for each customer to optimise sell-through rate while factoring in additional parameters like depth of stock and margins.

Personalised product ranking enables the best customer-to-product matching and ensures the right products are seen by the right customers at the right time.

 

 

AI in CONVERSATIONAL COMMERCE: CHATBOT

 

AI Chatbot for Fashion Retail

AI-powered chatbots let fashion retailers stay in one-to-one touch with their customers before, during and after a sale. Chatbots fill the gaps in the omnichannel customer journey to deliver a seamless and uninterrupted brand experience.

A chatbot can be integrated into multiple touchpoints at a retailer’s e-commerce and social media platforms: the website, Facebook and even instant messaging applications such as WhatsApp, iMessage and WeChat. Communicating with customers on the same channels that they already use increases the convenience of shopping. Instead of waiting for the customer to come to them, chatbots enable retailers to be present when and where the customer is ready to engage.

Customer Service

Chatbots can be tailored to mirror a brand’s identity and tone of voice. This way, fashion retailers can scale their service capacity in key areas such as customer relationship management (CRM) and personal styling that are directly linked to the brand experience.

The chatbot can make product recommendations, arrange exchange and returns, answer customer enquiries and offer styling advice 24/7.

Also, the AI assistant interprets the flow of conversation and spots the best hand-over point where the customer can be connected with a human assistant. This approach significantly reduces the volume of enquiries for customer service teams while still allowing a human touch where needed.

Direct Sales

Chatbots can take pre-orders, make reservations for in-store collection and take payments to make direct sales.

Business Insight

As customers interact with a chatbot, AI collects rich data on individual customers and spots common patterns across the data. For instance, styles most frequently enquired or products most frequently complained about provides valuable business insight to retailers that inform future strategy.

 

 

AI and IN-STORE INTEGRATIONS

 

In Store AI for Fashion Retail

Physical retail remains a powerful tool in the fashion retailer’s arsenal to create exceptional omnichannel brand experiences. The use of AI can be extended offline to seamlessly tie in a customer’s online journey into the physical store.

Customer Service and Clienteling Support

In-store sales teams can use AI technology to easily browse in-stock inventory and find products that are right for the body type, physical features and individual taste of each customer they serve. Results can be further filtered to make spot-on recommendations for different occasions the customers are shopping for.

An AI clienteling tool empowers sales assistants with expert outfit styling for each product in stock. This means retailers can scale their personal styling capacity without the time and cost of training required to transform a sales assistant into a personal stylist.

Personal styling adds real value to the in-store experience and significantly increases spending.

Shop Floor Time Optimisation

Sales assistants can use AI technology to instantly find and recommend similar items for products that go out of stock in the customer’s size or desired colour without the need to go through all available inventory. By spending less time on finding products, they can offer quality assistance to a higher number of customers per hour.

Making relevant product recommendations reduces missed sales opportunities while increasing sales per employee.

 

True Omnichannel Shopping

AI enables a retailer to recognise returning customers at every touchpoint regardless of the channels they have engaged with in the past, truly breaking the boundaries of traditional retail. Connecting the data from all digital and physical retail locations under one umbrella means a loyal customer walking into a retailer’s physical store for the first time or at a different location can receive the fully customised service they are accustomed to.

Sales assistants can easily access a customer’s profile and immediately know their style, likes, dislikes and purchase behaviour to delight the customer with a bespoke experience every time.

 

Digital In-Store Experiences: Smart Mirrors

Retailers can integrate AI technology into a number of customer touchpoints on the shop floor to turn their store into an experience hub.

On The Shop Floor

AI-powered smart mirrors directly interact with the customer to make personalised product and outfit recommendations. This facilitates product discovery and reduces fatigue from an abundance of choice.

Through AI visual recognition, smart mirrors scan the customer and suggest outfits that complement the clothes they are wearing in the right size and fit.

Alternatively, they can be used to scan a product and see different ways of styling a complete outfit with other products in the store.

In The Fitting Room

When placed in the fitting rooms, smart mirrors let the customer find alternatives for the clothes that don’t fit or look as expected and request them to be brought to their cabin. Without the inconvenience of leaving the fitting room, customers are more likely to find the right products that result in a purchase.

Physical Store as The Experience Hub

Finally, smart mirrors can be used for interactive installations in the store as part of visual merchandising. These displays combine aesthetic and functional value, driving footfall and repeat visits to the physical store. They can be used in numerous ways to inspire customers and act as a storytelling tool.

 

 

AI in MARKETING & CRM

 

AI Marketing & CRM in Fashion

The power of AI, machine learning and rich customer data can be used to create marketing and promotional campaigns that are personalised to each customer. AI can accurately predict future purchase behaviour based on past purchases, browsing behaviour and individual customer preferences.

Retargeting Campaigns

Retailers can increase retention and repeat purchase by reaching out to their customers with relevant products and outfits that complement past purchases. Instead of retargeting campaigns that push alternatives from the same product category a customer has already purchased – let’s say, more white dresses – AI can create a personalised campaign per customer to suggest products that would complete a look – such as the bag and shoes that would look great with the white dress they already own.

Promotional Campaigns

AI detects the product categories, seasonal occasions and offers that a customer is most likely to engage with. Retailers can extend personalised promotions that correspond to the value each customer is seeking. Instead of generic promotional campaigns that might or might not have significance to a customer, they can extend targeted promotions backed by data that combine the right product selection with the right time of the year.

Loyalty Programs

AI builds a better understanding of what will drive higher engagement and a longer-lasting relationship between a retailer and each unique customer. Retailers can use this data to build a personalised loyalty program combining points, rewards and tiers. This adds value to the customer journey and accelerates loyalty by giving the customers more reason to keep engaging. Offering personalised rewards for referrals is a smart way of integrating customers into the client acquisition strategy as brand ambassadors.

Content Generation

AI is being used in content creation and automation by retailers who want to turn content into a powerful tool for lead generation and conversion.

AI-powered copywriters can automatically generate original content using natural human language. Automating periodic, short-form content frees up copywriters to work on higher concept creative brand assets and strategy.

Data-driven marketing content delivers higher conversion, click and email open rates in addition to a consistent tone of voice that is always on-brand.

Marketing Strategy 

AI accurately predicts future market trends to formulate short-term and long-term marketing strategy rooted in data and consumer insight. Taking the guesswork and ‘gut instinct’ out of key marketing decisions, retailers can make informed strategic choices on areas ranging from new product launches to periodic content planning. 

 

Key Takeaways

Integrating AI into a fashion retail business has proven benefits throughout the value chain:

AI key benefits

AI provides multiple solutions for fashion retailers to more effectively address each pain point in the omnichannel customer journey and deliver a better brand experience that sets them apart from the competition.

However, integrating AI into a fashion business is like finding the right suit: one size does not fit all and the best fit will be achieved through a tailored application.

Fashion retailers who leverage AI as Augmentative Intelligence to supercharge their existing business model will scale their resources, amplify their core strengths, be empowered by data-driven insight to overcome their weaknesses and maximise their market penetration as a brand. Growing market share and revenue while cutting down operational costs means an increasingly profitable business.

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

How Automated Product Tagging Can Reform Your Fashion Business

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

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

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

 

What Are Product Tags?

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

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

Attribute metadata is:

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

 

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

 

How Does Product Tagging Work?

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

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

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

 

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

1- Online Storefront

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

 

2- The Shop Floor

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

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

 

 

3- Inventory Management

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

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

 

4- Personalisation

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

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

Personalisation can be integrated into multiple customer touchpoints:

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

 

 

 

5- Predictive Analytics: Inform Future Business Decisions

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

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

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

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

 

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

 

  • Multi-Tiered Product Tagging Structure:

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

 

  • Mutually Exclusive and Non-Mutually Exclusive Product Tags:

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

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

 

  • Visual Search and Multi-Product Detection:

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

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

  • Fashion-specific Data Set:

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

 

  • Optional Control:

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

 

  • Attribute Groups:

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

 

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

How AI Is Set To Change Fashion Retail

 

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

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

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

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

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

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

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

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

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

 

ai fashion retail experience

 

From Farming To AI Fashion Retail

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

 

radio shack desktop from 1970s

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

 

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

 

ai smart mirror for fashion retail

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

 

Fashion Retail: Right on The Cusp of Change

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

 

ai wearable technology in fashion retail

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

 

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

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

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

 

The Technological Power Play in Fashion Retail

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

 

The Vital Questions To Ask Your Business

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

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

And find out where you can be unique!

 

How to Incorporate AI into Your Fashion Retail Business

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

 

ai data fashion retail

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

 

The Evolution of AI in Fashion Retail

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

See the full interview here.

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