Consumers in 2025 expect a seamless blend of online convenience and in-store experiences. They want the freedom to browse products digitally while enjoying the tactile, immersive experience of shopping in person. This is where the concept of phygital retail retail comes in. This concept blends digital technologies like GenAI, augmented reality, and more with physical stores. This creates a personalized, efficient, and engaging shopping experience.

So, in this blog, we’ll explore how generative AI drives the success of phygital retail through various use cases.

Let’s get started!

Generative AI in Phygital Retail

Here are six ways in which generative AI acts as a catalyst in the phygital retail space:

Fig: 6 Ways GenAI Catalyzes Phygital Retail Innovation

Personalized In-Store Recommendations

AI and real-time data come together to offer personalized product suggestions right when shoppers need them. Whether it’s through interactive screens or mobile alerts, these make shopping easier and help build loyalty by understanding customer preferences. This mix of digital and physical elements creates a more engaging and enjoyable shopping experience.

Here’s how AI transforms the fashion retail experience:

When a customer enters a store with AI-driven personalization, their loyalty app recognizes them and analyzes their shopping history, providing tailored recommendations through smart mirrors in fitting rooms. Moreover, the store associates enhance the experience with styling tips and exclusive discounts, leaving customers delighted with their personalized selections. So, AI powers these innovations, transforming retail into a highly personalized journey that boosts sales and fosters customer loyalty.

Dynamic Product Displays and Digital Signage

Driving sales and customer loyalty today requires more than static displays. Modern shoppers demand engaging, personalized experiences that cater to their unique preferences. AI enables retailers to deliver real-time, tailored interactions, creating adaptive in-store journeys that captivate and connect with individual customers, fostering stronger engagement and satisfaction.

This is how GenAI powers dynamic product displays and digital signage:

a. Personalized Content Creation in Real-Time: AI helps in tailoring content in real-time by analyzing user behavior and preferences. This helps boost engagement, generate satisfaction, and create more conversions.

b. Dynamic and Context-Aware Content Updates: AI dynamically updates content in real time, adapting to user behavior, preferences, and location. This delivers hyper-relevant recommendations and visuals, boosting engagement, user experience, and conversions.

c. Seamless Integration with Customer Data for Predictive Engagement: Integrating customer data with AI enables predictive engagement by analyzing behavior, preferences, and past interactions. Businesses can forecast actions like purchase likelihood, enabling timely, personalized outreach to boost engagement, loyalty, and conversions.

d. Interactive and Immersive Experiences: AI personalizes content in real time, enhancing interactions through tools like AR, chatbots, and dynamic recommendations. This creates engaging, adaptive experiences. It also makes customer journeys more immersive and interactive across platforms.

e. Automated Design and Content Generation: Generative AI automates content creation, producing product visuals, videos, and ads aligned with brand goals. This enables quick updates across stores and platforms. It ensures displays stay fresh, relevant, and consistent.

f. Location-Based and Multi-Channel Integration: Generative AI customizes digital signage based on location in the store. It connects with online stores, allowing customers to scan QR codes for more details. This creates a smooth shopping experience, both in-store and online.

Quick read: Learn about AI’s impact on India’s retail and q-commerce sectors.

Creating Immersive Virtual Try-Ons and Showrooms

Generative AI improves virtual try-ons for apparel, cosmetics, and eyewear, allowing customers to see realistic depictions of texture, fit, and color through AR mirrors or mobile apps.

Here are three real-world use cases for the same:

a. Virtual try on with Nike Fit

• Overview: Nike Fit uses AR and AI within the Nike app for virtual try on for the customers to find the perfect shoe size.
• How it works: Users scan their feet using their smartphone’s camera, and the app uses AR to capture foot dimensions accurately. AI then recommends the best shoe size based on this data.
• Benefit: Reduces returns due to sizing issues, enhance customer satisfaction and minimizes waste.

b. Sephora Virtual Artist

• Overview: Sephora’s app leverages both AR and AI to allow users to try on makeup products virtually.
• How it works: Customers can scan their face to virtually apply different makeup products, like lipstick, eyeshadow, and foundation. AI analyzes the customer’s skin tone to make personalized product recommendations.
• Benefit: Allows customers to test products without needing physical samples, improving convenience and enhancing the experience for online shoppers.

c. IKEA Place

• Overview: IKEA’s app, IKEA Place, allows users to visualize furniture in their homes using AR.
• How it works: Customers can use their smartphone camera to place virtual furniture in their space to see if it fits and complements the room’s decor. AI algorithms help recommend products based on room dimensions and style.
• Benefit: Reduces the risk of purchasing items that may not fit or suit a customer’s space. Thus, increasing confidence in purchasing decisions.

Quick note: Retailers are using AI to create digital showrooms accessible via VR or mobile. For example, a furniture store lets customers virtually walk through a showroom and visualize items in their own space, offering a seamless phygital experience.

Explore how we helped a retail giant slash order fulfillment time by 50% with our AI-powered solutions.

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AI-Driven Analytics for Phygital Optimization

AI-driven analytics enables retailers to track, analyze, and interpret customer data in real-time. This technology allows retailers to understand their customers’ journey across touchpoints, forecast demand, optimize inventory, and personalize recommendations.

The image below highlights recent studies on the impact of AI-driven phygital optimization in retail:

Fig: Key Stats on Phygital Optimization in Retail

Here are a few ways AI analytics supports phygital optimization:

a. Customer Behavior Analysis: AI can assess purchase history, browsing behavior, and even in-store foot traffic to help retailers understand how customers move through and interact with both digital and physical spaces.

b.Inventory and Demand Forecasting: AI-driven analytics can predict which products will be in demand based on factors like seasonality, local preferences, and historical sales data. It can ensure stores are stocked with the right products at the right time.

c. Personalization and Targeted Marketing: It enables hyper-personalized marketing by analyzing individual preferences and behaviors, ensuring that customers receive relevant product recommendations, promotions, and content.

d. Operational Efficiency: AI analytics can optimize staffing, manage supply chains, and ensure inventory alignment between online and in-store operations.

Read about some of the use cases of AI-driven phygital optimization:

Use Case 1: In-Store Recommendations Based on Online Behavior

• Scenario: A customer browses for workout gear on a retailer’s website but doesn’t make a purchase. Later, when they enter a nearby store, AI-driven systems recognize the customer through their mobile app login or loyalty program membership.
• Phygital Solution: The store’s digital displays or associates equipped with tablets can provide personalized recommendations based on the items the customer viewed online. This targeted approach often increases the likelihood of a purchase by showing items directly relevant to the customer’s preferences.

Use Case 2: Real-Time Inventory Management for Omnichannel Fulfillment

• Scenario: A retailer offers buy-online, pick-up-in-store (BOPIS) services. Without accurate inventory tracking, items may not be available for in-store pickup.
• Phygital Solution: AI analytics provides real-time inventory visibility across all locations, helping to route orders to the nearest store with stock. Apart from that, predictive analytics can even ensure high-demand items are prioritized and replenished faster. Thus, minimizing disappointment from stockouts and ensuring seamless phygital service.

Use Case 3: Dynamic In-Store Pricing and Promotions

• Scenario: A grocery store wants to clear out perishable items before they expire.
• Phygital Solution: Using AI to monitor stock levels and expiration dates, the store dynamically adjusts prices or promotions on digital displays for specific items nearing their sell-by date. Customers can see these targeted deals in-store, increasing purchase likelihood and reducing waste.

Enhanced Customer Support with AI-Driven In-Store Assistants

Here’s how AI-driven store assistants elevate customer support:

a. 24/7 Availability: AI-driven in-store assistance is available around the clock, which can be especially helpful during off-hours or in self-service areas where staff might not be readily available.

Example: A customer can use a voice-activated AI assistant in the store to ask for product details or pricing information if the store employees are occupied with other customers.

b. Supporting Humans to Solve Complex Problems: AI assistants quickly answer common customer queries, improving response times and reducing the need for human help. This allows employees to focus on more complex tasks that require a personal touch.

Example: If a customer asks an AI-powered in-store kiosk whether a specific product is available in stock, and the system will immediately check the inventory and provide the answer, saving the customer time.

c. Reduced Wait Times: AI systems reduce wait times in retail by offering real-time product assistance and enabling faster checkouts via mobile payments or self-service kiosks.

Example: AI-powered smart shelves or mobile apps can guide a shopper directly to the aisle where the item is located, minimizing the time spent searching for a product.

d. Seamless Integration with Online and Offline Shopping: AI assistants can offer customers personalized promotions, check product availability, or even allow them to place orders online if the item is out of stock in-store. This integration enhances the overall shopping experience by creating a unified and consistent journey.

Example: A customer may find a product they like in-store, but if it’s out of stock, the AI assistant will offer to order it online and ship it home, creating a seamless experience beyond the store.

e. Increased Sales and Upselling Opportunities: AI-driven assistants can also be used to suggest related or complementary products, helping retailers increase sales and boost average transaction values. These personalized suggestions can be based on a customer’s shopping history. They make them more likely to resonate with the customer.

Example: In a clothing store, an AI assistant may suggest a matching scarf or shoes to a customer who may have bought a jacket. This boosts sales.

Here are two use cases for the same:

a. Generative AI Chatbots for In-Store Assistance: Generative AI chatbots in mobile apps or kiosks provide real-time support, helping customers find products, answer queries, and offer personalized deals. They suggest alternatives for out-of-stock items and recommend products based on past preferences, enhancing the in-store experience.

b. Augmented Staff Training and Support: Generative AI empowers store associates with real-time insights into customer preferences, inventory, and promotions. It guides associates on how to engage customers based on their interaction history.

Generating Content for Social Media and Loyalty Programs

Generative AI allows retailers to create personalized content for social media and loyalty programs, enhancing customer engagement both online and in-store.

Here are the specific use cases for the same:

a. AI-Created Marketing Content: Generative AI allows retailers to quickly produce personalized content for social media, email, and app notifications, creating a feedback loop between online and in-store engagement.

That is, if a customer browses specific items in-store, GenAI can send personalized follow-up content, such as targeted emails featuring similar products or reminders about upcoming sales. This connects the in-store experience with digital touchpoints to encourage continuous engagement.

b. Dynamic Loyalty Rewards: Generative AI can also personalize loyalty programs by generating tailored rewards based on real-time insights.

For example, a first-time visitor might receive a discount on items they viewed in-store, while frequent customers could receive rewards that reflect their shopping habits. This reinforces the phygital connection by motivating customers to engage with the brand across both digital and physical channels.

So, by focusing on generative AI and similar cutting-edge technologies, retailers can not only enhance customer satisfaction but also position themselves at the top of the retail landscape.

Take part in this phygital and digital transformation in retail starting today with generative AI. Reach us at Nitor Infotech to learn more about it. Also, get familiar with our cutting-edge software development services to transform your business in 2025.