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Navigating AI Advancements in Retail, the Risks, and the Rewards

5 Mins read

The Odd Couple (Introduction)

Artificial Intelligence and Fashion have never been synonymous.

While one represents the latest in algorithmic technological learning, the other is the bastion of creative individualism and human expressionism. Yet, despite these seemingly inequitable differences, AI and Fashion have formed an unlikely union that is leading to both continued

in the present and into the predicted future. The global AI in the fashion market size is currently expected to grow to USD 1,260 million by 2024.

The following will explore AI’s applications in fashion retail with high-level insights into the current direction of the technology and the areas best fit for its implementation.

If The Price Is Right (Pricing Optimization)

Applied statistics can “read the mind” of consumers in regards to pricing options when used correctly. Personalized pricing ensures the right price is offered to the right customer, better supporting a successful sale. To some extent, e-commerce has returned the world to pre-price tag days by making it possible to change the price in a flash, for all consumers or individual consumers. Single pricing for all leaves some customers unable or unwilling to buy and others with surpluses — i.e., the gap between what they would have been willing to pay and what they did. To a seller, a consumer’s surplus is profit left on the table. To consumers, it’s called a deal, and they tend to love it. Personalized pricing aims to eat up all the surplus it can.

Along with digital displays, the internet has opened up a trove of data that feeds pricing strategies, including user location, IP addresses, web visits, past purchases, click-through speeds, and social media “likes.” Often online consumers volunteer information such as their birth dates, education levels, and occupations. All of those data streams can feed into personalized pricing.

According to the Deloitte report “Consumer Experience in the Retail Renaissance,” 40% of retailers that have adopted AI use it to tailor pricing and promotions in retail time — putting it among the very top uses of AI technology. However, research dating back to the early 2000s on the topic reveals that individualized pricing reduces overall trust for businesses and that prices shown to different consumers are perceived as unfair. More recently, academic researchers expanded on this phenomenon, reporting that consumers are more amenable to personalized pricing if they have some role in the pricing process (in this case, via negotiating).

Slow and Steady Doesn’t Always Win the Race (Auto-Generated Product Descriptions)

Customers often make their final purchase decision after reading the product description, short literature summarizing the key features, specifications, and rewarding attributes of a particular product. It is an undeniably important part of any e-commerce website and plays a pivotal role in any sale. If they have not already decided to buy, the description is a business’ last chance to influence customers’ decisions. Yet despite the value provided by such text, adding the best descriptions for an entire catalog remains challenging and time-consuming for businesses, leading to numerous oversights or ungodly costs. However, while it takes about 18 months to complete 10,000 descriptions, AI content generation tools can do it within a few hours.

Many retailers, or more specifically, retail employees, fear the implementation of such AI solutions as they believe it will replace their jobs. In reality, many AI solutions are aimed at creating a collaborative experience between machines and humans to make retailers’ day-to-day jobs easier and more efficient. My company, OMNIOUS.AI, is an outspoken advocate for promoting AI’s collaborative aspects while providing effective solutions such as Auto-generated Product Descriptions with OMNIOUS TAGGER. By leveraging AI, businesses have the potential to create a dynamic description that addresses the interests of each buyer, resulting in more sales and higher customer satisfaction.

Start Your Engines (Insight Engines / Personalized Search)

It is well known that customers determine whether your business offers the products they are looking for via an initial site search. The accuracy of these searches largely depends on the specificity and thoroughness of product tagging. Providing the most relevant results for written queries is a top priority goal for any retail fashion business. To enhance the effectiveness of site search engines, businesses can use AI-powered insight engines.

Insight engines apply relevant methods to describe, discover, organize, and analyze data. Each engine allows existing or synthesized information to be delivered proactively or interactively, and in the context of digital workers, customers, or constituents at timely business moments. The AI-driven technology enables businesses to combine search with machine learning capabilities to make internal search more personalized and accurate.

Show What You Know (Inventory Planning)

Preparation is an important part of any business, especially within retail, where out-of-stock products can upset consumers and ruin sales. Artificial intelligence can be an extremely valuable tool within this area as AI solutions can process significantly more data than humans. AI-powered demand forecasting solutions can provide more accurate results and prevent cash-in-stock and out-of-stock cases that businesses may encounter.

According to a global report from Mckinsey Digital, AI-powered forecasting can reduce errors by 30 to 50% in supply chain networks. The improved accuracy leads up to a 65% reduction in lost sales due to inventory out-of-stock situations and warehousing costs decreasing around 10 to 40%.

Conclusion / Risks

While there are many benefits to AI technology, there are also risks. Allowing technology to take on an increasingly important role in business puts a lot of trust in the hands of something non-human. Some of the various topics of concern are: automation-spurred job loss, privacy violations, algorithmic bias caused by inaccurate data, continued socioeconomic inequality, market volatility, and overall customer security.

Anxieties regarding data breaches putting consumers at risk for exploitation have long been present in our society as more and more industries have become reliant upon collecting such information. The impersonal nature of AI has been a deterrent as much as it has been a rewarding feature. While business costs are decreased and control, as well as, efficiency is increased, our opinions of such technology as infallible can lead to potential issues regarding unrealized data corruption or otherwise disturbed functions. Despite this cause for apprehension, retail has been marked as one of the industries advancing with AI adoption, and therefore all businesses hoping to exist into the future must pay careful attention.

Deciding where to start is the first step on the journey of building your AI network. Knowledge is power and power is what you will need to survive in an increasingly competitive industry.

Jaeyoung Jun – Founder and CEO of OMNIOUS.AI

As an award-winning industry researcher, Jaeyoung Jun’s credentials include conducting R&D for major government agencies and technology companies worldwide. Jun has been leading the digital revolution of the fashion industry since 2015.

While working towards his Ph.D, Jun founded OMNIOUS.AI after seeing the potential applications of deep learning technology in the fashion industry. Jun had the vision to commercialize technologies that can read, quantify, and locate specific fashion data and trends directly from images. Jun’s contributions to online fashion businesses, e-commerce companies, and retailers were awarded in 2020 when he was selected as the grand prize winner in the startup sector at the 30th Korea Textile and Fashion Awards.

Before founding OMNIOUS.AI, Jun led AI research as a Ph.D candidate at Korea’s first public, research-oriented science and technology institution, KAIST (Korea Advanced Institute of Science and Technology). He has conducted R&D with various companies and organizations such as LG Display, SK Telecom, and the U.S. Air Force. Jun was recognized early on in his research as a first-place winner in KAIST’s E5 Startup program in 2015. His first-place honors expand beyond there to include wins in the Neurorobotics Competition (sponsored by Korean Society for Computational Neuroscience), and the Big Question of Changing the World X-Project (created by the Ministry of Science, ICT and Future Planning).

Jun is also an evaluation committee member of the Korean government agency, the National Information Society Agency for artificial intelligence businesses. Since 2020 his responsibilities have included planning and developing various business models and R&D. As an evaluation committee member, he is in charge of making government data open and encouraging the collection and use of sector-specific data.

AI advancements stock image by metamorworks/Shutterstock

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