Contextual Shopping Suggestions

How Walmart Uses AI to Recommend Kitchen Tools When You Buy Milk Ever wonder why Amazon suggests a milk frother when you add milk to your cart? The challenge is harder than it looks—how do you bridge the gap between routine grocery purchases and discovering useful general merchandise? 👉 The Challenge Traditional recommendation systems excel at suggesting similar items (milk → cheese) but struggle with cross-category discovery (milk → milk frother). The problem becomes even trickier when customers have 20+ items in their grocery cart—which products should drive recommendations, and how do you rank them intelligently? 👉 Walmart's Dual Approach Researchers at Walmart Global Tech developed a two-stage system that combines the best of both worlds: Stage 1: Smart Candidate Generation - Historical co-purchase analysis identifies proven item pairs - Large Language Models (LLMs) generate contextual suggestions that go beyond purchase history - Example: For eggs, the LLM suggests egg poachers, timers, and specialized pans—items that enhance the egg experience but might not appear in traditional data Stage 2: Real-Time Cart Context Ranking - A transformer-based neural network analyzes the entire cart contents - Uses cross-attention to understand how potential recommendations relate to all cart items simultaneously - Considers customer persona, platform, and sequential shopping behavior 👉 The Results The system delivered impressive improvements: - 36% increase in add-to-cart rates for LLM-generated recommendations - 27% lift in ranking quality (NDCG@4) when using full cart context - 4.7x more unique recommendation coverage compared to traditional methods 👉 Why This Matters This research tackles a fundamental e-commerce challenge: helping customers discover products they didn't know they needed. By combining AI reasoning with behavioral data, the system creates those "aha moments" where a grocery run becomes an opportunity for useful discovery. The approach shows how modern AI can enhance rather than replace traditional recommendation methods, creating more engaging shopping experiences while driving meaningful business results. Paper by Akshay Kekuda, Murali Mohana Krishna Dandu, Rimita Lahiri, Shiqin Cai, Sinduja Subramaniam, Evren Korpeoglu, and Kannan Achan from Walmart Global Tech.

It’s a rainy day in Bengaluru and almost as a reflex I ordered some unhealthy snacks using a quick commerce app. This got me thinking and I went into this rabbit hole to look at data to understand how micro-moments impact our shopping behaviour. Especially when it comes to quick and easy delivery. With such an ease of access, our shopping isn’t always planned now. It’s shaped by small, context-driven moments. And data shows how powerful these are: - Weather-led micro-moments: Rainy evenings in Bangalore = fried snacks up by 30% on Swiggy Instamart. Heatwaves in Delhi = Blinkit reports 200% more Amul ice creams sold. These aren’t monthly trends but they’re momentary demand spikes. - Holiday micro-moments: Festivals spark impulse shopping. Cadbury Celebrations and Haldiram’s gift packs saw a 3x jump on quick commerce during Diwali. Christmas in metros like Bangalore and Delhi sees plum cakes and wine orders 2–3x higher. - Time-of-day micro-moments: 7 AM: “I forgot milk!” → Amul and Britannia make up 40%+ of orders. 3 PM: “Need a snack break” → Lays, Kurkure and Coke see a 60% uplift. 12 midnight: “One more episode” → Maggi & Cornetto surge 50–70% on Blinkit. These micro-moments are where brands win or lose attention. Quick commerce isn’t just about 10-minute delivery, it’s about being there at the exact moment of need, with the right product visible, available, and top of mind. So next time you open your app in the rain or at midnight, remember you’re part of a micro-moment shaping the future of shopping. Fascinating to see how q-comm is playing the role of a catalyst in this unique shift in shopper behaviour. Wonder how the demand side plans for some of the unforeseen events. 🤔 #QuickCommerce #MicroMoments #ShopperBehavior #ConsumerInsights #RetailTrends #Ecommerce #FMCG #MarketingStrategy

Personalization isn’t the problem. Bad personalization is. Brands spend millions on personalization tech… Yet conversions barely move. Why? 🚨 They personalize too early (before trust is built). 🚨 They personalize the wrong things (irrelevant suggestions). 🚨 They rely on surface-level data (first names ≠ personalization). What actually works? ✅ Behavior-based recommendations - Show products based on browsing, not assumptions. ✅ Contextual upsells - Offer add-ons at the right moment, not as a random pop-up. ✅ Personalization that feels invisible - The best personalization doesn’t feel like personalization. Real Data: → Brands that personalize post-purchase see 20-30% higher repeat orders. → Checkout upsells perform 3x better when based on cart contents. → Contextual email/SMS recommendations drive higher LTV than generic discounts. Personalization isn’t about showing more. It’s about showing the right things at the right time. If your strategy isn’t increasing conversions, it’s time to rethink it.

Does it feels like the universe is reading your mind when you see the exact movie you were thinking about popping up in your recommendations? Well, it's actually the brilliantly crafted recommendation algos designed to predict your preferences! Recommendation engines are algorithms used to predict a user's preferences based on their past behaviors. Types of recommendation engines: 💡 Collaborative Filtering: It makes predictions based on the past behavior of similar users. 🔹 User-based Collaborative Filtering: Recommends items by finding similar users to the current one. For example, if User A likes items X, Y, and Z, and User B has a similar preference for X and Y, then we might recommend Z to User B. 🔹 Item-based Collaborative Filtering: Recommends items that are similar to items the user has already liked or interacted with. For example, if a user likes item X, the system will suggest items that have been liked by others who also liked X. 💡 Content-Based Filtering: Recommendations based on the attributes of the items and the preferences of the user. These systems analyze the features of items that a user has shown interest in and suggest items with similar features. For example: In a movie recommendation system, if a user likes action movies with a certain actor or director, the system would recommend action movies that feature the same actor or director. 🔸 Item profiling: Each item is represented with its features like genre, keywords, tags. 🔸 User profiling: A profile is built for the user based on their interaction history (e.g., liked items or rated items). 🔸 Matching: Items that share similar characteristics to what the user has shown interest in are recommended. 💡 Context-Aware Recommendations: These systems take into account the context in which the recommendation is made. Factors it might consider: 🔸 Time: Recommending different things depending on whether it’s daytime or nighttime. 🔸 Location: Showing items based on the user's current location (e.g., restaurant recommendations based on proximity). 🔸 Device: Tailoring recommendations depending on whether the user is on a mobile phone or desktop. 💡 Deep Learning-Based Recommendations: Neural networks are models that can learn complex patterns from user behavior and item attributes. For example, music streaming apps use deep learning to analyze a variety of signals, including content features and user interactions to provide personalized recommendations. Recommendation is often modeled as a matrix completion problem. Imagine a huge grid whose rows represent online streaming customers and columns represent the movies in the catalogue. If a customer has seen a particular movie, the corresponding cell in the grid contains a one; if not, it’s blank. The goal of matrix completion is to fill in the grid with the probabilities that any given customer will watch any given movie. #recommendationengines #deeplearning #algorithms #tech #TechInsights #techblog