Use Cases & Examples Of Artificial Intelligence (AI) In Retail

The classic brick-and-mortar experience was getting a bit stale. The same old -same old shelves, boring old hunt for that one size that fits you and seems that you never find it, and the monotonous generic sales pitches. But then Artificial Intelligence (AI) walked right through that door, ready to give the whole shopping scene a much-needed makeover.  

Suddenly, it isn’t just about browsing aisles – it’s about hyper-personalized experiences, super-smart inventory management, & customer service that feels like someone knows you forever. From online giants like Amazon to your local waffle shop, AI in retail industry is changing the game, both in-store and online (aka AI in retail and e-commerce).

SoftmaxAI has created this blog on AI in Retail Industry where we’ll provide you with use cases and real-life examples of AI in retail and e-commerce, so without any further ado let us begin. 

AI in Retail Industry: Use Cases and Examples

Demand Forecasting 

Traditional demand forecasting methods often rely on historical sales data & manual analysis, which can be time-consuming and are prone to errors. In contrast, AI-driven demand forecasting solutions of AI application in retail industry can process and analyze vast amounts of data from multiple sources in real-time, uncovering complex patterns and insights that would be difficult for humans to identify.

The benefits of using AI for demand forecasting in retail include:

• Improved accuracy: AI algorithms can analyze a wide range of data points to provide highly accurate demand predictions, reducing the risk of stockouts or overstocking.
• Real-time insights: AI enables retailers to process data and adapt to changing market conditions in real-time.
• Complex pattern recognition: AI excels at identifying intricate patterns that manual analysis might overlook, allowing retailers to anticipate shifts in demand driven by factors like changing consumer preferences or external events.
• Enhanced inventory management: Accurate demand forecasts lead to optimized inventory levels, minimizing carrying costs and ensuring product availability when customers need it with the help of predictive analysis.

Examples of AI-driven demand Forecasting in Retail

Walmart: The retail giant leverages AI to improve its inventory management and demand forecasting. By analyzing sales data, market trends, and customer behavior, Walmart’s AI application in retail industry can predict which products will be in high demand, optimizing stock levels and reducing waste.

Personalized Product Recommendations

AI-powered recommendation systems utilize machine learning algorithms to analyze vast amounts of data, including:

• Customer browsing history
• Past purchases
• Wishlist items
• Product attributes (e.g., color, size, style)
• Customer demographics
• Contextual data (e.g., time of day, season, location)

These systems of AI application in retail continuously learn from customer interactions and behaviors to provide increasingly accurate and relevant product suggestions. The AI algorithms can identify patterns and preferences that may not be immediately apparent to human analysts, allowing for highly tailored recommendations.

Key Benefits of AI-Powered Product Recommendations

• Increased Conversion Rates: Personalized recommendations can significantly boost conversion rates by presenting customers with products they are more likely to purchase.
• Higher Average Order Value (AOV): By suggesting complementary or related items, AI in retail and e-commerce can encourage customers to add more products to their carts.
• Improved Customer Experience: AI application in retail through tailored recommendations make shopping more convenient & enjoyable for customers, leading to increased satisfaction and loyalty.
• Enhanced Customer Engagement: Relevant product suggestions keep shoppers engaged on the site for longer periods, increasing the likelihood of purchases.

Example of Personalized Product Recommendations by AI Application in Retail

Amazon’s Recommendation Engine: Amazon is perhaps the most well-known example of AI-powered product recommendations in retail. Their system analyzes customer behavior and purchase history to suggest products under headings like “Frequently bought together” and “Customers who viewed this item also viewed”. This approach has been highly successful, with Amazon reporting that 35% of its sales come from recommendations.

Conversational AI in Retail

Conversational AI, which includes technologies like AI chatbots & voice assistants, leverages intelligent algorithms along with Natural language processing (NLP) to interact with customers in a human-like manner. 

Conversational AI in retail industry is changing customer interactions, providing real-time support, and personalized shopping experiences, while also bridging the gap between online and brick-and-mortar shopping environments. As the technology progresses, retailers are increasingly adopting conversational AI to enhance customer engagement, streamline operations, & improve overall customer satisfaction.

Also read: A Beginner’s Guide to Natural Language Processing

Key Use Cases for Conversational AI in Retail

• Virtual Shopping Assistants
• Customer Service Chatbots
• Personalized Product Recommendations
• Voice-Enabled Shopping
• In-Store Navigation and Assistance
• Automated Inventory Management

Benefits of Conversational AI in Retail

Enhanced Customer Experience: 24/7 availability and personalized interactions improve customer satisfaction.

Increased Efficiency: Conversational AI in retail eases the automation of routine tasks and allows human staff to focus on higher-value activities.

Data-Driven Insights: AI analysis of interactions provides valuable insights for business through conversational AI in retail.

Cost Reduction: Automated customer service can significantly reduce operational costs.

Improved Sales: Personalized recommendations and seamless shopping experiences can boost conversion rates.

Example of Conversational AI in Retail

Walmart partnered with Google to enable voice shopping through Google Assistant, allowing customers to add items to their Walmart cart using voice commands. Another example of Conversational AI in retail is Lowe’s “LoweBot” an autonomous retail service robot that helps customers find products and provides real-time inventory information in stores.

Visual Search

Visual Search in Retail

Visual Search in Retail has become a significant application of AI in the retail industry. Visual search allows customers to use images rather than text to search for products. This AI-powered technology analyzes images uploaded by users to identify key visual attributes and find similar items in a retailer’s catalog.

How Visual Search Works

• Image upload: The customer uploads an image or takes a photo of a product they’re interested in.
• Image analysis: AI algorithms analyze the image to identify attributes like color, shape, pattern, etc.
• Catalog matching: The system compares the analyzed attributes to products in the retailer’s database.
• Results display: Visually similar products are presented to the customer.

Benefits of Visual Search

• More intuitive search experience for customers
• Helps customers find products they can’t easily describe in words
• Increases product discovery and cross-selling opportunities
• Reduces friction in the path to purchase
• Provides valuable data on customer preferences and trends

Example of Visual Search in Retail 

ASOS: The online fashion retailer ASOS has implemented visual search functionality in its mobile app. Customers can upload a photo of an item they like, and the app will show similar products available on ASOS. AI application in retail has made it extremely easy for ASOS’ consumers to find items that match their fashion preferences, leading retailers to increase customer satisfaction & sales.

Dynamic Pricing and Promotions

Dynamic pricing, also known as surge pricing or demand-based pricing, is a strategy where prices for products or services fluctuate in real time based on various factors. These factors include:

• Demand: Higher demand typically leads to higher prices, while lower demand may trigger discounts.
• Competition: Prices can be adjusted to match or undercut competitors’ offerings.
• Inventory Levels: Excess inventory may lead to price reductions to encourage sales.
• Time of Day/Week: Prices can change based on peak shopping hours or days.
• Customer Behavior: Past purchase history or browsing behavior can influence personalized pricing.

Traditionally, setting and adjusting prices was a manual, time-consuming process. AI in retail and e-commerce changed this by automating the analysis of vast amounts of data and making rapid, intelligent pricing decisions.

Benefits of Dynamic Pricing and Promotions

• Maximized Profitability: AI can identify the optimal price point at any given moment, ensuring retailers don’t leave money on the table or miss out on sales due to overpriced items.
• Improved Inventory Management: Dynamic pricing can help manage inventory levels by adjusting prices to stimulate demand for slow-moving items or capitalize on high-demand products.
• Enhanced Customer Experience: Personalized pricing based on customer behavior can make shoppers feel valued and more likely to make a purchase.
• Competitive Advantage: Retailers using AI for dynamic pricing can stay ahead of the curve by quickly reacting to market changes and competitor actions.

Example of Dynamic Pricing Strategy in Retail

Best Buy’s Dynamic Pricing Strategy: Best Buy employs AI-driven dynamic pricing to compete effectively in the electronics retail space. Best Buy’s algorithms analyze competitor prices, product demand, & inventory levels to adjust prices in real time. This AI application in retail has let Best Buy still be competitive against online retailers while maintaining profitability in its brick-and-mortar stores.

Generative AI

GenAI are AI models having the capability to generate new text-based content, designs or even suggest products based on the database they have been trained. GenAI in retail sector is changing customer exp & streamlining operations in remarkable ways.

Benefits of Generative AI in Retail

• Enhanced Creativity:  Generative AI in retail industry is able to generate novel product designs and marketing campaigns, fostering innovation.
• Improved Efficiency: It Automates content creation, customer service, and product design processes, saving time and resources.
• Personalized Experiences: Generative AI in retail industry delivers tailored product recommendations, marketing messages, and virtual try-on experiences.
• Increased Customer Engagement: Generative AI in retail industry also provides interactive and engaging experiences, driving customer satisfaction and loyalty.
• Boosted Sales: Generative AI in retail industry improves product discovery, personalization, and customer experience, leading to higher conversion rates.

Example of Generative AI in Retail

Shopify Magic: Shopify deploys generative AI in its Shopify Magic feature, which automates the content creation process for its users. This GenAI tool can generate product descriptions, email subject lines, & headers for their online stores, helping merchants save time & maintain consistency in their communications.

Also read: Scope of Generative AI Development – How GenAI’s Impact on Businesses?

Final Thoughts 

As we wrap this up, you’d be wondering, if demand forecasting, personalized product recommendation or conversational AI in retail actually useful for my business? We’ve already established that premise, AI in retail and e-commerce are extremely important for both retailers and consumers alike. The question that you need answer to is what technology is the best for you and how you will be able to get the maximum ROI. Don’t stay confused, get all the answers by contacting us and learn the ways we can change your business for good!