How Fair-Trade Data + AI predicts shopper behavior

Ah yes! Back to the center of my heart.... predictive analytics. Here is a prompt to read the attached... 78% of businesses say first-party data is their most valuable asset — yet 81% still rely on third-party cookies to understand customers (HBR, 2022). Without connection, personalization stops at the surface. Prediction never starts.

Data × Applications × Models: the impact equation. When the correct data meets the right applications and the right models, results are tangible: +10–15% online conversion (LLM personalization), +3–5% GMV (dynamic pricing & propensity), –30% OOS days (demand forecasting), +5–10 NPS points (multimodal sentiment). This is the Data × Applications × Models Matrix we use to prioritize rollouts for FMCG and retail clients. Where to start: - 0P/1P data hygiene + proprietary knowledge graph - RAG for conversational search & self-service - Edge vision for in-store flows and heat maps - KPIs by design: cross-channel ROAS, FCR, CLOV Ready to move from “pilots” to operational impact? Let’s connect https://lnkd.in/d5VxQz62 #DataStrategy #AI #Retail #FMCG #ProductAnalytics

The retail game has changed, fast. AI, disruptive technologies, shifting shopper expectations, and ongoing economic volatility are reshaping every corner of the industry. To win in this new era, instinct isn’t enough. You need insight, the kind that drives confident, data-led decisions. At Acosta Europe, we’ve partnered with leading intelligence providers to help brands stay ahead of disruption and turn change into opportunity. Download our new whitepaper: Key Insights for Retail Growth in an Era of Constant Change Inside, you’ll uncover: ✅ How AI, data and macroeconomic forces are redefining the retail experience ✅ Growth strategies from NielsenIQ, Kantar, and Bain & Company ✅ What Retail 2.0 means for innovation, execution, and shopper engagement If you work in strategy, sales, insights or execution, this is essential reading for what’s next. Download here: https://lnkd.in/eRp-ZKgk Retail 2.0: The Time For Bold Action is Now #Retail20 #RetailStrategy #FutureOfRetail #AcostaEurope #Whitepaper #InsightDriven

Every year, brands spend over $20 billion just to be seen in retail. Most of that spending is guesswork. ShelfSlide’s AI fixes that. Our Demand Forecasting and Local Trends Radar analyzes search, social, and foot-traffic data to predict which neighborhoods and shelves actually sell. The result: smarter placements, improved visibility, and reduced waste. That data becomes our moat — it gets smarter with every booking. If you invest in defensible data and AI-driven platforms, ShelfSlide is building something rare in the retail industry.

AI-powered retail analytics has rapidly evolved from static dashboards to intelligent, real-time, and even generative systems that not only predict but also prescribe the next best action. This shift is enabling retailers to optimize pricing, assortment, and supply chains with unprecedented speed and precision. As the global AI in retail market is projected to grow from $9B in 2024 to $85B by 2032 (Fortune Business Insights), the question is no longer if retailers should adopt AI but how fast they can.

AI product attribute enrichment is changing how retailers present and sell every SKU online, from search relevance to conversion lift to faster onboarding cycles. In our post, Senior Data Engineer Girish Patil explains how multimodal models add precise, trend aware attributes that make catalogs clearer and shopping journeys smoother for customers and teams. The article covers real retail use cases, measurable outcomes, and BBI’s capability of delivering enrichment at scale across millions of products with consistent taxonomy and continuous learning. 
 Read the article and see how seven practical methods can improve search relevance, campaign speed, and prioritize innovation. AI Product Attribute Enrichment: 7 Ways to Transform Retail Catalogs https://hubs.ly/Q03KsLPy0 #ProductData #Ecommerce #RetailTech #PIM #CatalogManagement #DataQuality #GenAI

According to Adobe Analytics, the 2025 U.S. holiday e-commerce season is forecast to hit $253.4 billion, up 5.3% YoY. But the real headline? AI-assisted shopping traffic is expected to grow 520% year over year, peaking in the ten days before Thanksgiving. Here’s how consumers plan to lean on AI tools: 👉 53% may use AI for product research 👉 40% for recommendations, 36% for deal finding, 30% for gift inspiration 👉 Top impact categories: toys, electronics, jewelry, personal care As AI tools increasingly shape the shopping journey - from discovery to transaction - brands and retailers must be ready to embed intelligent assistance at every touchpoint. #ecommerce #artificialintelligence #ai #digitalretail #shoppingtrends #holidayseason #innovation