Advanced Analytics for Ecommerce Insights

🌐 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 R. for your Help #Datafam #PowerBI #EcommerceAnalytics #MarketingROI #CustomerExperience #DataStorytelling #BusinessIntelligence #DashboardDesign #DataDrivenDecisions #DataStrategy #DataVIZ

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.

Scaling e-commerce brands isn’t about guesswork—it’s about data-driven creative strategy. Here’s the exact framework we use to turn creative insights into profit: 1. Shift From Guesswork to Data-Driven Decisions 🔹 Primary Metrics (Performance): Track purchases, CPA, and spend to know if your creative is working. 🔹 Secondary Metrics (Storytelling): Dive into Scroll Stop Rate, Hold Rate, Engagement Rate and Outbound CTR to understand why it’s working. Too many brands stop at CPA—without knowing why, you can’t replicate success. 2. Focus on Creative Optimization Build a Creative Optimization Feedback Loop to: ✅ Replicate winning elements across campaigns. ✅ Invest in high performers and cut underperformers. ✅ Refine creative weaknesses with precise insights. 3. Understand Consumer Behavior Primary metrics tell you the numbers; secondary metrics reveal consumer behavior. A low Hold Rate? It might be pacing or weak visuals. 4. Leverage Demographics & Placements Break down performance by age, gender, and placement to: 🔍 Discover hidden opportunities. 🎯 Personalize messaging for maximum impact. 💡 Tailor creatives for each segment. 5. Track Key Engagement Metrics Focus on: 👉 Scroll Stop Rate (grabs attention) 👉 Hold Rate (keeps attention) 👉 Engagement Rate (has emotion) 👉 Outbound CTR (drives traffic) Identify issues before they impact your budget. Final Thoughts: Systematic analysis > Guesswork. Data-driven creative wins. The best brands analyze, iterate, and scale—no gut feelings required. Action Plan: 1) Set up dashboards for performance and behavioral metrics. 2) Regularly review creative performance. 3) Use insights to refine and test new ideas. 4) Build a creative library of proven winners.

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.

Most brands think they’re “data-driven” until they meet AMC. AMC is more than DSP's companion. It’s a custom query engine sitting atop Amazon’s massive first-party dataset, allowing advertisers to unlock granular, cross-channel insights that were previously unreachable. Here’s what AMC quietly enables: -Holistic journey mapping across retail media, OTT, and on-site ads. -Custom attribution models beyond last-click or last-touch. -Audience overlap analysis for ultra-precise segmentation. -Predictive signals you can’t get anywhere else, when paired with the right data science muscle. Yet many brands treat it as an “advanced tool” for some future phase. But in 2025, waiting is the costliest strategy. While others optimize based on surface-level metrics, AMC-savvy teams are engineering market advantage via deeper, cleaner, closed-loop insights. Tech isn't the challenge, the mindset is. AMC rewards curiosity, technical fluency, and a willingness to rethink measurement. If you're not tapping into it yet, you should be. #AmazonMarketingCloud #RetailMedia #AdTech #DataDrivenMarketing #EcommerceStrategy #Martech #DigitalTransformation

Last month, a jewelry client increased their conversion rate by 32.7% and boosted revenue by 35.7% after implementing a CRO program based on shopper behavioral data in GA4. When they started with us back in September they had almost no data in GA4, and they had some concerns about the investing in Google Analytics implementation: ❌ "What is this going to tell me that my TripleWhale and Northbeam doesn't?" ❌ "Even if I have the insights, who is going to run CRO? Me?!!" ❌ "What if engagement increases but doesn’t translate into sales?" All valid concerns… But we showed them how behavioral research guides the way to greater conversions with statistics and an engineering approach increasing conversions —just by collecting the right data and using our AI to analyze behavior and get test suggestions. So we got to work: 🔹 Implemented tracking on the most important shopping behaviors 🔹 Ran through analysis of what shopping behaviors were correlated to transations 🔹 A/B tested the visibility of features ENCOURAGING those behaviors on PLP pages, measuring whether early exposure influenced conversion rates 🔹 Measured revenue impact to ensure I wasn’t just increasing engagement, but driving real sales Since we did that (+ some consistency), they’ve: ✅ Increased conversion rates +32.7% ✅ Generated 35.7% more revenue in that category. ✅ Built a repeatable, data-backed strategy for using what we learned across the entire website. If you're an eCommerce brand struggling with low conversion rates or uncertain about how to use shopper behavior effectively to run your CRO program. 📩 comment below, and I’ll share with you our templates for how we did it! #EcommerceGrowth #Clickvoyant #ConversionOptimization #googleAnalytics #MarketingAnalytics 🚀

𝗟𝗲𝘁'𝘀 𝘁𝗮𝗹𝗸 𝗮𝗯𝗼𝘂𝘁 𝗣𝗖𝗩𝗠 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 (𝗮𝗸𝗮. 𝗴𝗿𝗼𝘄𝘁𝗵 𝗱𝗿𝗶𝘃𝗲𝗿𝘀 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀)- a fundamental approach to understanding why your Net Revenue or Profits are up or down. This powerful tool breaks down the components driving revenue and gross profit changes. Here's how you can start implementing PCVM in your business: 1. Gather the Right Data - Collect detailed customer-product transactional data for at least the past 24 months. - Combine this with third-party data, especially competitive pricing, for a complete market view. 2. Leverage Advanced Analytics Tools - Move beyond Excel to tools like Tableau and Power BI or coding languages like Python and R to perform PCVM in real-time and at scale. - Use Price Elasticity modeling to run scenario analyses and understand the impact of pricing actions on your business. 3. Decode Your PCVM Results - Assess how pricing actions affected your revenue and profit changes, either compared to prior periods or compared to the budget. - Identify areas where pricing actions did not negate cost inflation or where you took price increases despite declining costs (positioning you above the competition and resulting in volume and gross profit $ loss). - Examine how your channel, customer, or product mix shifts impacted profitability and run scenario analyses against adjusting your mix. From my experience, companies that dive into PCVM analysis as part of their regular Pricing, Sales, and Operations reviews uncover hidden revenue opportunities and drive significant profit improvements. Transitioning from Excel to advanced analytics might seem like a big step, but it's actually quite simple, and the incremental decision-making benefits are immediate. To truly capitalize on these insights and elevate your Revenue Growth Management acumen, it's crucial to assess your current capabilities and identify areas for improvement.  𝗜 𝗲𝗻𝗰𝗼𝘂𝗿𝗮𝗴𝗲 𝗲𝘃𝗲𝗿𝘆𝗼𝗻𝗲 𝘁𝗼 𝘁𝗮𝗸𝗲 𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝗿𝗲𝗵𝗲𝗻𝘀𝗶𝘃𝗲 𝗥𝗲𝘃𝗲𝗻𝘂𝗲 𝗚𝗿𝗼𝘄𝘁𝗵 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗦𝗰𝗼𝗿𝗲𝗰𝗮𝗿𝗱. This free, 5-minute assessment will highlight your strengths and weaknesses across four critical areas: Pricing & Profitability Strategy, Pricing Analytics & Optimization, Promotion Analytics & Optimization, and Sales & Marketing Enablement. In the comments section, we are sharing a couple of resources for you: 1. Our 2024 Revenue Growth Analytics Maturity Scorecard 2. Our free Revenue Analytics Tools resources where you can download templates for PCVM in Excel, R, and Tableau.

🚨The greatest drop-off is from Product Details Page To Cart Page, so we must improve our Product Details Page! Not so fast ✋ In today's age of data obsession, almost every company has an analytics infrastructure that pumps out a tonne of numbers. But rarely do teams invest time, discipline & curiosity to interpret numbers meaningfully. I will illustrate with an example. Let's take a simple e-commerce funnel. Home Page ~ 100 users List Page ~ 90 users Product Display Page ~ 70 users Cart Page ~ 20 users Address Page ~ 15 users Payments Page ~12 users Order Confirmation Page ~ 9 users A team that just "looks" at data will immediately conclude that the drop-off is most steep between Product Details Page & Cart Page. As a consequence they will start putting in a lot of fire power into solving user problems on Product Display Page. But if the team were data "curious", would frame hypothesis such as "do certain types of users reach cart page more effectively than others?" and go on to look at users by purchase buckets, geography, category etc and look at the entire funnel end to end to observe patterns. In the above scenario, it's likely that the 20 cart users were power users whilst new & early purchasers don't make it to this stage. The reason could be poor recommendations on the list page or customers are only visiting the product display page to see a larger close up of the product. So how should one go about looking at data ? Do ✅ Start with an open & curious mind ✅ Start with hypothesis ✅ Identify metrics & counter metrics that will help prove/disprove hypothesis ✅ Identify the various dimensions that could influence behaviours - user type, geography, category, device type, gender, price point, day, time etc. The dimensions will be specific to your line of business. ✅ Check for data quality and consistency ✅ Look at upstream and downstream behaviour to see how the behaviour is influenced upstream and what happens to the behaviour downstream. ✅ Check for historical evidence of causality Dont ❌ Look at data to satisfy your bias ❌ Rush to conclude your interpretation ❌ Look at data in isolation - - - TLDR - Be curious. Not confirmed. #metrics #analytics #productmanagement #productmanager #productcraft #deepdiveswithdsk

Top Metrics Every #Ecommerce Company Should Answer - Average Order Value of First Order vs. Recurring Order: Since e-commerce usually struggles with retention, use the Average Order Value (AOV) of the first order to calculate the ROI on ads. Focus on making that first transaction count! - Likelihood to Order Again by Order Occurrence: Measure the probability of a customer making a second purchase and the likelihood of a fifth order after a fourth, etc.... This helps identify where the drop-offs are and determine when customers become loyal. If it takes four orders to hook a customer, then focus on getting to 4 orders and ignore the rest. - 3-Month, 6-Month, and 9-Month LTV by First Product and Order Size: Track the Lifetime Value (LTV) over time based on the initial product purchased and order size. This helps assess the combination of order value and retention, revealing which products drive the highest LTV. Ensure that initial spending isn't too low (risking negative marketing ROI) or too high (which can decrease LTV). - Media Mix Percentage for First Buyers: Determine if your first-time customers are influenced by multiple ads, whether they return organically, or if they switch platforms. This insight is crucial for fine-tuning performance marketing strategies for new customers. (Also can save your data teams months on building a complex media mix attribution that is not needed) - Spend on Converted Customers: With cookies constantly being reset, keep updating your list of converted customers. Despite the promises of Meta and Google, converted customers often still see ads. Ever wonder why you keep seeing ads for products you already bought? These metrics provide significant insights quickly, offering the biggest impact for your e-commerce strategy. What metrics do you find helpful? What am I missing? React with 💡 if you like this kind of content, and I'll share more insights across different industries. #ecomm #data #metrics #bestpractices #ltv #roi

What is a Commerce Graph 🌐 and why will it be so critical in the times ahead? Historically, understanding consumer behavior has largely been relegated to the walled gardens of Meta and like platforms... Imagine a vast network connecting every aspect of the e-commerce world - a comprehensive map that captures and analyzes data from every interaction, product, and consumer within the digital retail space. At its heart, the Commerce Graph is a treasure trove of interconnected data points, from customer profiles to purchase histories, browsing behaviors, and more. If you can find a way to unlock this data, businesses gain deep insights into consumer preferences, trends, and patterns, empowering them to make informed decisions and personalized shopping experiences. Why It's So Valuable: 360-Degree Customer Insights 🏂 >>> It can enable a panoramic view of the customer journey. By aggregating data from various channels and touchpoints—think website visits, social media interactions, and email engagements—it provides a comprehensive understanding of how customers engage with a brand from start to finish. Tailored Shopping Experiences 🛍️ >>> Businesses can leverage the graph to deliver highly personalized shopping experiences. By analyzing past behaviors and preferences, merchants can tailor product recommendations, promotions, and marketing messages to individual tastes, boosting conversions and fostering loyalty. Predictive Analytics and Forecasting 🎯 >>> It can enable predictive analytics and forecasting. By spotting trends and patterns in historical data, businesses can anticipate future consumer behavior and market trends, optimizing inventory management, pricing strategies, and marketing campaigns accordingly. Maximized Revenue Potential 📈 >>> Insights from the Commerce Graph help identify cross-selling and upselling opportunities. By analyzing purchase patterns and product affinities, businesses can suggest complementary products or upgrades, driving up average order value and boosting sales. Seamless Omnichannel Experiences 🌐 >>> In today's omnichannel world, consistency is key. The Commerce Graph allows businesses to unify customer data from various channels and sync interactions in real time, ensuring a seamless shopping journey across all touchpoints. Disco has been building its own commerce graph over the last 3 years. We've been exposed to $40B of transaction data, which has enabled us to amass and enrich 100M+ unique shopper profiles. This data is used to power our predictive recommendation engine at the hear of our post-purchase ad network. And this is just the beginning ⚡️