Retail Industry Trends

In retail, many chase the next big thing—a new style, a new way to reach consumers—triggering a frantic race to adopt. But most trends fade as fast as they appear. The real game-changers are curated habits that prove they can stand the test of time. I’ve championed social commerce as the future of retail for over a decade. In hindsight, that barely scratches the surface. It’s now a deeply ingrained consumer behavior. The imperative isn’t just to adopt it, but to evolve with it—constantly and intentionally. At HSN, social commerce was core to our strategy. We pioneered the blend of shopping and entertainment. That’s the essence: finding the sweet spot where entertainment, connection, and commerce converge. Soon after, platforms like Twitch began enabling users to both game and shop in real time, blending entertainment with commerce. Fanatics has successfully leaned into this model as well, immersing fans in live experiences while showcasing gear in action, often worn by their favorite athletes and community, turning fandom into a powerful trust signal. More recently, TikTok Shop collapsed the purchase funnel into a single scroll. It's no longer discover, then buy. Now, it’s see it, want it, buy it—seamlessly, in-platform. So, as we look ahead, how do I see this "social commerce habit" evolving? Here's what I expect: 🔹 Creator Integration is Non-Negotiable. For Gen Z, in particular, TikTok Shop has become a primary discovery engine. They trust their favorite creators to genuinely try products and offer honest feedback. The more brands lean into authentic partnerships with creators, the more trust they build in this integrated shopping experience. It’s about relationship-driven commerce. 🔹 Embrace a Zero-Click World. Speed and simplicity are paramount. Consumers need to be able to see, buy, and receive as fast as humanly possible. This means minimal clicks, minimal friction, and no moments for reconsideration. It's about instant gratification and removing all barriers between desire and ownership. 🔹 Elevate Live Shopping. This is a powerful return to the personal connection and real-time interaction that defined the best of traditional retail. Shoppable videos and live sessions transform social media into a personalized shopping aisle. Imagine experts demonstrating products, showing how they fit or can be styled, all in real-time, tailored to your interests. It brings humanity back to digital retail. 🔹 Unlock the Power of Virtual Try-Ons. A longstanding hurdle in e-commerce is "try before you buy." AI-enabled virtual try-on features solves that, making online shopping more immersive and convenient. This translates directly into higher conversion rates, deeper engagement, and customers spending more valuable time interacting with your brand digitally. It’s time to stop treating social commerce like a trend. This is commerce, full stop. It’s a fundamental consumer behavior that belongs at the center of every modern retail strategy.

Because a wrong demand forecast ruins everything else... This infographic shows statistical forecast vs machine learning (ML) in demand forecasting: ✅ Approach 🧮 Statistical forecast: relies on historical data patterns, with limited capacity for external variables 🤖 Machine learning (ML): uses advanced algorithms to detect complex patterns, incorporating economic indicators, social trends ✅ Best to Use For 🧮 Statistical forecast: stable demand patterns with minimal external variables 🤖 Machine learning (ML): changing demand with diverse external influences (e.g., promotions, weather) ✅ Accuracy 🧮 Statistical forecast: works well for simple, well-defined time-series patterns (e.g., seasonality, trends) 🤖 Machine learning (ML): more accurate for complex, high-dimensional data; forecast accuracy rates are 10-20% higher ✅ Model Type Examples 🧮 Statistical forecast: exponential smoothing, moving averages 🤖 Machine learning (ML): neural networks, random forests, XGBoost ✅ Adaptability 🧮 Statistical forecast: requires manual intervention for changing trends or patterns 🤖 Machine learning (ML): highly adaptable to changing demand patterns with retraining ✅ Scalability 🧮 Statistical forecast: has limited scalability; small datasets or simple SKU portfolios 🤖 Machine learning (ML): scales easily for large datasets and complex SKU portfolios ✅ Team 🧮 Statistical forecast: Supply Chain Team can build most of these models themselves 🤖 Machine learning (ML): Supply Chain Teams with Data Scientists are required Any others to add?

If more of your store sales start on TikTok lately, you might wanna read this. 𝘛𝘩𝘦 𝘴𝘢𝘭𝘦 𝘪𝘴 𝘥𝘦𝘤𝘪𝘥𝘦𝘥 𝘣𝘦𝘧𝘰𝘳𝘦 𝘺𝘰𝘶𝘳 𝘤𝘶𝘴𝘵𝘰𝘮𝘦𝘳 𝘦𝘷𝘦𝘯 𝘦𝘯𝘵𝘦𝘳𝘴 𝘺𝘰𝘶𝘳 𝘴𝘵𝘰𝘳𝘦. The checkout happens in-store. But the sale happens everywhere else. Here's the reality: This year 60%+, and in 2027, 70% of retail sales will be digitally influenced. I can't emphasize this enough; here's what most brands miss—digital influence isn't just about online sales. It's about shaping every moment before the customer even walks into your store. L'Oréal cracked this code: 100M+ AR try-on sessions driving real conversions. 31 brands orchestrating seamless experiences across 72 countries. No.1 in beauty influencer marketing (29% market share), 20-80% higher conversion rates through enhanced digital experiences. The new customer journey isn't linear—it's layered: - They discover you on social - Research you through reviews and UGC - Try your product virtually through AR - Get retargeted with personalized content - Finally purchase in-store (feeling confident they're making the right choice) Every touchpoint matters, and every interaction influences the final decision. The brands winning today aren't just selling products—they're orchestrating experiences across owned, paid, and earned media that guide customers from curiosity to checkout. Digital discovery is increasingly pay-to-play and shoppers are paying attention. ++ Tactical Recommendations for CPG / FMCG Brands ++ 1. Beyond just having perfect, high SOV product pages, create discovery ecosystems. - Optimize for "zero-moment-of-truth" searches. - Activate shoppable content at scale. - Leverage user-generated content as social proof. Brands that do these see a 35% higher conversion rate from digital touchpoints to in-store purchases. 2. Connect digital engagement directly to retail execution. - Geo-target digital campaigns to drive foot traffic - Create "store-specific" digital content CPG brands using geo-targeted social ads see a 23% higher in-store sales lift in targeted markets. 3. Most important one; stop flying blind—measure digital influence on offline sales. - Implement unique promo codes for each digital touchpoint to track conversion paths. - Use customer surveys at point of purchase. - Partner with retailers on shared data insights Brands with proper attribution see 15-25% improvement in marketing ROI within 12 months. 𝗧𝗼 𝗮𝗰𝗰𝗲𝘀𝘀 𝗮𝗹𝗹 𝗼𝘂𝗿 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗼𝗹𝗹𝗼𝘄 ecommert® 𝗮𝗻𝗱 𝗷𝗼𝗶𝗻 𝟭𝟰,𝟲𝟬𝟬+ 𝗖𝗣𝗚, 𝗿𝗲𝘁𝗮𝗶𝗹, 𝗮𝗻𝗱 𝗠𝗮𝗿𝗧𝗲𝗰𝗵 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲𝘀 𝘄𝗵𝗼 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲𝗱 𝘁𝗼 𝗲𝗰𝗼𝗺𝗺𝗲𝗿𝘁® : 𝗖𝗣𝗚 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗚𝗿𝗼𝘄𝘁𝗵 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿. #CPG #FMCG #AI #ecommerce Procter & Gamble PepsiCo Unilever The Coca-Cola Company Nestlé Mondelēz International Kraft Heinz Ferrero Mars Colgate-Palmolive Henkel Bayer Haleon Kenvue The HEINEKEN Company Carlsberg Group Philips Samsung Electronics Panasonic North America

Brands that previously criticized Amazon are now increasingly utilizing the platform, indicating a notable trend for 2024. Here are some other developments I anticipate: When Allbirds' CEO published an article on Medium criticizing Amazon for imitating its product at a lower price, it was unexpected that Allbirds would later join Amazon as a seller. Yet, that's precisely what happened. Allbirds products are now available not only on Amazon but also at REI and Nordstrom. This move is part of a broader trend where DTC-first brands are diversifying their distribution channels, encompassing marketplaces, traditional brick-and-mortar retail, wholesale, and their own stores. A notable benefit of this expansion, as per Shopify's data, is the 'halo effect' on eCommerce: brands entering physical retail see, on average, a 37% increase in website traffic. As we transition away from cookies, brands are expected to increasingly focus on owned channels. Amazon has been a pioneer in this area with the introduction of its marketing cloud, which enhances brands' abilities to understand customer journeys and target specific audiences. Utilizing first-party data allows brands to maximize ROI on marketing expenditures and provide more personalized shopping experiences. This trend likely influenced Amazon's launch of Buy with Prime, enabling brands to combine fast delivery with ownership of customer data. Sustainability will remain a crucial factor in brand visibility and loyalty. I anticipate Amazon and other retailers will give more prominence to brands committed to sustainability. Those with climate pledge certification or using the Ships in Own Product Packaging program will likely see benefits (aside from savings on fulfillment costs). Consumers are also expected to demand greater transparency in supply chains, including sustainable last-mile delivery methods like electric vehicles. AI's impact on customer search and discovery is a major focus for Amazon. We may see conversational search alter how we find products (e.g., "show me gift ideas for boys age 4-6 with over 1000 4.5 stars between $25-35"). AI will bring more personalized recommendations, optimize pricing, enhance customer service, and improve customer segmentation. Social commerce is also expanding. With the launch of TikTok shops and the ability to buy Amazon products on Meta platforms, this trend is set to grow. I predict that brands will soon use Amazon data to target shoppers on Meta for purchases either through Amazon.com or Buy with Prime. Finally, Livestream shopping, AR, and VR – key questions for 2024 include: Will Livestream shopping gain as much traction in the US as it has in China? Will augmented and virtual reality shopping experiences become more mainstream? I’d love to hear your thoughts and predictions for the future of commerce in the comments.

Forecasting is a common application of data science, and it's crucial for businesses to manage their inventory, especially those with perishable items effectively. In a recent tech blog, the data science team from Afresh shared an innovative approach to accurately predict demand, incorporating non-traditional factors such as in-store promotions. Promotions are common in grocery stores, helping customers discover and purchase discounted items. However, these promotions can significantly alter customer behavior, making traditional forecasting methods less reliable. Traditional models struggle to incorporate these factors, often leading to higher prediction errors. To address this challenge, Afresh’s data science team developed a deep learning forecasting model that integrates various features, including promotional activities tied to specific products. The model's performance was evaluated using a normalized quantile loss metric, showing an 80% reduction in loss during promotion periods. This example highlights the superior performance of this solution and showcases the power of deep learning in solving a critical issue for the grocery industry. #machinelearning #datascience #forecasting #inventory #prediction – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gWRgTJ2Q 

Want to use machine learning for time series forecasting? The best models will identify the drivers of trends. I once worked with a KPI like the image below. My ML model identified a serious problem. First, let's establish a working definition of "trend" when it comes to time series forecasting: The tendency of the KPI to increase/decrease over time. Like the image above, my real-world KPI exhibited a strong upward trend. Additionally, as shown in the image above, the trend was linear (i.e., a straight line). Finance loved this KPI because it could be easily forecasted with high accuracy. Executives loved this KPI because it kept going up and up. I didn't like it all. The problem was that traditional forecasting techniques rely only on the historical KPI values. These forecasting techniques may implement additional calculations (e.g., moving averages) to enhance accuracy. However, these calculations are based solely on historical KPI values. So, it's no wonder that Finance was able to easily forecast the KPI. However, I wanted to know what the drivers of the KPI were. Enter machine learning forecasting models. Machine learning forecasting models can not only use historical KPI values, but can also include any other data that might impact KPI values: Month of the year Day of the week Economic data Promotions Weather Etc. In the case of my KPI, I was examining activities originating from the marketing team (e.g., promotions and digital ads). That's when my ML forecasting model uncovered a serious problem. The ML model identified that the primary external driver of KPI values was the marketing team's digital advertising spend. I dove into the data and found that digital ad spend increased over the same time period as the KPI. However, the digital ad spend was increasing at a higher rate. The digital ads were experiencing diminishing returns. We were burning budget to prop up the KPI. That's the power of ML forecasting models. BTW - Millions of professionals now have access to the tools to craft powerful ML forecasting models. Python in Excel is included with M365 subscriptions and provides access to libraries such as scikit-learn and statsmodels. Everything you need to go far beyond Microsoft Excel's forecast worksheet.

They were hemorrhaging money on digital tools their managers refused to use. The situation: A retail giant in the diamond industry with post-COVID digital sales tools sitting unused. Store managers resisting change. Market volatility crushing performance. Here's what every other company does: More training on features. Explaining benefits harder. Pushing adoption metrics. Here's what my client did instead: They ignored the technology completely. Instead, they trained 200+ managers on something nobody else was teaching; how to fall in love with change itself. For 8 months, we didn't focus on the digital tools once. We taught them Change Enthusiasm®, how to see disruption as opportunity, resistance as data, and overwhelm as information. We certified managers in emotional processing, not technical skills. The results were staggering: → 30% increase in digital adoption (without a single tech training session) →  2X ROI boost for those who embraced the mindset →  25% sales uplift in stores with certified managers →  96% of participants improved business outcomes Here's the breakthrough insight: People don't resist technology. They resist change. Fix the relationship with change, and adoption becomes automatic. While competitors were fighting symptoms, this company cured the disease. The secret wasn't better technology training, it was better humans. When managers learned to thrive through change, they stopped seeing digital tools as threats and started seeing them as allies. Most companies are solving the wrong problem. They're trying to make people adopt technology. We help people embrace transformation. The results speak for themselves. What would happen if you stopped training on tools and started training on change? ♻️ Share if you believe the future belongs to change-ready organizations 🔔 Follow for insights on making transformation inevitable, not optional

Ecommerce Trends That Will Define 2025 I’ve spent 15+ years in eCommerce—building, scaling, and investing in brands. Here are the biggest shifts happening right now (and how to stay ahead): ~~ 1. Amazon is eating the market Three years ago, many brands refused to sell on Amazon. Now, they have no choice. 2025 will be the year even die-hard DTC brands fully embrace it—or get left behind. == 2. Brands will stop relying on Meta ads CACs keep rising. Data is worse. And ad fatigue is real. Smart brands are diversifying into: • Amazon ads (better targeting, lower costs) • Wholesale (Nordstrom, Target, etc.) • Community-driven growth (UGC, affiliates, and referrals) • Applovin- especially for older demos == 3. AI-powered personalization will be standard Generic emails won’t cut it. AI will help brands send hyper-personalized messages, from abandoned carts to post-purchase upsells—based on real user behavior. == 4. More brands will offer memberships instead of discounts Discounting kills margins. In 2025, brands will shift toward paid memberships with perks like free shipping, exclusive drops, and loyalty rewards—modeled after Amazon Prime. == 5. The return of retail Retail isn’t dying—it’s evolving. Brands will use physical stores as acquisition channels, not just sales hubs. Having a presence in the right locations will boost online sales. == 6. Subscription models will get smarter The old “subscribe & save” model is dead. In 2025, brands will focus on: • Flexible subscriptions (skip, swap, pause anytime) • Bundled perks (like cashback on future orders) • One-time membership fees instead of recurring billing == 7. One-click checkout will dominate Every extra step kills conversions. In 2025, brands will integrate Amazon-like checkout (Shopify Shop Pay, Apple Pay, Bolt) to remove all friction. == 8. Brands will build moats around retention, not just acquisition Loyalty will be the new growth strategy. The best brands will: • Build post-purchase sequences • Offer reorder incentives • Use text and email strategically to bring customers back == 9. TikTok & UGC will drive more sales than paid ads 2025 will be the year brands fully shift from polished ad creatives to authentic, raw customer content—because it converts better than anything else. == 10. AI-powered customer service will take over Live chat? Too slow. Email support? Too late. The best brands will integrate AI chatbots that can instantly resolve issues, process returns, and even make product recommendations. == The eCommerce landscape is shifting fast. The brands that adapt will thrive. The ones that rely on old playbooks? Not so much. Follow Josh Payne for more eCommerce, SaaS, and investing insights.

AI at the Edge: Smaller Deployments Delivering Big Results The shift to edge AI is no longer theoretical—it’s happening now, and I’ve seen its power firsthand in industries like retail, manufacturing, and healthcare. Take Lenovo's recent ThinkEdge SE100 announcement at MWC 2025. This 85% smaller, GPU-ready device is a hands-on example of how edge AI is driving significant business value for companies of all sizes, thanks to deployments that are tactical, cost-effective, and scalable. I recently worked with a retail client who needed to solve two major pain points: keeping track of inventory in real time and improving loss prevention at self-checkouts. Rather than relying on heavy, cloud-based solutions, they rolled out an edge AI deployment using a small, rugged inferencing server. Within weeks, they saw massive improvements in inventory accuracy and fewer incidents of loss. By processing data directly on-site, latency was eliminated, and they were making actionable decisions in seconds. This aligns perfectly with what the ThinkEdge SE100 is designed to do: handle AI workloads like object detection, video analytics, and real-time inferencing locally, saving costs and enabling faster, smarter decision-making. The real value of AI at the edge is how it empowers businesses to respond to problems immediately, without relying on expensive or bandwidth-heavy data center models. The rugged, scalable nature of edge solutions like the SE100 also makes them adaptable across industries: Retailers** can power smarter inventory management and loss prevention. Manufacturers** can ensure quality control and monitor production in real time. Healthcare** providers can automate processes and improve efficiency in remote offices. The sustainability of these edge systems also stands out. With lower energy use (<140W even with GPUs equipped) and innovations like recycled materials and smaller packaging, they’re showing how AI can deliver results responsibly while supporting sustainability goals. Edge AI deployments like this aren’t just small innovations—they’re the key to unlocking big value across industries. By keeping data local, reducing latency, and lowering costs, businesses can bring the power of AI directly to where the work actually happens. How do you see edge AI transforming your business? If you’ve stepped into tactical, edge-focused deployments, I’d love to hear about the results you’re seeing. #AI #EdgeComputing #LenovoThinkEdgeSE100 #DigitalTransformation #Innovation

Grateful to be featured in the "Shoptalk Hot Takes" interview by Blenheim Chalcot and ClickZ.com alongside George Looker to unpack omnichannel commerce. 5 key takeaways and tactics from my conversation: 1. Design for Customer Continuity, Not Just Channel Expansion 💡 71% of customers expect brands to personalize interactions across every touchpoint. Tactical: Map out customer journey across channels, then design experiences that recognize and reward continuity—cart persistence, loyalty rewards, browsing history sync, etc. 2. Build the Infrastructure: Unify Data Streams Across All Touchpoints 🧠 Data fragmentation = missed opportunity Tactical: Integrate POS, e-commerce, mobile, social, and marketplace data into a centralized data lake or unified commerce platform. 3. Establish a Single Source of Truth for Customer Profiles 🔍 Brands with unified profiles see up to 2x better campaign performance. Tactical: Implement Customer Data Platforms (CDPs) to consolidate behavioral, transactional, and engagement data into unified customer profiles. 4. Partner Strategically for Scale, Not Just Stack ⚙️ A bloated tech stack doesn’t equal agility As I noted, Retailers are getting sharper about which partners can scale with them. Ecosystem efficiency matters more than ever. Tactical Step: Audit your tech stack and partnerships consistently. Prioritize partners that offer extensibility, future-proofing, and proven omnichannel success. 5. Measure What Matters: Unified KPIs Across Commerce 📈 You can’t optimize what you don’t measure holistically Tactical: Align your analytics stack to report holistically across channels—tie marketing to merchandising, CX to LTV, and inventory to revenue. 🧠 Bottom line: think holistically, move strategically, and build ecosystems that scale experience with agility, not just transactions. Complete list in comment 👇 #ecommerce #omnichannel #unifiedcommerce