About the author
Michael Ayomide Johnson, Software Engineer at Meta. With over a decade of experience in the Payment and FinTech industry (including debit card personalization, issuance, and payment processing), Michael Ayomide Johnson now focuses on building services at Meta to proactively detect and enforce against harmful actors on the platform. His expertise in creating large-scale processing systems is vital in his role as a backend engineer
Introduction
AI and VR are gradually changing online shopping. They let customers instantly try things on, help to make better choices and reduce returns. Half of the European consumers show enthusiasm for these technologies and recognise their potential to improve online shopping experiences.
Events like Metaverse Fashion Week also set new standards for customer interaction in eCommerce. Additionally, voice search, facilitated by devices like Alexa, and visual search, adopted by retail giants like Amazon, eBay, and Alibaba, are simplifying product discovery and enhancing engagement, ushering in a new era of personalised shopping experiences in eCommerce.
Online shopping is starting to feel more like a real experience, ushering in a new era of personalised eCommerce. But at the same time, these technologies dramatically influence shopping at brick-and-mortar shops. Join me to take a look at how artificial intelligence and virtual reality change the way we shop.
AI and In-Store Shopping
Despite the growing popularity of e-commerce, physical stores remain a preferred choice for many shoppers. However, consumer expectations have evolved, with a desire for both traditional retail benefits like friendly service and modern tech features found online.
To compete with e-commerce, brick-and-mortar stores actively implement new technologies including artificial intelligence.
AI-assistants
Skin-care retailers and cosmetics stores use AI systems to analyse customers’ facial features and skin types. The AI-driven face recognition software offers comprehensive advice on makeup and skincare products and provides information about the user’s skin condition based on a photo. Thus, customers get personalised product recommendations and an enhanced shopping experience. This also minimises human effort and helps the company save the personnel training budget.
An illustration of such innovations is the Style My Hair app developed by L’Oréal. The app makes it possible to try various shades of blonde, brown, red, or coppers. Based on the 3D hair colour technology, Style My Hair seamlessly follows the natural flow of hair, making it easy to choose what suits you best. Once a customer has found a desired look, the app helps to locate a nearby L’Oréal salon where skilled professionals make the hair colour exactly according to the AI recommendations.
Another inspiring real-life example is Perfect’s In-Store Virtual Makeup Try On & Smart Beauty Mirror. This comprehensive AR virtual makeup solution allows shoppers to try on makeup in real time using in-store smart beauty mirrors.
It is a safe alternative to conventional makeup testers; consumers can explore different products virtually before purchasing. This solution ensures a hygienic and engaging shopping experience while addressing current concerns regarding the safety and sanitation of traditional makeup testers.
Streamlined Checkout
AI-powered checkout systems, such as cashier-less stores or self-checkout kiosks, streamline the payment process by automating transactions. With the help of computer vision and machine learning algorithms, these systems recognise products and process payments seamlessly, reducing wait times and improving efficiency.
Self-checkout kiosks offer shoppers a convenient way to scan and pay for their items using their smartphones, bypassing the need to wait in line at the checkout counter.
This technology allows shoppers to scan product barcodes with their phones, choose from multiple payment options, and complete their purchases without the hassle of queuing.
Apart from this, AI algorithms make offline shopping more secure. They analyse transaction data in real-time and can detect suspicious behaviour preventing fraudulent transactions. Biometric authentication methods, such as facial recognition or fingerprint scanning, enhance security and reduce the risk of payment fraud.
Supply Chain Forecasting
Machine learning is becoming actively used for product demand prediction across various channels and timeframes. It improves planning processes in merchandising, supply chains, and operations. Taking into account that in recent years, managing supply chains has become even more challenging, AI and ML prove to be indispensable tools in this sphere. Market turbulence exacerbated by the COVID-19 pandemic and the conflict in Ukraine has only emphasised the necessity for adaptability and versatility. Consequently, businesses and stakeholders are now prioritising the resilience of supply chains in the face of rapid market changes.
Gartner forecasts that by 2025, 70% of organisations will integrate AI architectures, crucial for retail efficiency and future readiness. With the help of AI-driven supply chain management systems companies improve demand forecasting accuracy and can even create new job opportunities.
Various retailers, including Walmart, IBM, UPS, Intel, and Procter & Gamble, employ AI and machine learning to optimise their supply chain operations. They use these tools for various purposes, for example, Walmart applies AI to predict customer demand, UPS uses ML to optimise package delivery routes. Similarly, IBM offers AI-powered solutions that analyse data from multiple sources to provide actionable insights for decision-makers.
VR and AR in Off-line Shopping
The National Retail Federation found that 97% of shoppers abandon purchases due to inconvenience, with both in-store and online shopping being problematic. Trying on the clothes often becomes an obstacle to a purchase. People might hesitate to try things on physically even if they’re already in the store for various reasons. Some are concerned about hygiene, particularly with shared fitting rooms, especially in light of public health concerns. Others may feel constrained by time, as trying on clothes can be time-consuming, and sometimes there are long lines or limited staff assistance. Privacy is another factor, as many people feel uncomfortable changing in public spaces. Additionally, physical discomfort or personal preferences for trying on clothes at home, where they have more control over the environment, also play a role.
Virtual Fitting Rooms (VFR)
Virtual Fitting Rooms solve these issues by allowing shoppers to quickly preview clothes without changing. VFRs also address the limitations of online shopping, as 62% of consumers prefer physical stores for the tactile experience. According to a Retail Perceptions Report, 40% of buyers are willing to pay more for products experienced through AR technology.
What is more, VFRs reduce return rates, which are a headache for fashion brands. The technology enables shoppers to check fit and size without trying on clothes, whether in-store or online. Many retailers, including Adidas, Sephora, Zara, and Uniqlo, have already implemented virtual fitting room technology in their Flagman stores.
Ralph Lauren has also incorporated virtual mirrors into its physical fitting rooms. These mirrors use Oak Labs’ RFID technology to identify the products a shopper brings into the fitting room. Combining this data with product details like sizes and colours allows the mirrors to provide additional product recommendations. Mirrors have a high interaction rate and allow the brand to gather valuable data from its in-store fitting room software.
Pricing and Clearance Optimisation
To foster company growth, many companies are implementing price optimization strategies. This approach can potentially alleviate the stress of seeking growth avenues and aid in expansion. In this case AI proposes optimal prices, ensuring they don’t alienate customers, maintain profitability, or diminish sales of other products in the portfolio.
Implementing AI-driven price optimisation relieves managers of the daunting task of analysing vast amounts of data and making quick decisions, enabling them to set optimal prices for thousands of products weekly or even daily.
Retailers typically progress through stages, starting with SKU-level optimisation and advancing to portfolio, channel, point of sale, and customer-specific pricing, resulting in improved profitability at every level.
AI’s effectiveness lies in its self-learning algorithms, which analyse extensive data and recommend prices that maximise revenue and sales across the product portfolio by considering thousands of interrelationships. By automating time-consuming tasks, AI frees up resources for high-level customer-centric decision-making.
Wrapping Up
AI and VR have great potential to change offline shopping for the benefit of customers and retailers. AI-powered personalised recommendations will continue to enhance the in-store shopping experience by providing tailored product suggestions based on customer preferences and purchase history.
VR technology employed more and more often will create immersive shopping environments where customers can virtually explore products and experience them in a lifelike setting before making a purchase. This will inevitably reduce the need for physical inventory on display and enable retailers to offer a wider range of products in limited retail space.
