Ai in retail- a woman interacting with a robot in a shop
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29/02/2024

AI in Retail: Key Trends and Use Cases for 2024

AI offers a plethora of solutions for the retail industry. How can retailers leverage them to boost sales, delight customers, and stay ahead of the curve?

Paulina Twarogal

Content Specialist

Andrzej Puczyk

Head of Delivery

Gone are the days of one-size-fits-all recommendations and bland product descriptions. Today’s retail landscape is undergoing a revolution fueled by artificial intelligence. It’s transforming the way people shop and interact with products, creating a more personalized, engaging, and efficient experience for both businesses and customers.

With the global AI in retail market size projected to hit a staggering $45.74 billion by 2032, it’s clear that AI is here to stay. But what specific AI solutions are driving this growth, and how can retailers leverage them to boost sales, delight customers, and stay ahead of the curve? Let’s dive deeper and explore the exciting possibilities.

The role of AI in retail

Even though AI adoption is still in its early stages, 87% of retailers have already deployed the technology in at least one area of their business. 60% of retail companies are planning to boost their AI investments in the near future, and by 2025, 80% of retail executives expect their organizations to adopt AI automation.

As AI’s presence grows, retailers face a crucial choice: embrace it to unlock strategic applications and boost performance or risk falling behind. It seems that ignoring AI is no longer an option. Particularly if customers are not only willing to use GenAI tools to enhance their online shopping experiences but they’re also enthusiastic about them. 

For example, 87% of shoppers who have tried a GenAI tool are excited about the positive impact AI has on their shopping experiences. 73% of consumers are open to AI-powered chatbots for customer service, and 60% have already used virtual assistants to make purchases through voice commands.

In essence, AI represents a pivotal turning point for the retail industry. Those who embrace its potential will thrive in the new era of retail, while those who resist risk becoming relics of the past. In fact, it’s been found that 69% of retailers have reported an increase in their annual revenue as a result of adopting AI, and 72% of those already using AI say they experienced a decrease in operating costs. What’s more, McKinsey forecasts that through improving digital customer interactions, artificial intelligence could bring in an extra $310 billion for the retail sector. Sounds impressive, doesn’t it?

What are the main reasons behind the increasing use of AI in the retail industry? These include:

  • Supply chain and logistics;
  • Improving products;
  • Guiding customers in stores;
  • Analyzing payments and pricing;
  • Managing inventory;
  • Customer Relationship Management (CRM).

AI use cases in retail: What solutions are there?

AI brings a wide range of opportunities to the retail industry that extends far beyond improving customer experience and generating unique content. It streamlines business operations and enables you to maintain a competitive edge. Take a look at some noteworthy AI applications in retail below.

Personalized product recommendations

As more consumers expect brands to understand their preferences, AI is becoming a major game-changer. 75% of retail customers are more likely to buy again from brands that personalize their experience. That being said, it shouldn’t come as a surprise why companies decide to deploy AI solutions on a larger scale than ever before.

AI allows you to analyze large amounts of customer data, such as browsing and purchase history, items added to a cart, and demographics. This level of analysis means that you can offer product recommendations tailored to your customer’s tastes and preferences. This not only simplifies shopping but also makes it more enjoyable, guiding customers toward better purchase decisions. In fact, brands leveraging advanced digital personalization tools see revenue jump by 6% to 10% faster.

AI retail use case: AMAZON

AI in retailAmazon stands as the best example of using GenAI to create personalized recommendations. As an omnichannel retailer, it customizes its homepage for each customer using AI-powered analytics and data collected on their purchasing behavior, preferences, wishlists, and items in their cart.

By analyzing both past and real-time data, Amazon gains valuable insights into its customers’ preferences. This allows the company to create highly personalized marketing campaigns that enhance the overall customer experience and satisfaction levels. According to McKinsey, recommendations drive 35% of purchases on Amazon.

Virtual try-ons 

One common challenge customers often face when shopping for clothes and other wearables online is ensuring the perfect fit. Fortunately, gone are the days of relying solely on static images to imagine how a product might look or fit.

Now, with the help of virtual try-ons powered by Artificial Intelligence and Augmented Reality technologies, customers can visualize how clothes, accessories, makeup products, and even furniture items will look or fit them and their spaces. The outcome? Reduced product return rates and increased customer satisfaction.

AI retail use case: WARBY PARKER

AI in retail

Warby Parker offers a virtual try-on experience for glasses and sunglasses, available both on their website and through their mobile app. Their system combines AI and AR technologies to analyze customers’ facial features and virtually overlay eyewear frames. 

Using the customer’s device camera, the tool creates a 3D map of their face to ensure accurate placement and size comparison. This allows customers to try on various styles and colors of frames in real-time, with the system providing instant rendering. As customers tilt, turn, and move their heads, the virtual frames adjust dynamically, mimicking natural movement and light reflection.

Intelligent product search 

Why do consumers leave an online store without buying anything? The top reason is because they can’t find what they’re looking for. According to Nielsen Norman, between 17-20% of users give up after just one unsuccessful search attempt. These abandoned shopping trips have a big impact, costing U.S. retailers an estimated $330 billion in 2021 due to search-related problems. With AI, product search becomes different. How?

AI-powered site search can grasp context and intent, enabling shoppers to input any keywords and get precise, relevant search results. Additionally, as it processes extensive data and learns about each shopper’s preferences, the results become personalized to each individual over time.

AI retail use case: ZALANDO

AI in retail

Zalando utilizes complex AI algorithms to analyze vast amounts of customer data: past purchases, browsing behavior, and saved items. This allows them to personalize search results for each customer and display products that are most likely to interest them based on their unique preferences. 

Zalando uses dynamic filters which evolve based on a user’s behavior. For example, if someone consistently applies size or color filters, these options may be pre-selected or highlighted in future searches. There’s also a search bar that understands natural language queries beyond just keywords. Users can describe desired styles, materials, or even occasions (e.g., “black ankle boots for winter”), and the AI interprets the intent, delivering relevant product suggestions.

Enhanced experience with visual product search

Studies show that 74% of online shoppers in the US and the UK struggle to find the products they want. This highlights the need for better search capabilities, with visual search emerging as a promising solution. It’s estimated that the global visual search market will rise to as much as $33 billion by 2028.

Visual search technology employs image recognition and AI algorithms to enable users to search for products using images instead of text. For users who might not be familiar with specific search terms or type the wrong search terms into the search bar, this makes it easier and faster to find relevant products.

Providing a more intuitive, engaging, and efficient way for discovering and buying products becomes important, especially in a world where 90% of information transmitted to the human brain is visual. And 62% of millennials express a preference for visual search over any other technology.

AI retail use case: ASOS

AI in retail

Asos offers a Style Match feature, which employs visual search technology on its app. With Style Match, users can snap a photo of an item or upload an image from their library to initiate the search process. ASOS’ machine learning algorithms analyze visual information like color and patterns to find a match and provide personalized recommendations to customers.

Hands-free shopping through voice search

AI brings yet another opportunity to retail—voice search, which enables consumers to browse products and make purchases without lifting a finger. They can do it by using voice commands via smart speakers like Google Assistant, Apple’s Siri, Amazon’s Alexa, or other voice-activated platforms. Many retailers are also developing their own voice-enabled shopping experiences directly integrated with their apps or websites. 

Recent statistics show that 55% of consumers use voice search to search for products, and 44% have already used it to add items to their shopping lists. In the USA, 33.2 million consumers have harnessed voice search to make purchases. Why? Simply because it proves useful, accessible to individuals with disabilities, and often much faster. 

AI retail use case: WALMART

AI in retail

Walmart has also tapped into the potential of voice commerce, thereby enhancing the shopping journey for its customers. Through Google Assistant or Siri, users can effortlessly add items to their Walmart online shopping carts, create shopping lists, and even initiate the checkout process using voice commands.

What sets Walmart apart is its focus on user convenience. Shoppers can easily access information about their previous purchases and preferences, which enables quick reorders and reduces the need for repetitive tasks. What’s more, Walmart’s voice commerce platform integrates with its physical stores, offering customers the option of in-store pickup or delivery.

Advanced description generation 

The greater the number of products you have to sell, the longer it might take to write unique descriptions for each one. How about using AI‑powered tools for this task? While not being professional copywriters, AI might quickly generate unique, compelling, and SEO-optimized descriptions, capturing details important to customers. 

Accurate e-commerce descriptions are crucial for setting expectations, building trust, and ensuring customer satisfaction. They provide comprehensive information about product features, specifications, and usage, helping customers make informed decisions and reducing returns and complaints.

AI retail use case: H&M

AI in retail

H&M, a well-known fashion retailer, has implemented an AI system called “Cherry” to write product descriptions for its online store. This system analyzes images of clothing items and uses natural language processing to generate descriptions. These descriptions are then reviewed and edited by human writers. This approach has helped H&M streamline its content creation process and provide consistent and accurate product descriptions for its customers.

Dynamic pricing and promotions 

When it comes to retail, price is a key determinant of purchase decisions. Surveys reveal that 90% of shoppers plan to switch brands, look for lower prices, and reduce spending because of higher prices. More than half are already doing so. 

Dynamic pricing and promotions are crucial for attracting customers, maximizing profits, and staying competitive. AI pricing engines continuously optimize prices using data, algorithms, and feedback loops. As a result, retailers are able to adjust prices based on their promotional activities, pricing history, product range, and other data. Not just by chain, region, or store but by individual.

AI retail use case: AMAZON

AI in retail

Amazon employs a dynamic pricing tool called Amazon Price Optimizer to adjust its product prices several times a day. The process takes into account factors like demand, competitor pricing, sales volume, and product availability. By doing so, Amazon remains competitive while maximizing profits. According to reports, this solution has led to a 5% increase in sales and a 2% improvement in profits for Amazon.

Personalized customer experience with AI-powered loyalty programs 

Consumers crave connections with brands, and 80% of them are more likely to stick with a brand that offers a loyalty program. Thus, offering relevant products, recommendations, and rewards tailored to their individual needs builds trust and loyalty. And those retailers who deliver that win their customers’ hearts (and wallets). 

This translates to higher sales, increased customer satisfaction, repeat purchases, and higher lifetime value. Imagine a world where your favorite store remembers your preferences, suggests items you’ll love, and rewards you for your loyalty. That’s the power of personalization and loyalty programs in action, a key ingredient for retail success.

AI retail use case: STARBUCKS 

AI in retail

Starbucks might be a good example of how a customer loyalty program can make a tremendous difference for the business. Starbucks uses AI to study customers’ past purchases, preferences, and even the time of day they visit. This information helps the Starbucks Rewards program offer personalized rewards like discounts on favorite drinks or exclusive deals.

This personal touch makes customers more loyal and encourages them to spend more and visit more often. It turns out that Starbucks rewards members are five times more likely to visit a Starbucks every day. As for the company, the loyalty program brought a 15% year-on-year increase in active membership in the US in 2023, reaching nearly 31 million active members. These loyalty members, on the other hand, account for 41% of Starbucks’ sales in the USA.

Streamlined customer service with AI chatbots

AI-powered chatbots and virtual assistants are changing the way customer service works in retail. These smart conversational agents can answer customer questions, give information about products, and deliver assistance round the clock. In fact, 64% of consumers prefer interacting with a chatbot rather than waiting for a human agent.

By using chatbots, stores can reply to customer messages quickly, improving customer satisfaction. Taking into consideration that 75% of customers engage through multiple channels throughout their journey, AI ensures consistent, high-quality service across all platforms. 

AI retail use case: EBAY

AI in retail

One excellent example of chatbots in online shopping is eBay ShopBot. It’s a virtual shopping assistant available on Messenger. By quickly answering all shopper questions and providing instant replies, this chatbot helps save time for everyone. No more tedious scrolling through eBay or ticking boxes; ShopBot offers friendly conversations and direct links to products you’re interested in.

Increased security with fraud detection 

As customers slowly shift from in-store to online purchasing, fraudulent activities in transactions, orders, and deliveries are on the rise. About 34% of American consumers report being potential fraud victims, and e-commerce businesses lose an average of $48 billion to fraud annually. 

To tackle this issue, retailers use AI-based tools for fraud detection and prevention. These tools analyze data to identify suspicious transactions by spotting unusual behaviors and inconsistencies in product descriptions. By quickly flagging and blocking such transactions, AI helps prevent fraud and ensures a safer shopping experience for everyone involved.

AI retail use case: PAYPAL 

AI in retail

PayPal relies on an AI-powered system called Deep Learning Fraud Detection to detect and prevent fraud in financial transactions. These tools analyze user behavior, transaction patterns, and various parameters related to user identification and credit card information, including address verification.

They also examine patterns to identify users with multiple accounts or those trying to exploit proxy servers for various purchases. Through machine learning, these tools continuously adapt and improve their fraud detection capabilities. PayPal reports that the AI-powered system has helped them reduce losses from fraud by 25%.

Enhanced inventory management and demand forecasting

Keeping the right balance of stock to meet customer demand, both in-store and online, is crucial. What retailer would like to overstock or run out of products? Probably none. They should have enough items to quickly fulfill orders, but not so many that their storage space gets crowded with products that aren’t selling. However, retailers can’t have everything under control at all times. That’s when AI technology comes in.

By analyzing historical data, customer preferences, and competitor information, AI helps retailers make smarter decisions when ordering inventory. Whereas, AI-powered tools such as cameras and sensors enable real-time monitoring of inventory levels. This optimizes supply chain and delivery processes and helps retailers avoid the dreaded “Out of Stock” status, which may drive customers away.

AI retail use case: LOWE’S 

AI in retail

Lowe’s, the American home improvement retailer, leverages AI to revolutionize inventory management and enhance the shopping experience for its customers. It uses small cameras strategically positioned on shelves in key areas of the store, like the light bulb section. These cameras keep an eye on stock levels in real-time. When they notice a gap on the shelf, they send a quick alert to the store’s devices. This helps staff know when to restock from the stockroom promptly. By using AI in this way, Lowes ensures that customers always find what they need and makes shopping more efficient and enjoyable.

Are there any challenges involved?

The future of retail powered by AI solutions is brimming with potential and promise. Still, as many exciting possibilities AI brings, it doesn’t go without its own set of challenges. What are they?

  • Lack of AI skills: The high demand for AI skills exceeds the number of available experts, which causes a shortage of talent in the industry.
  • Poor quality or insufficient data: For AI-powered systems to work efficiently, high-quality and accessible data is needed. Otherwise, the insights might be flawed and incomplete. 
  • Security risks and ethical concerns: From compliance and data breach risks to the lack of transparency and issues related to privacy and consent—There are quite a lot of them. And if these aren’t addressed properly, they might result in legal penalties, damaged reputation, and both customer and stakeholder distrust.
  • Integration with existing systems: Many retailers might find it challenging to integrate AI systems with the existing infrastructure in their companies.

Nevertheless, despite all the challenges retailers may come across while implementing AI in their operations, the rewards are considerable. So, if you’re thinking about elevating your business but are unsure what to start with, reach out to us. At Neontri, we’ll help you translate your ideas into a successful digital product that not only meets your expectations but also exceeds them.

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