AI Shopping Trends: What to Expect in 2025
Retailers are no longer just selling products—they’re making real-time decisions powered by data. And at the heart of this transformation is AI data analytics in retail.
In 2025, with hyper-personalization, dynamic pricing, and multi-channel fulfillment becoming the norm, retailers can no longer rely on static dashboards or backward-looking reports. They need predictive insights, instant adaptability, and intelligent automation—all of which AI delivers at scale.
At Glance, AI analytics powers not only product discovery and avatar-led personalization but also backend intelligence—feeding shopper intent signals into inventory decisions, pricing, and merchandising strategies. From real-time data captured on lock screens to personalized product recommendations based on visual interactions, Glance AI is redefining how data turns into action.
This guide explores how AI-powered data analytics is transforming retail—across forecasting, inventory, customer insights, and even store layout optimization. We’ll also examine success stories from India’s top retailers and D2C brands leading the shift.
Related reads:
How AI Inventory Management Optimizes Stock
AI in Retail Supply Chain
Glance AI’s Role in Customer Experience
AI data analytics gives retailers the power to shift from reactive performance monitoring to real-time, predictive, customer-centric decision-making.
One of the most powerful use cases of AI in retail analytics is inventory forecasting. Traditional systems rely heavily on past sales data, leading to either overstock or stockouts—both of which hurt margins and customer experience.
Predictive analytics changes this by making inventory management proactive instead of reactive.
AI-powered forecasting models ingest and analyze:
These systems predict demand with much greater precision and automatically suggest replenishment, transfers, or markdown triggers based on dynamic data—not static assumptions.
Glance AI’s analytics identify visual demand signals before a product is even added to a cart. For example:
This creates a closed feedback loop between demand discovery and backend inventory optimization.
Explore further: How AI Inventory Systems Reduce Waste and Increase Sales
KPI | Pre-AI Forecasting | With Predictive Analytics |
Stockout Rate | 18–25% | <8% |
Overstocking Costs | High | Reduced by 30–40% |
Lost Sales Due to OOS | Significant | Recovered via auto-fill |
Inventory Holding Duration | >45 days | Down to 25–30 days |
Predictive inventory analytics help retailers make faster, data-backed decisions—preventing lost sales, markdowns, and waste.
Knowing your customer isn’t just about demographics anymore—it’s about understanding intent, mood, timing, and preferences across every touchpoint. This is where AI-driven customer analytics plays a transformational role.
Unlike traditional CRM tools that rely on static profiles, AI models learn in real time, adapting to behavior patterns and feeding back into the personalization engine.
AI data analytics in retail captures and processes:
This helps brands create dynamic customer segments, each with tailored journeys, offers, and product mixes.
Glance AI uses behavioral AI to:
Glance doesn’t just recognize what users clicked—it interprets why—enabling intent-mapped personalization.
Outcome | Result |
Average Order Value (AOV) | Increased by 15–20% through smart cross-sell |
Personalization Opt-In Rate | Doubled when based on behavior vs static input |
Repeat Visit Probability | Up to 3x higher with interest-aligned content |
CX Ratings | Significantly higher when journeys feel intuitive |
Related: Glance AI User Engagement Engine
AI gives retailers the power to listen, adapt, and engage in real time—turning clicks into context and behavior into brand loyalty.
In a market where shoppers compare prices in seconds and competitors launch flash sales overnight, static pricing strategies no longer work. Retailers need AI-powered dynamic pricing models that respond in real-time to both supply-side shifts and consumer behavior.
AI doesn’t just automate price changes—it optimizes margin, timing, and customer sensitivity across product categories and geographies.
AI models analyze:
Metric | Pre-AI Era | With AI-Pricing Engine |
Offer Conversion Rate | ~2–4% | 8–12% |
Gross Margin Retention | Volatile | Smoothed with discount control |
Promo Waste (Unused Offers) | High | Reduced via targeted delivery |
Customer Drop-off During Offers | Common | Reduced by behavior-triggered timing |
AI transforms pricing from static math into real-time customer science—boosting revenue while maintaining user trust.
In today’s omnichannel retail landscape, how products are arranged, visualized, and discovered—whether online or offline—matters more than ever.
AI now plays a strategic role in designing both digital storefronts and physical spaces, ensuring that layout, visual cues, and placement are data-driven to maximize engagement and sales.
AI tools analyze:
This data enables dynamic visual merchandising—from homepage curation to aisle sequencing—built around how people naturally browse, pause, and purchase.
Glance’s AI identifies:
The system adjusts visual display logic and avatar styling sequences to optimize for discovery + action—without any manual merchandising setup.
Explore more: How Glance AI Powers Visual Personalization
Metric | Traditional Visual Merchandising | With AI Visual Analytics |
Session Duration (App/Web) | 1.5–2 mins | 3–4 mins |
Add-to-Cart Rate (Visual Products) | 6–8% | 12–15% |
Aisle-Level Footfall Conversion | Low correlation | Improved by 25–30% |
Merchandising ROI per Campaign | Difficult to attribute | Real-time uplift tracking |
AI doesn’t just suggest what to sell—it determines how to visually present it for maximum engagement, both online and in-store.
AI isn’t just an add-on to retail analytics—it’s the new engine.
From predicting inventory needs to redesigning digital store layouts and delivering emotion-aware personalization, AI enables real-time, contextual, and profitable retail decisions. For brands like Glance, AI data isn’t just analyzed—it’s acted upon instantly, helping every moment on a shopper’s screen feel intuitive and intelligent.
Want to go deeper?
Glance AI for E-Commerce Personalization
Inventory Management with AI
AI in Retail Supply Chain
AI data analytics in retail uses machine learning to process large volumes of customer, product, and operations data to generate predictive insights for inventory, pricing, personalization, and store performance.
AI improves inventory accuracy by forecasting demand, optimizing replenishment schedules, and reducing overstock or stockouts based on real-time data and customer intent signals.
Yes. AI analyzes user behavior to deliver personalized recommendations, tailored offers, and adaptive user interfaces, enhancing engagement and loyalty.
Brands like Flipkart, Reliance Retail, Nykaa, Tata CliQ, Meesho, and Glance AI are leveraging AI to optimize supply chains, personalize shopping, and automate visual merchandising.
Absolutely. AI supports e-commerce through dynamic content and recommendations, while offline stores benefit from footfall analytics, shelf optimization, and intelligent layout design.