Use of Emerging Technologies in FashionTech: AI in Fashion for Improved Customer Engagement
A recently published McKinsey report states that digital innovations will enable significant growth and improvements for fashion retailers in 2022.
Even with globally severe conditions due to the pandemic crisis and economic aftershocks, the fashion industry still has a positive outlook.
The global apparel and footwear market is expected to be valued at up to $2.25 trillion by 2025, with an additional 20% growth in online sales.
So what will make some clothing retailers do exceptionally well and stay ahead of competitors?
The answer is in digital tech and consumer engagement, which has a lot of data and working algorithms at the core.
Innovating with AI in fashion will improve both client engagement and operational efficiency.
FashionTech trends of AI usage:
- How AR and AI allow for the advancement of the in-store experience
- How the digital stylist builds fantastic looks
- How visual discovery can augment traditional searches
- How AI could improve clienteling in fashion retail
Let’s explore these by one with applicable real-life cases.
State of the Fashion Industry in 2022 & Ongoing
To get a complete picture of what you can do with emerging technologies, it is vital to check out the fashion industry market and its upcoming trends.
1. Supply chain pressures to increase prices
Challenges including uneven consumer demand and bottlenecks within the supply chain will force prices to increase by 3.2% on average next year. In some cases, the increase could be even as high as 10% or more due to margins.
Stefan Larsson, Chief executive of PVH Corporation, mentioned: “There is a big opportunity to better match planning and buying to demand, and that’s something we learned when Covid hit. The second-biggest thing we learned is to build resilience into the supply chain now.”
Due to closed borders and dependency on global supply, consumers opt more for domestic spending than international brands.
84% of fashion executives in the BoF-McKinsey survey mentioned placing the supply chain on top of their agenda due to material shortages, transportation issues like port shutdowns and container shortages, the rise in freight shipping, and growing uncertainty across some geographical points of sale.
As these challenges will only pile up, it is necessary to pay close attention to control management and focus on complete transparency and agility.
This way, fashion retailers need to reassess their sourcing strategies, upgrade inventory management, and incorporate visibility-enhancing solutions via emerging technologies.
For example, they can use fashion AI for complex dashboards for digital supply chain control, accurate data, and traceability from suppliers.
Meanwhile, H&M Group Chief Executive Helena Helmersson indicates that the firm’s supply chain development is focused on technology with a priority to find “competitive advantages in a supply chain context when it comes to speed, agility, cost efficiency, and price.”
2. Necessity to adjust in fashion assortments
Brands and retailers are expected to recover from the Covid-19 shutdowns within a year. However, depending on the country’s economic state and resilience, this recovery will be uneven across regions and consumer markets. Moreover, 51% of 2019 air traffic between Asia and Europe is expected to recover in 2022.
Additionally, retailers should focus on learning data-driven product development and adjusting inventory mix to create suitable clothing assortments to match client preferences.
These days, more consumers spend time online resulting in an interest in virtual goods and services. The idea for fashion retailers is to think of new ways to engage with their consumers like non-fungible tokens, gaming elements, or virtual and AI in fashion, especially for the younger cohort of Gen-Z.
Therefore, international companies must make the correct investment decisions to offer new and creative purchase solutions, especially technological breakthroughs.
3. Growing need for in-app shopping experience, sustainability, & authenticity
All in all, the fashion industry will be driven by the following vital factors:
- Social shopping
eCommerce via social engagement across brands and customers for brands in line with new functionalities. This social eCommerce channel unlocks new sales opportunities through unique experiences from in-app discovery to checkout. Retailers and brands should check out different use cases, test options for in-app purchases, and technologies like live-streaming and augmented reality try-on.
- Circular economy
Fashion retailers are pressed to reduce their negative environmental impact, work on their recycling processes, and limit the use of raw materials to decrease textile waste.
Sustainability continues to gain more importance for end-users, while embedded technological maturity will allow this to be added within the design, production phases, and sorting processes.
- Product passports
According to a poll, 2 out of 5 fashion retailers are eager to adopt product passwords in 2022. This allows them to create transparency and authentication for storing and sharing sensitive information with their customers and partners. In addition, passports take measures against counterfeiting, differentiate the product assortment, and create loyalty by building consumer trust.
- Cyber resilience
The fashion business, like any other, faces the challenges and risks of cyber-attacks and incorrect data management and operations. In addition, rises in regulatory and consumer pressures are driving brands to urgently resolve and invest in their digital data security and strategic defense.
Therefore, fashion retailers are pressed to innovate and adapt circular business models via pilot projects and modern applications. Those who adopt digitalization, sustainability experiences, and cover cyber security and inventory management and costs will receive the best outcomes once the industry recovers.
4 Key Game-Changing Solutions for Using AI in Fashion
Since you now have a better idea of the technology helping to win over the market and meet client demand, next, we explain how fashion retailers can use AI and other innovations to their competitive advantage.
Jelle Stienstra, Digital Strategy Director at PTTRNS.ai, states the following about AI in the fashion industry: ”AI is an integral part of different industries these days, more and more companies are using the fast-developing applications of AI, and many first movers have embraced AI technology and are applying it for different purposes”.
1. Visual discovery augments traditional search
Clients are prone to search for and purchase clothes online, but many get irrelevant results, spend lots of time searching for a suitable item, and in many cases the clothes do not fit precisely when delivered. This sort of scenario creates negative shopping experiences and brand disloyalty.
AI-driven applications allow users to take screenshots of clothes and use data-educated gear to detect the same outfits and similar garments wherever they can be found. This algorithm delivers the most relevant search results for clients without explanation, making the purchasing process straightforward and satisfying.
Pinterest and Google Photos have already created visual search functionality, but only a few apparel companies use the technology to help their customers find the target goods.
The Lykdat app, called “Shazam for clothing,” can be educative as the solution finds fashion items based on photos.
Ifedapo Olarewaju, Chief Technology Officer at Lykdat, aims to release tech solutions and services that enhance organizational performance and meet customer needs.
Snap Vision solutions help clients shop for items based on a specific photo. In an interview with Bloomberg, MBE, founder and CEO at Snap Vision, Jenny Griffiths, mentioned that the software uses a combination of machine learning and mathematical heuristics to efficiently highlight the image with key attributes of color, texture, and shape to define the item in the photo.
Consequently, similar things are recommended to online shoppers from the product catalog. The software can increase sales due to accurate search results and customer engagement by offering the most relevant bits from the product catalog, even if they couldn’t find what they were initially looking for.
ViSenze software delivers an innovative shopping experience through smart product recommendations, visual search, and product tagging via AI technology. ViSenze set up a process allowing the processing of over a billion monthly queries from retailers, supporting them online and offline.
In his article about the mainstream of visual commerce, Oliver Tan, CEO & Co-Founder at ViSenze, describes that images and videos dominate social media and the importance of creating user-generated content (UGC). This is where AI makes the change: “Artificial intelligence is emerging as a new way to enable innovative retailers to efficiently manage the assault of UGC coming at them each day and turn it from a burden to a boon, at scale. With the ability to process all images and video content, it’s easier to sort, select, and deploy relevant, timely UGC”.
2. Usage of AR/AI in fashion for trying items on
Returning to sizing, AI in fashion helps with accurate item sizing, resulting in great experiences, real-time stock availability, and inventory reduction. The idea of virtual fitting based on a particular goal is lucrative for any fashion apparel company.
Personalization is another essential component to understanding online shoppers’ expectations and influencing their experience based on colors, textures, style preferences, and individual proportions.
Similarly, AI-driven algorithms already work great for Amazon, resulting in a 35% increase in sales in these personalized suggestions.
Fashion is the most polluting industry and only a few retailers use technologies to make their clothing lines eco-friendly and sustainable. Even so, new realities dictate that fashion companies should follow these trends due to pressure from consumers and environmental activists.
Moreover, some companies, like Fabrikant, interact with brands by designing 3D models and animations. These models are not created in the real world and thus do not create a big footprint – instead incorporate higher sustainability levels.
Another example, Dress-X, which allows you to buy digital clothes, wear them on your avatar, and showcase outfits on social media. This way, individuals can experience transformation while reducing textile waste in the industry.
In addition, here are a couple fashion vendors who are also approaching the shopping experience in a new way based on data analysis:
- 3DLOOK combines sizing, personal recommendations, and try-on experiences by taking a couple of pictures on an iPhone.
- FitMatch uses a shopper’s 3D body shape using augmented reality and LIDAR-powered matching algorithms to make personalized product recommendations to fit certain categories.
- Boldmetrics is an example of resolving similar issues without pictures, based on small questionnaires and their own size dataset.
- Perfitly creates digital user avatars based on pictures, measurements, and digital e-garments for sizing recommendations and visualizations.
- Amazon uses virtual shoes that can be tried on by scanning the QR code on an iPhone.
- Snap AR embeds AR to visualize, try on, and experience fashion products before buying them. Snap AR also invested in the Fit Analytics startup to solve sizing problems.
This is precisely how, when using AR and AI in fashion, retailers can reduce return rates significantly in line with increasing conversions and sales. In addition, showcasing products and their context in 3D allows you to offer accurate sizing to delight customers, directly influencing profit margins.
To finalize, artificial intelligence in fashion can be used in the following ways:
- 3D fitting like mobile scanning or smart wear data collection
- Accurate size recommendations
- Integration of product availability with fit & size options
- Virtual reality and metaverse stores for increased user experiences
- 3D fashion designs to remove waste and improve sustainability
3. AI-empowered designers & stylists
Most fashion retailers rely on clothing designs created manually. On the other hand, however, using AI in fashion can help resolve the issues caused by situations like pandemics or crises like labor and talent shortages.
AI-empowered software can design clothes using data-centered algorithms formed by images of previous collections, distinct designers, current trends, and information marketers collect regarding customer tastes, colors, and styles.
Still, little research has been done, and the existing applications are limited. Work should be done in terms of human in the loop, meaning creativity of human stylists and designers could be scaled with AI algorithms to thousands of customers. AI-empowered human stylists will scale their creativity for thousands of customers.
As for some existing examples, the following startups are making some innovations in the area.
- StitchFix provides subscription services scaling and improving human stylists’ work by using many algorithms with excellent descriptions from data science and machine learning advisors like Eric Colson, Brian Coffey, Tarek Rached, etc.
- Thread personal styling platform combines human stylists and AI algorithms.
- Intelistyle offers a personalized customer experience with AI by selecting the right clothes and outfits online or in-store.
- Love, Bonito is virtual clothes recommendation technology – LBStylist, driven by data science and machine-learning models according to individual preferences, body shapes, skin undertones, etc.
All in all, AI-empowered human stylists are a great way to provide innovative user experiences, optimize your inventory stock, increase ROI, and reduce textile waste considerably.
4. AI-augmented clienteling, modern POS, & omnichannel
Fashion retailers are starting to differentiate themselves by incorporating clienteling and omnichannel techniques.
Innovate with Clienteling Concept
According to TJ Prebil, Senior Director of Product Marketing at Redpoint, clienteling is an approach used by retail sales associates to establish long-term relationships with customers based on data about their basic preferences, behaviors, and purchases.
Therefore, the core input of modern clienteling is the Big Data about the customer, and the output of this Data provides solutions for personalized recommendations and retailer experiences that contribute to trust and loyalty. The concept extends beyond simple retail and includes every aspect and employee in the chain of client interaction.
This way, fashion retailers can base their client recommendations on a data-driven tool that possesses all the client’s priorities and tastes. This AI-enabled software allows for the creation of personalized Customer Boards, or Lookbook pages, and sends them to customers and bases the offerings on individual tastes.
Items included on the Customer Board are personally curated according to past conversations and customer data obtained through omnichannel interactions.
Again, Human-in-the Loop AI for fashion helps create high-quality AI systems where humans develop data-sets, label them, and train data on ML algorithms and models in order to provide quality outputs. Humans then fine-tune and validate these algorithms for proper functioning, try out different scenarios, and check the quality of the end results.
What can this technology provide?
- Personal recommendations at the right time, with the right context
- Transform store associates into knowledgeable stylists and fashion advisors
Omnichannel approach to sales
Gartner predicts that by 2025, organizations adopting AI in customer engagement and revolutionizing customer care should expect a 25% increase in operational efficiency.
Omnichannel is the multi-channel approach to sales that unites all communication channels with customers and concentrates on a consumer-centered view of the online or offline shopping experience.
For example, the customer journey could start with an Instagram advertisement and continue to a brick-and-mortar store nearby.
The approach, in which customers encounter positive and consistent experiences in every channel, begins with the following critical factors:
- Consecutive, recognizable brand vision and tone
- Personalized communication according to interests and needs
- Distribution of content informed by past interactions and existing customer journey phase
This approach implies a complex system with a high-load backend to maintain, merge, and analyze data from all the sales channels.
Therefore, AI and AR are critical to creating a perfect hybrid retail model that unifies customer journeys and improves the customer experience while streamlining business operations.
Amit Jhawar, the President of Attentive, mentioned interesting insights about abandoned carts such as two-way communication and questions regarding material, fit, or sustainability. The company helps to move beyond the “no-reply” (what a funny definition) world by enabling AI-based digital clienteling.
According to Accenture, 69% of customers strive for multi-channel service, while only 13% believe fashion retailers are reaching their full potential by providing physical and digital customer experiences.
AI enables fashion retailers to scale based on insights into consumer needs and preferences, closing the knowledge gap, and preparing solutions. Well-known brands like Oasis, Starbucks, and Virgin Atlantic use AI to provide top-notch omnichannel experiences.
As for existing examples of utilizing AI in omnichannel strategy, many well-known fashion brands are on their way to succeed with its help:
1. Louis Vuitton – is currently an “omny” strategy leader for its associate enablement, as mentioned in 2022 in Newstore’s Omnichannel Leadership Report. The brand incorporates tech solutions and partners with Google Cloud to use AI in fashion for their brand ambassadors and associates for an enhanced customer experience.
As mentioned by LVMH’s chief financial officer, Jean Jacques Guiony: “they are no longer just sales professionals, though—they are brand and style influencers who support each service across the customer journey.” This, too, is what defines Louis Vuitton as a luxury brand, insisting that customers will continue to head to stores due to the experience they receive.
The aim is to use AI for customer analysis, trend forecasting, and inventory management to fulfill unique CX and personalization.
The company states: “AI will increasingly touch every part of the operation at LVMH, from product development to the supply chain and interactions between employees and customers”.
2. Topshop – the only high street brand that appeared in London Fashion Week, uses data from its social media (Twitter) and uses an emerging trend based on hashtag #LFW. As a result, they launched ‘Digital Billboards’ that displayed relevant outfit looks near their shops as one of their campaigns during UK fashion week.
3. Neiman Marcus – the brand incorporates innovations to boost customer interaction and experience. This strategy includes online stores memorizing individual’s clothing and shoe sizes and thus offering only relevant available outfits. In addition, the Neiman Marcus app “Snap. Find. Shop.” allows customers to upload photos and, with the help of smart recognition, check out similar items from their catalog. As well, the in-store experience of “Memory Mirror” helps clients record 360-degree videos while trying on clothes for wiser buying decisions.
Those companies orienting on omnichannel POS and сlienteling should look at solutions that bring additional value to their customers. Therefore, it is necessary to embed these solutions, as mentioned earlier, that raise the level of clienteling by:
- Cutting the time for fitting and sizing
- Bringing the “Personal Stylist” experience into the store/online by equipping associates with corresponding software
- Merging customer’s searches and sales associate recommendations of generated looks based on virtual try-on
Future of Fashion & AI Technology Adoption
As seen through many examples, technological advancement will impact the fashion industry shortly. Therefore, using artificial intelligence in fashion has become not only trendy, but a necessary feature, for any retailer to win over the market after the recession.
AI technology helps in multiple ways, but it is necessary to decide on ROI and what of the following aspects should be implemented in the first phase:
- Logistics automation and waste management
- Inventory management with shelf-scanning robotics
- Fitting & sizing recommendation solutions based on measurements and pictures
- Virtual try-one experience and AR-based 360-degree fitting
- Personal styling solutions, as a part of mass customization, based on the human-in-the-loop approach
- Omnichannel and clienteling service
- Online search recommendations & easy search by image
- Gaining more understanding of consumer behavior
- Apparel and collection design
- Automated authentication of a brand and its collections
- Fashion trend forecasting
- And much more.
At Neurons Lab, we work a lot with RetailTech and FashionTech companies focusing on the co-innovation and co-development of the most prominent AI-based solutions. Our company comprises creative teams including tech specialists, scientists, and fashion industry professionals.
Among our recently-tailored cases are state-of-the-art customer engagement and virtual try-on solutions for leaders of the FashionTech market.
Looking for AI expertise in the fashion industry?
The team at NeuronsLab has vast experience and knowledge in creating AI prototypes. With hundreds of models created, we can add value to your organization by providing dedicated industry experts, data scientists, and doctorates in science to analyze and validate the data according to your business needs and tech implementation.