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 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 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 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.





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 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 Artificial Intelligence Is Set To Change The Next Decade of Fashion (with Paul Kruszewski)


An exclusive interview with Paul Kruszewski, 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 the farfetched robot apocalypse of Sci-Fi films becomes an impending reality of our daily life, the reaction of the fashion industry remains mixed. 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 other hand, scepticism takes the reins and demands to know:

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

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 fashion companies looking to incorporate AI into their business. Come and take a look at the past, present, and future of AI and its implications for the fashion industry.



From Farming To Artificial Intelligence

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 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!”.


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


An Industry 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. “I think AI is going to transform everything… the supply chain, design. 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”.


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 such a disaster scenario just yet. Rather than replacing humans, AI will be supplementing existing 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-led skills training into their degree programs to raise the next generation of industry 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

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, adapting AI into your 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 product and 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!


A Curated Dataset Is The Key To Success

A lot of traditional fashion and retail organisations do not have the expertise to work with their data. In such instances, Paul recommends collaborating with an external AI expert who will curate their 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!”


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


The Evolution of AI in Fashion

Talking about the evolution of AI as it relates to fashion, Paul reports 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 we take virtual pictures of them”.

See the full interview here.