E-Commerce Technology Platforms

A few weeks ago, Amazon Web Services (AWS) started renting out its AI-powered shopping experiences to other retailers, including kate spade new york.   The technology is impressive. The real advantage is the data behind it.   ChatGPT, Claude, and Gemini are brilliant with language. Ask them to compare two air purifiers and you'll get a sharp answer.   What they can't see is you. They have no idea what you bought last year, what you returned, or what you almost bought last week.   Amazon has your entire shopping history — your searches, your buys, your cart ads. And not just yours, but hundreds of millions of shoppers, going back years. They know so much about you that the agent doesn't have to guess what you want. It already knows.   A competitor can train a smarter model. It can't manufacture years of your customers' shopping history.   Amazon says conversational shopping already converts at 3.5 times the rate of keyword search. Hand that to an assistant that already knows your last ten orders and it stops being a fair fight.   That's the big difference between what Amazon can do and what the other AI platforms can't. The agent that knows the customer wins, and Amazon is the one that already knows them.

This morning at Amazon Accelerate the focus was firmly on AI. One thing I like about Amazon’s approach is that the use of AI is extremely practical and is squarely linked to improving the customer and seller experience. All too often AI is talked about in very nebulous ways, without proper consideration of the applications. Also, Amazon has been using some form of AI, especially machine learning, for over 25 years. This isn't new for them; but they're embracing the advances. Here are some things I learned… 🤖 Amazon is launching an AI-powered seller assistant called Amelia. It is designed to help sellers across all aspects of their selling journey. Amelia will also help sellers assess performance and recommend improvements. 📋 Producing listings and content is very time consuming for sellers. AI tools are helping to speed and simplify the process; and its making listing more effective by helping sellers identify keywords and search terms that customers use. 👀 Product titles will no longer be static but will be personalized based on individual users and what they’ve searched for. The example was used that if you search for pink aviator sunglasses, AI will ensure that for relevant listings the word pink is included in the title. 🎥 AI tools are already in place to help sellers create images. This is now being extended to video. With one click, sellers can provide a static image of their product, and it will be turned into a relevant video. Video has better conversion rates for selling. ⭐️ Last year, customers left 125 million reviews on Amazon. That’s too much information to sift through manually. AI is helping buyers and sellers by quickly summarizing and pulling out key trends and points from all reviews. 📉 There are a stack of AI tools helping sellers make more sense of various analytics within their businesses. This helps them make more informed decisions and to better forecast the impacts of decisions like advertising more. 👩🏽⚖️ AI is helping sellers to ensure they are compliant with rules and regulations. 📺 Outside of AI, Amazon’s ads now reach 275 million people per month in the US across all channels. #retail #retailnews #AmazonAccelerate #Amazon #AI #ecommerce

Ever wondered how GA4 is changing the game for B2B websites? Let’s dive into the world of Google Analytics 4 together. I’ve been tinkering around with GA4 and am amazed at how it transforms our approach to B2B website analytics. From user engagement to e-commerce insights, GA4 is like a treasure trove waiting to be unlocked. 📈🔍 Here are some key metrics in GA4 that are particularly impactful for B2B sites. Let’s decode them: 1. User Engagement: GA4’s focus on event-based tracking allows us to gauge user interactions more effectively. Metrics like engagement rate, engagement time, and specific event completions provide a deeper understanding of how users interact with our content and offerings. 🔍 2. Conversion Rates: With GA4, we can define and track custom conversions, allowing us to monitor specific actions that are crucial to our B2B objectives, such as form submissions, PDF downloads, or sign-ups. 📈 3. Source and Medium: Understanding where our traffic is coming from (organic, direct, referral, social) is critical. GA4 allows us to dissect these traffic sources, helping us optimize our channel strategies for better lead generation. 🌟 4. Funnel Analysis: GA4’s advanced analysis features enable us to track the user journey through various stages of our funnel. This is crucial in identifying drop-off points and optimizing the path to conversion. 📊 5. Customer Lifetime Value (CLV): In B2B marketing, the long-term value of a customer is paramount. GA4 helps us estimate CLV, allowing for strategies that focus on long-term profitability and customer relationship building. 🌱 6. Audience Segmentation: The ability to create detailed audience segments in GA4 helps in tailoring our strategies to specific user groups, enhancing the relevance and effectiveness of our marketing efforts. ✍️ 7. E-commerce Tracking: For B2B sites with e-commerce elements, GA4 provides detailed metrics on buyer behavior, transaction data, and revenue tracking, offering valuable insights into customer purchasing patterns. 💰 GA4 isn’t just about tracking data; it’s about unlocking actionable insights that drive informed decisions. As we continue to explore its capabilities, the potential for refining our B2B strategies is immense. I’m keen to hear from others leveraging GA4. What metrics have been most insightful for your B2B site? Share your experiences below! 👇 #GA4 #B2BMarketing #DataDrivenInsights #DigitalStrategy #Analytics #Growth

🌐 Behind Every Click is a Story I Let the Data Tell It. 📊✨ In a world where e-commerce brands pour thousands into campaigns and still struggle with cart abandonment, product returns, and low retention, the real question isn’t “What happened?” , it’s “Why did it happen?” and “How do we fix it?” 🔎 That’s where data comes in. 📈 And this is where Power BI becomes more than just a dashboard, it becomes a lens for clarity. Over the past few weeks, I built a full-scale, interactive e-commerce performance dashboard, touching every point from marketing campaigns to customer satisfaction. The goal? Make sense of the chaos. Turn complexity into simplicity. Drive action. 🧠 Here’s What I Discovered: ✅ Marketing Channels Instagram drove the most engagement, but Email had the best ROI. Billboard Ads, though expensive, performed poorly — proof that visibility ≠ value. ✅ Cart Abandonment Patterns Over 15% of carts were abandoned. The biggest culprit? Cash on Delivery (COD) users. Fashion orders also had the highest failure and return rates — a clear sign to revisit fulfillment strategies. ✅ Customer Insights That Matter Females aged 35–44 were power buyers across categories Credit Card and PayPal users had smoother journeys. ✅ Returns & Dissatisfaction Top reasons for returns: 📦 “Item Not As Described” 💔 “Arrived Damaged” These aren’t just logistics issues — they’re missed chances to improve product listings and supply chain quality. 🚀 What This Dashboard Achieved: Instead of just dropping charts, I focused on building a narrative: 📌 A story of behavioral trends 📌 A story of missed revenue opportunities 📌 A story that guides business decisions with confidence Power BI didn’t just help me visualize — it helped me strategize. 💡 Final Takeaway Your data is always talking. But without the right tools and the right mindset, it just looks like noise. 📣 This project reminded me why I love data analysis — not just for the numbers, but for the stories they unlock and the decisions they inspire. Let’s connect if you’re building something cool in the analytics space — I’m always open to swapping insights and perspectives. Thanks to Jude Raji for your Help #Datafam #PowerBI #EcommerceAnalytics #MarketingROI #CustomerExperience #DataStorytelling #BusinessIntelligence #DashboardDesign #DataDrivenDecisions #DataStrategy #DataVIZ

𝗧𝗟;𝗗𝗥: Amazon's multi agent design in 𝗜𝗻𝘀𝗶𝗴𝗵𝘁 𝗔𝗴𝗲𝗻𝘁𝘀 orchestrates specialized AI workers that transform how 1M+ sellers run their businesses leading to outsize outcomes. 𝗙𝗿𝗼𝗺 𝗗𝗮𝘁𝗮 𝗢𝘃𝗲𝗿𝗹𝗼𝗮𝗱 𝘁𝗼 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 E-commerce sellers face a paradox: rich tools everywhere, insights nowhere. Amazon's response? 𝗜𝗻𝘀𝗶𝗴𝗵𝘁 𝗔𝗴𝗲𝗻𝘁𝘀 (IA)—an LLM-based multi-agent system that lets sellers simply ask: "𝘞𝘩𝘢𝘵 𝘸𝘦𝘳𝘦 𝘮𝘺 𝘵𝘰𝘱 10 𝘱𝘳𝘰𝘥𝘶𝘤𝘵𝘴 𝘭𝘢𝘴𝘵 𝘮𝘰𝘯𝘵𝘩?" or "𝘏𝘰𝘸 𝘥𝘰𝘦𝘴 𝘮𝘺 𝘣𝘶𝘴𝘪𝘯𝘦𝘴𝘴 𝘤𝘰𝘮𝘱𝘢𝘳𝘦 𝘵𝘰 𝘣𝘦𝘯𝘤𝘩𝘮𝘢𝘳𝘬𝘴?" (Read more here: https://bit.ly/41cbt4R) No more hunting through dashboards. Just natural conversation yielding precise data insights. 𝗧𝗵𝗲 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 IA's hierarchical manager-worker structure optimizes for coverage, accuracy, and latency: 𝗠𝗮𝗻𝗮𝗴𝗲𝗿 𝗔𝗴𝗲𝗻𝘁:  • Lightweight encoder-decoder for Out-of-Domain detection (96.9% precision)  • BERT-based classifier for agent routing (83% accuracy, 0.31s latency)  • Query augmentation for temporal disambiguation  • Parallel processing to minimize latency 𝗪𝗼𝗿𝗸𝗲𝗿 𝗔𝗴𝗲𝗻𝘁𝘀:  • Data Presenter: Handles descriptive analytics ("Show me sales trends")  • Insight Generator: Provides diagnostic analysis ("How is my business performing?") 𝗧𝗵𝗲 𝗦𝗲𝗰𝗿𝗲𝘁 𝗦𝗮𝘂𝗰𝗲: 𝗥𝗼𝗯𝘂𝘀𝘁 𝗗𝗮𝘁𝗮 𝗠𝗼𝗱𝗲𝗹 Unlike fragile text-to-SQL approaches, IA leverages:  • API-based data retrieval with built-in constraints  • Divide-and-conquer query decomposition  • Dynamic domain knowledge injection  • Strategic planning for granular data aggregation 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲  • 89.5% question-level accuracy  • <15s P90 latency  • 97.7% relevancy score  • 95.8% correctness score All of this is powered by of course Amazon Web Services (AWS) Bedrock and SageMaker. Currently live for Amazon US sellers, transforming how businesses interact with their data. Great work by Jincheng Bai and team! 𝗧𝗵𝗲 𝗔𝗺𝗮𝘇𝗼𝗻 𝗔𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲 Insight Agents isn't just another chatbot—it's a force multiplier for sellers. By combining lightweight specialized models with strategic LLM deployment, Amazon delivers enterprise-grade insights at conversational speed. The future of business intelligence isn't more dashboards. It's intelligent agents that understand your questions and deliver precise, actionable insights.

🚀 Game-Changer for Sellers: Amazon Now Powers Fulfillment for Walmart, Shopify & SHEIN Orders Amazon just made a bold move at its Accelerate seller conference—expanding its Multi-Channel Fulfillment (MCF) to support orders from Walmart, Shopify, and SHEIN. This means sellers can now use one shared pool of inventory to fulfill orders across multiple platforms, unlocking: ✅ 19% average boost in sales ✅ Fewer stockouts ✅ Faster inventory turnover This is more than just logistics—it's a strategic leap for small and medium-sized businesses looking to scale efficiently across channels. 🛍️ What’s New: SHEIN: New MCF app in Seller Central & SHEIN Seller Hub Shopify: Real-time tracking & inventory sync via Shopify’s Fulfillment Network Walmart: Seamless integration via WebBee, Pipe17, Goflow with unbranded packaging Amazon is also investing $15B in 80 new warehouses and $4B to triple rural delivery reach, signaling a future where same-day and next-day delivery becomes the norm—even outside major cities. 📦 The convergence of marketplaces and fulfillment networks is reshaping how sellers operate. The question now is: Are you ready to optimize across platforms with a unified inventory strategy? #ecommerce #supplychain #retail #management #logistics

📌📊 eCommerce Google Analytics 4 Dashboard I've recently created a Looker Studio Report for eCommerce brands. The main goal is to analyze website traffic performances using GA4 Data. Having a custom-built solution like this will give marketers an edge over their competition and understand performances from different traffic sources. ➡️ Where do your users come from? The dashboard breaks down traffic by channel, showing the distribution across direct, organic search, referral, and other sources. It also provides geographical data to understand your primary markets. ➡️ Which campaigns/traffic sources bring the most revenue? Revenue is broken down by channel, allowing you to compare the performance of different traffic sources over time. This helps identify which channels are most effective for driving sales. ➡️ What are the key performance indicators? The dashboard tracks crucial eCommerce KPIs including Total Revenue, Purchases, Conversion Rate, and Average Order Value. It also monitors user engagement metrics like sessions, bounce rate, and average session duration. ➡️ How does device type impact user behavior? Device type data shows the distribution of sessions across desktop, mobile, and tablet. This information can help optimize the user experience across different platforms. This level of insight helps marketers make informed decisions to drive better results for their advertising efforts. As a marketer, it has never been easier to manage your marketing data and turn it into actionable insights. ⚙️ Technical note: In this example, I've used Looker Studio native GA4 data connector to import the data 🔍 Demo Version: https://lnkd.in/e4YWQBGv #DataAnalytics #DataVisualization #BusinessIntelligence

The shift no one saw coming: Amazon just gave FBM sellers an FBA-level advantage. After years of pushing sellers toward Fulfillment by Amazon, Amazon is now expanding the other side of the equation. FBM Ship+, a new delivery solution for self-fulfilled orders, quietly turns every seller into a potential “micro-FBA node.” The premise: FBM Ship+ lets self-fulfilling sellers offer expedited delivery speeds at no additional cost, while displaying more accurate delivery promises to buyers.  In Amazon’s pilot, participating sellers delivered packages an average of nine days faster, driven by two changes: 🔵 Partner carriers shipped 2.5 days faster on average. 🔵 Amazon’s new predictive model corrected sellers’ manual estimates by 6.5 days. According to Amazon’s internal study, sellers saw an average 34% sales lift once faster, verified delivery dates were displayed on their listings. Currently, FBM Ship+ is available for shipments from China to the U.S., U.K., Germany, Italy, Spain, France, and Japan, with Amazon confirming plans to expand to more markets and domestic routes over time. FBM Ship+ automates what used to be manual friction: 1️⃣ Sets accurate delivery dates: Amazon assigns a one-business-day handling time and fast delivery windows based on partnered expedited carriers. Same-day handling applies to SKUs historically shipped within a day. 2️⃣ Uses Amazon Buy Shipping: Sellers purchase discounted shipping labels directly through Amazon Buy Shipping, whether in Seller Central, via API, or through ERP integrations. Each label shows real-time discounted rates and the cash-back amount per order. 3️⃣ Earns cash back: Sellers hand off packages to partner carriers by the estimated ship date and ensure a scan within 48 hours. The cash-back credit (valid in all eligible marketplaces) is automatically applied to their Seller Central account. Early adopters also qualify for ¥2 per-order cash back on shipments from China to Europe until December 31, 2025. And the thing is that for years, Amazon’s fulfillment system has rewarded dependency, FBA meant control, FBM meant friction. FBM Ship+ feels like it is in the middle of the line. It gives self-fulfilled sellers FBA-like precision and buyer confidence, while allowing Amazon to extend its delivery network without expanding warehouses. This feels like a strategic decentralization of Prime-level logistics, where every package shipped through FBM Ship+ feeds Amazon more data, strengthens its predictive network, and deepens its influence over seller behavior, without touching inventory. #AmazonFBA #Logistics #Amazon #SupplyChain

As a director of e-commerce, I tried growing without the right marketing tools. It did not go well. At first, I thought I could make it work. Google Analytics for user behavior tracking. Meta Ads Manager for attribution. Google Tag Manager for A/B testing. A scrappy growth stack. Cheap. Efficient. Genius. It failed. GA4 made tracking impossible. Meta and Google both swore they drove 100% of our revenue. GTM required a developer for the smallest experiment ever. I spent more time debugging than actually growing the business. That’s when I realized: You can’t grow what you can’t see. Without the right data, every decision is a guess. So we stopped piecing things together and built a marketing stack that actually gives us reliable insights. Here’s what actually moved the needle: Heap | by Contentsquare: user analytics, heatmaps & session recordingsGA4 is a disaster. Heap auto-tracks user behavior, so we can see where revenue is leaking and fix it, fast. Crazy Egg: user surveys. Data only tells you what’s happening. Surveys tell you why. We use Crazy Egg to collect real feedback on why customers don’t buy. Zoom→ customer interviews. LTV comes from repeat buyers. We talk to our best customers every month to understand what keeps them coming back. Optimizely→ A/B testing & personalization. Most teams “experiment” without real insights. Optimizely helps us run controlled tests that impact conversion rates, AOV, and retention. Triple Whale: attribution & performance insights. Ad platforms take credit for every sale. TripleWhale gives us a real source of truth for attribution, so we can optimize smarter. Segment: customer data platform (CDP)Your data is fragmented across tools. A CDP makes sure every marketing channel has clean, consistent tracking. SendGrid: automated and marketing emailsBetter deliverability = higher retention and more repeat purchases. SendGrid makes it easy to iterate and improve. Most e-commerce teams don’t fail because of bad ideas. They fail because they can’t see what’s actually happening. If you don’t have the right insights, how can you optimize RPV and LTV? How do you ever know what experiment to run? E-commerce teams, what’s in your growth stack? What’s missing? Let me know if there is a tool you think is better.

🛒 How Basket Analysis Can Drive eCommerce Growth: A Bangladeshi Scenario As eCommerce continues to grow rapidly in Bangladesh, businesses are dealing with more and more customer data. One of the most valuable and often overlooked ways to make sense of that data is through basket analysis. Whether you’re working at a platform like Daraz, Chaldal, Pickaboo, or even running your own online shop, basket analysis can help uncover what products people are buying together. These insights can help you make smarter decisions when it comes to marketing, product placement, bundling, and personalized offers. 🔍 What is Basket Analysis? Basket analysis (also known as market basket analysis) is a method used to find associations between products based on customer purchase history. For example: - What do people usually buy with rice? - Are customers who buy smartphones also buying covers or screen protectors? - Are snack items more popular during weekends? By identifying patterns like these, eCommerce platforms can: - Increase average order value - Run more effective cross-sell campaigns - Deliver personalized recommendations - Make better inventory decisions 🧺 Real-Life Example: A Case Based on Chaldal While analyzing data from Chaldal, one of Bangladesh’s largest online grocery platforms, we noticed something interesting. Many customers in areas like Dhanmondi and Mirpur were buying instant noodles and tomato ketchup together, especially during the evening. This pattern suggested a common need: quick dinner solutions, likely for students or working professionals. Based on this insight, we tested a few simple strategies: - Introduced a combo offer with noodles and ketchup - Showed both products in the “Frequently Bought Together” section - Ran targeted push notifications in the evening with a message like “Need a quick dinner? Grab our Noodles + Ketchup combo now!” The early results were promising: - Better product visibility - More engagement during evening hours - A small bump in basket size for repeat users We’re still monitoring the data, but it’s a great example of how even small insights can be turned into smart decisions. 💡 Final Thoughts You don’t need AI or complex tools to start using basket analysis. A simple SQL query or spreadsheet analysis can help you uncover product relationships that lead to real business value. #eCommerce #BasketAnalysis #DataAnalytics #DigitalBangladesh #CustomerInsights #BusinessGrowth #SQLforBusiness #OnlineGrocery #MarketingStrategy #StartupBangladesh