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.

Intelistyle in Createch’s “Ones to Watch” List

Ones to Watch” list showcases 50 UK-based projects praised by experts for delivering new products, services and experiences through creative and tech expertise. This report, described as “who’s who guide of the UK companies at the forefront of Createch” was the highlight of the Createch 2019 conference held at London’s Code Node on 11th June, as part of London Technology Week. The list was compiled by innovation research specialists Springwise with support from London & Partners and Digital Catapult.

Tim Davie, CEO of BBC Studios and Co-Chair of the CIC, a joint forum of government and industry said: "The intersection of creativity and technology is one of the most exciting growth areas in today’s economy. Createch 2019 will provide a unique guide for anyone trying to navigate through this fast-changing landscape."

Createch has identified 4 key sectors that revolutionize user experience:

1) Immersive Entertainment

2) Transformative Experiences

3) Seamless Service

4) Personalised Tools

Intelistyle among Createch

Intelistyle, AI styling and omnichannel personalisation start-up, is one of the 50 companies featured in the “Ones to Watch” list. Styled by AI” isn’t yet a commonly used term, although that may be about to change, the report concludes. 

Intelistyle feautures in the “Personalised Tools VR ⁄ 3D ⁄ Robotics” category both for its B2B personalisation services to fashion retailers and for the personalisation offered to the end user through its chatbot. The artificial intelligence (AI) software basically offers effective styling results across platforms: from in-store to online, even at home - a truly omnichannel experience.

Intelistyle among Createch

Another reason Intelistyle made the list is the revolutionary technology that outperforms latest academic research to ensure excellent styling quality: Through the use of machine learning, the data from thousands of outfits put together by stylists, influencers, designers and retailers is extracted to decode the 'essence of style' and offer styling solutions.

The same exceptional styling quality got Intelistyle featured in Forbes, for outperforming human stylists during London Fashion Week in 2019.

Intelistyle’s “secret power” is providing customised, on-brand, personalised recommendations to fashion retailers, improving their KPIs (revenue, conversion, clicks-through-rate, average order value and more) while delivering a better customer experience.

Intelistyle in Createch: You can read the full report here.

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

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

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

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

fashion technology blockchain

 

Blockchain Will Fight Counterfeit Products & Streamline The Fashion Supply Chain

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

Label Tags Will Redefine Origin and Ownership

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

Supply Chain Transparency

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

 

Competitive Payment Options Will Dictate Retail Preferences

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

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

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

fashion technology smart mirror

Nobal’s smart mirror solution for fitting rooms

 

Artificial Intelligence Will Change Everything

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

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

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

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

AI Stylist Beats Human Stylists at London Fashion Week

lfw ai fashion stylist

AI Stylist Outperformed Fashion Influencers

In February 2019, after training and testing our AI Stylist for 2 years we decided we were ready for London Fashion Week. We previously spent a lot of time testing with various stylists we worked with, as well as by withholding parts of the dataset from the model and measuring user behaviour in our app. We were itching though to take it to the ultimate fashion event of the industry and see the reactions of random members of the public.

Our goal was not to run a scientific experiment. This happens daily in our lab, where we test with thousands of outfits (see how ai fashion styling works). Our goal was to interact with real people, hear their feedback, see their reactions and most importantly satisfy our curiosity. Would fashion lovers from all over the world be able to tell the difference between an AI and a human stylist?

 

AI Stylist vs Human Stylists

So we decided to design an experiment. We generated 10 sets of outfits. In each set, we had two outfits of similar style: One generated by our AI stylist and the other created by Instagram fashion influencers.

The question we asked participants, was “Which outfit was created by an influencer?”. Here is an example.

ai fashion stylist

 

We used SurveyMonkey to run the survey. Our sample consisted of 27 participants, 82% of whom were female. Each correct guess got 1 point. Participants scored on average 46%. For reference, if they were randomly responding to the questions they would have scored 50%. The highest score by a participant was 80% and the lowest 10%.

 

Often participants asked what were the criteria to make the decision and our answer was “Choose the outfit you like best”. 70% of them prefered the outfits created by our AI Stylist.

How the AI Stylist Works

So how do we do this? We examine thousands of outfits put together by human stylists, influencers, designers and retailers. Through deep learning, we extract the essence of style. We go a lot further than your typical computer vision techniques that focus on pattern recognition. Using machine learning to find visually similar clothes is commonplace these days.

However, style is a lot more nuanced than finding similar patterns or colours. Two seemingly different patterns or fabrics can be combined together to build a stunning finished look. And two pieces of clothing that would normally clash, can be brought together in a beautiful outfit with the use of right accessories. Employing the latest deep learning technology, we have just started decoding the genome of style which enables us to assign a unique signature to every piece of clothing. AI is only at the beginning of its journey but it’s making huge leaps every day.

 

Why is this important? Well, it is a huge step for diversity in the fashion retail industry – both luxury and fast fashion retailers are putting inclusion to the forefront of their offering. While body positivity is a huge movement, most retailers still build their lookbooks through studio photoshoots with fashion models. This means that looks offered to customers on retail websites cater to one body type, skin tone, hair tone and eye colour. Offering styling advice to each customer is expensive for a retailer. And what about different occasions, weather and style preferences?

 

AI offers the potential to scale fashion styling advice, creating outfits personal to the needs of each customer, celebrating diversity and individuality. Companies such as Stitchfix have already demonstrated the power of that approach. We want to take this further, allowing customers to digitise their wardrobe, style their own clothes and receive a personal service across their favourite retailers.

Read the full Forbes article on the success of our AI Stylist at London Fashion Week.

 

Interested in learning more? Get In Touch!

 

your email

your message


Nobal iMirror

Omnichannel Retail: Competing in new and innovative ways

This article has been updated for omnichannel retail trends of 2020 and was first published in November 2018

 

OMNICHANNEL RETAIL: RISING TRENDS of 2020

 

Rising Online Trends

Personalised Online Experiences: Data-driven personalisation in retail using innovative technologies such as Artificial Intelligence (AI) will deliver customer journeys that are tailored to individual visitors, not customer segments. Personalised outfit recommendations will allow fashion retailers to trickle down the value of a VIP personal styling experience to all customer segments to increase both average order value (AOV) and brand loyalty.

 

Conversational Commerce: Next-generation chatbots that can adopt a brand’s tone of voice and identity seamlessly will bridge the gap between online and offline experiences. Best practices of a true omnichannel retail experience will be achieved through strategic handovers from the chatbot to a brand representative where a human touch is needed.

 

Rising In-Store Trends

Return of “Neighbourhood Stores: According to The State of Fashion 2020 report by BoF and McKinsey & Company, smaller brick-and-mortar stores with highly focused assortments and hyper-personalised services at urban, decentralised locations will play an increasingly important role in omnichannel retail experiences. To answer this consumer demand for convenience, immediacy and more intimate interactions, online players such as Nordstrom are opening smaller format local stores with a different product selection tailored to each location.

 

Rise of “Experiential Stores: Physical stores that don’t have any inventory for sale will act as experience hubs for the consumers to build a more personal relationship with brands and their products. Consumers will be redirected online to make the actual purchase. Outerwear brand Canada Goose has been one of the first brands to test out a truly experiential store where visitors can try on their famous fur-lined hooded parkas walking around Cold Rooms that imitate the look, sound and feel of dramatic winter weather. Shoppers can only buy a coat at the exit via smart screens linked to the brand’s online store.

 

The “Department Store” Reimagined: Industry giant LVMH unveiled the plans for reopening iconic Paris department store La Samaritaine in 2020 and Galeries Lafayette made a bold move to design a concept store-department store hybrid complete with an army of tech-savvy personal stylists and upmarket food halls as a “retail laboratory” to test out innovative practices.

 

Concerns around In-Store Surveillance : In a bid to level the playing field with digitally native brands, some brick-and-mortar retailers are installing sensor technology and smart cameras in-store to monitor movement throughout the floor in addition to estimating a shopper’s personal information such as age, sex, and ethnicity. The fact that people are not openly consenting to give information about their movement is estimated to generate a major privacy backlash and retailers are warned to approach such integrations with caution and sensitivity.

 

As technology blurs the distinctions between physical and online touchpoints, retailers will need to rethink their competitive omnichannel retail strategies. We chat with Thomas Battle at Nobal who are enabling retailers to bridge the gap between offline and online through their iMirror technology.

Talk to us about your latest venture and what excites you about it.

Right now retail is undergoing a revolution where the definition of shopping is being re-defined. Is the future of retail entirely digital? Does brick-and-mortar still excite shoppers enough to bring them into stores? Will an omnichannel combination of e-commerce and physical retail drive customers to purchase more?

We believe that the future of retail is experiential shopping that combines the best features of e-commerce with the best of a brick-and-mortar store. Customers will come to a store to partake in an experience and just happen to buy a fully customised product that will be sent to their home via drone.

Experiences will range from workouts to educational workshops to community meetups during which customers will be surrounded by a tailored brand, community and set of products that shoppers will engage with on an emotional level. Shoppers will build outfits that match their personalities and then have them delivered directly to their homes.

We’ve built the iMirror, the world’s most advanced interactive mirror, to be the digital interface that facilitates the experiences delivered by these stores. The iMirror brings every mirror in the store to life and allows brands to engage their customers with an intuitive digital experience. Our flagship experience, the digital fitting room, is already allowing customers to get product recommendations, order out of stock inventory, and communicate with sales associates in real-time anywhere in the store.

Re-imagining and re-inventing an industry with major retail thought leaders, and building products that change the way we interact with the physical and the digital worlds is incredibly exciting.

omnichannel retail smart mirror

Nobal’s iMirror bring online commerce into the physical store.

What do you see as the biggest challenges in omnichannel retail these days?

The biggest challenge in omnichannel retail is creating those unique in-store experiences. A production that draws in people not just to shop, but to learn, interact and enjoy.  Right now every store needs to look at themselves and ask “Is coming into my store a fun or amazing enough experience that I overcome the convenience of buying products online?” Creating these experiences is hard and the companies exploring this space are exploring a new frontier of opportunities. That being said, for the first companies to discover how to do this right, there is a lot of long-term upsides.

 

What are the most important trends that you see and how do you see omnichannel retail changing in the next 5 years?

Combining physical and digital sales channels into true omnichannel retail experiences is probably the most important trend currently in retail. Most companies have siloed their online and brick-and-mortar experiences and they don’t talk to each other. Each sales channel has their own value and issues, by combining the best features of both, you create an environment in which your customers have a much better experience in your stores.

omnichannel retail smart mirror close up

Immersive in-store experiences created with the iMirror

 

What makes a great omnichannel retail experience? Are there any companies that you think are nailing it in this space?

Great omnichannel strategies are ones that seamlessly integrate the technology into the environment around you. Technologies like tablets are at a huge disadvantage because they take you out of the shopping experience. Companies like Perch are doing a great job of this seamless omnichannel integration in places like Sephora, (and us of course!).

Nike and Perch Omnichannel Retail Experience

Perch in collaboration with Nike – reimagining the retail store experience based on merging the Physical + Digital.

What is one habit of yours that makes you more productive as a business leader?

Task prioritization is one of the hardest things in the world to do and (at the same time) one of the most valuable things to get right. For me, I work through living To-Do lists that keep me on track at a macro and micro level throughout the day.

What is the one book that you recommend our audience should read and why?

The Up Side of Down is one of my favourite books. As a startup, it is key to celebrate and embrace failure. Failure is how we learn and grow as human beings. Once we start fearing failure, we become stagnant and cease to grow.

What is the one piece of advice that you would give to business leaders looking to incorporate innovation into their strategy? What’s the best way to make that happen?

Right now, no one knows what the store of the future is going to look like. In 15 years, it is going to seem like the most obvious thing in the world as to what experiences become omnipresent in retail. The market is ultimately going to decide what works and what does not. Companies that take risks, test and iterate on as many innovations as possible will be in a much better position to take advantage of the huge upside that comes from being the first to market with a new breed of the shopping experience. The only other option is to risk going the way of Sears and Toys R Us.

 

AI in Retail: How Fashion Can Leverage Innovation

Innovation is an essential component to success in retail today as AI opens the doors to enhancing and scaling operational capabilities that businesses traditionally had to execute manually.

Kostas Koukoravas speaks to Validify‘s CEO, Fergal O’Mullane, about the biggest innovations in fashion retail and how retailers can leverage AI technology to stay ahead of the curve.

 

Talk to us about your latest venture and how did you come up with the idea.

Validify helps leading retailers access curated and vetted information on the latest emerging retail technology from around the world. I have been working in the AI retail tech space in London for the last 15 years, consumer shopping behaviour has transformed in this time on the back of transformative innovation, in particular, the use of smartphones and social media. The number of tech companies launching new solutions has also increased exponentially and retailers are finding it increasingly difficult to identify the right technology for them.

We founded Validify to help retailers to discover groundbreaking innovation and to help streamline the selection and adoption process.

 

What are the most interesting applications of A.I. in the retail industry?

It’s very early days for AI in retail. Artificial intelligence is an overused term and encompasses a number of areas from machine learning to deep learning all the way through to image recognition and natural learning processing (NPL) that underpins voice technology. At present, the application of AI in the retail industry is limited or in ‘pilot’ phase. Despite current limitations, there are interesting applications of AI in the areas of personalisation, stock inventory, search and customer service (chatbots) to augment retail operations.

AI in Retail: Personalisation

Retailers turning to machine learning and predictive analytics to serve up personalised content and outfit recommendations to their customers. Whilst conceptually not new (Amazon has led the way in this for years), the detailed level of customer and product attribute tags that can now be assessed in real-time significantly improves the personal experience.

 

personalised ai retail landing page

 

AI in Retail: Inventory Management

AI is used in retail to automatically analyse swathes of data to predict demand, forecast inventory and replenish in real-time. Can reduce stocks, excess build-ups and the need for markdowns. It also enables retailers to stock the store with different products depending on demographics.

 

shopping mall ai retail

AI in Retail: Image analysis

Prevalent in the beauty industry, facial recognition technology being used to provide customers with personalised recommendations based on skin types. Image analysis also being used in-store to analyse customer footprint and sentiments in relation to the environment and product.

 

smart mirror ai retail

Panasonic showcases its new Future Mirror at CEATEC 2016

AI in Retail: Chatbots

Though still in relatively early stages, NLP is being used to enable these chatbots to interpret human language and sentiment in order to respond in ‘human-like’, conversational manner. Similar to the technology being used in Amazon Alexa and Google Home etc.

chatbot ai retail

 

Can you give us some examples of the most innovative companies in retail? What makes them stand out?

There are some great examples of innovation coming from both mature retailers and the newest online players.

H&M is a great example of a company that used to lag behind. However, in recent years has deployed innovative technology across the business including investing in automated warehousing, employing AI-driven inventory management technology to its recent development of voice-activated mirrors in its flagship store in New York. Ikea is another great example of a company embracing innovation as a core pillar of their business. They continue to invest in new technologies such as Augmented Reality (AR) and Virtual Reality (VR) whilst partnering with Apple and Amazon to further their technology ambitions. They were one of the first to use AR in a practical application enabling customers to visualize the product in their homes without ever going near a store.

 

virtual reality retail

 

On the other side of the spectrum, new retail incumbent Stichfix is innovating with fashion design, using AI to create and design garments reactively to consumer opinions/ buying habits.

What makes these companies stand out is their understanding that innovation and technology is core to the future of their business, not an afterthought. In particular, these companies are willing to experiment with new technologies ahead of their competitors via trial and error. The acceptance that failure is part of the innovating process and the ability to move on quickly will enable these retailers to potentially stay ahead of the curve.

What are the biggest barriers to adopting AI and innovation for mature companies in retail?

Mature companies are often burdened by legacy technology, processes that prohibit technology adoption and a culture that doesn’t foster innovation. Mature businesses working off legacy technology lack the agility to adapt to the pace of change we are currently seeing in the retail space. This is conflated by the elongated internal processes and decision-making often found in larger, more mature companies. The culture of ‘innovation’ has traditionally been harder to foster in more mature companies often seen as the responsibility of a single person/ team to manage. Without company-wide acceptance that innovation is necessary it acts as a barrier to adoption especially in the current climate where retailer budgets are under increasing pressure. Finally, many companies (not just mature) struggle to know ‘how’ to innovate, what technologies to employ and where to source them.

 

robot ai retail

“…businesses need to take a more agile,  test and learn approach to innovation…don’t be afraid to fail, but fail fast…this is the approach taken by the most successful businesses in the world…”

What are some good ways to overcome them?

To overcome some of these challenges you often have to start to form the top – getting senior management to embrace innovate and make a commitment to invest in doing so. Also, businesses need to take a more agile,  test and learn approach to innovation…don’t be afraid to fail, but fail fast…this is the approach taken by the most successful businesses in the world…including the biggest retailer Amazon.

What do you think the impact of Brexit will be in London as an innovation hub?

Without having a crystal ball, it is difficult to predict the exact impact Brexit will have. There are concerns that incredible talent available in London will move out. The reality is that London already relies on talent and companies outside the UK to help support it as an innovation hub. At Validfy we continue to work every day with technology companies looking to settle in London and build out their teams. There are still huge investments being made into the UK in general and some of the biggest technology companies are still setting up in London.  The UK has over 5million start-ups and that continues to grow – made ever more apparent by the number of co-working spaces popping up in London to support the ecosystem. We do not believe that London will collapse as an innovation hub overnight but it may need to adapt.

What is the one piece of advice that you would give to business leaders looking to incorporate innovation into their strategy?

The first step should be ensuring you truly understand who your customer is, what value you deliver them and what can you do to deliver the best possible customer experience. Innovation is a powerful enabler, but the fundamentals of being a successful retailer haven’t changed, you need to deliver a great product and a great customer experience.

Retailers also need to take a more agile approach to innovation, they need encourage a test and learn the methodology to innovation…it’s ok if something doesn’t work, pilot it, if it works roll it out, if it fails throw it out, the important thing is to fail fast!

The 4 strategies to win in Fashion & Luxury Post-Covid. 💡 Download the whitepaper for FREE! 💡
+