Understanding Ecommerce Analytics Tools

Yesterday, in the flood of mind-blowing, benchmark-setting, GPU-melting AI announcements, it was easy to overlook the quiet little beta announcement coming out of Amazon - one that focuses less on the tech and more on the consumer, asking a question as old as innovation itself: “Cool tech bro, how do you monetize that tho?” Enter: Interest AI. ✨ Amazon’s new LLM-powered assistant, now in beta, lives inside the shopping app. It’s trained not on the open internet - but on YOU. What you’ve browsed, bought, returned, reviewed, streamed at 2 a.m., and forgotten in your cart. Ask it: “What’s a good beginner camera?” Get: “Here’s one based on your budget, your previous purchases, and your mild obsession with aesthetically pleasing home decor.” It doesn’t just answer questions. It answers your questions. Personalized, contextual, and commercial from the jump. But here’s the real play: Interest AI doesn’t just respond to intent - it generates it. It constantly scans Amazon’s massive, ever-expanding catalog to surface new items tied to your passions - travel, fitness, cooking, your cat’s wardrobe. It transforms how you discover, not just how you shop. It's not just a smarter search bar - it's a predictive, personalized discovery engine at scale. Interest AI not sexy. It won’t pass a Bar exam. But it might get you to click “Add to Cart.” And that, of course, is the point. Amazon isn’t chasing AGI. It’s chasing 💰 CLV (customer lifetime value) 💰 . While others build general-purpose LLMs, Amazon builds contextual commerce machines. This could quietly become one of the most monetizable use cases of LLMs we’ve seen to date. And it leans into Amazon’s real edge: first-party data, not foundational models. While the market experiments with AI co-pilots, Amazon just strapped a personalized sales engine to the world's biggest mall.

🚨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

Meta says purchases are up 50%. Shopify says they're up 7%. Somebody's lying, and it's probably not Shopify. Just reviewed a client's dashboards that perfectly capture the attribution crisis plaguing performance marketing. Meta Ads Manager:  → 50% increase in purchases → ROAS improving to 2.43x → Spend up across all accounts → "Winning" campaigns everywhere Shopify Analytics (same period): → 7% increase in actual orders   → Revenue growth flat → AOV unchanged → Real business impact: minimal The uncomfortable truth? We're celebrating fake growth. This isn't about iOS changes or cookie deprecation. It's about platforms optimizing for credit, not results. When you run multiple accounts, retargeting campaigns, and cross-platform efforts, attribution becomes a hall of mirrors. Every platform claims victory for the same conversion. The fix isn't better attribution models. It's incrementality testing: → Geographic holdouts (run ads in some regions, not others) → Customer surveys asking "how did you actually find us?" → Marketing mix modeling that accounts for organic growth → Focus on net new customer acquisition, not total conversions I've seen brands "optimize" themselves into bankruptcy while their dashboards showed green arrows everywhere. Real performance marketing means measuring what matters: incremental revenue, not platform-reported conversions. The best campaigns often look terrible in ad dashboards because they're creating demand, not just harvesting credit. How big is the gap between your platform metrics and actual business growth?

Two major updates to Amazon Marketing Cloud (AMC) today: First, the long-awaited 5-year historical purchase data view is now available for everyone. This shows us customer behavior patterns we've never seen before. Here's what I mean: I recently looked at data from a CPG manufacturer: * 1-year window: 37% repeat purchasers, $24 average GMV * 5-year window: 85% repeat purchasers, $185 average GMV The difference is striking. With five years of data, brands can now: * Spot product lifecycles * Map seasonal patterns across multiple years * Track how customers move through product portfolios * Understand actual customer value over time Second announcement - Amazon is removing cost barriers for AMC features. For example, Amazon Insights, which was previously a paid feature, is now available at no cost. These signals allow you to Analyzes custom audience segments to show behavior patterns, media exposure, shopping activity, and purchase trends. This Helps to refine your media strategy by showing what’s resonating with your most valuable audiences and enables advanced segmentation for future targeting or suppression strategies. For anyone wanting to try the 5-year data view, or learn about building AMC audiences, reach out to your AMC tool provider or contact your Amazon Ads PDM.

Every brand runs WhatsApp or CRM campaigns. Few know when to send them. Festive season or not, most teams guess their timing. Or most teams just follow what’s been done before. Someone set that timing months ago and no one ever questioned it. But there’s a simpler, data-backed way to plan it. Go to your 𝗦𝗵𝗼𝗽𝗶𝗳𝘆 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 → Check your 𝘀𝗮𝗹𝗲𝘀 𝗯𝘆 𝗵𝗼𝘂𝗿 for weekdays and weekends. You’ll instantly notice your “natural” buying peaks. In most D2C brands, I’ve found 3 clear peaks: ▪️ Morning (around 10 AM) ▪️ Afternoon (around 2 PM) ▪️ Night (around 9 PM) Yours might differ slightly but the pattern will exist. Now, instead of blasting your CRM or WhatsApp messages randomly, schedule them 𝟯𝟬–𝟲𝟬 𝗺𝗶𝗻𝘂𝘁𝗲𝘀 𝗯𝗲𝗳𝗼𝗿𝗲 𝘁𝗵𝗼𝘀𝗲 𝗽𝗲𝗮𝗸 𝘀𝗮𝗹𝗲𝘀 𝗵𝗼𝘂𝗿𝘀. That way: Delivery is done before the buying window starts. You hit the inbox right when intent is highest. Simple tweak. Big lift in CTR and conversions. 𝗔𝗰𝘁𝗶𝗼𝗻𝗮𝗯𝗹𝗲: Don’t send CRM campaigns when you can. Send them when your customers are buying. Agree? What natural buying peaks you have observed? 👇 #PeformanceMarketing #CRM #WhatsAppCampaign #GrowthMarketing

📌 Power BI Breakdown # 11: Shopify Analytics Shopify has become the standard for eCommerce businesses. Whether you’re running a DTC brand or scaling globally, chances are your store lives on Shopify. And that means one thing: a goldmine of data. Every product view, every checkout, every fulfilled order leaves a trail of insights. But here is the problem: that data usually stays locked inside Shopify’s own ecosystem. Yes, Shopify Analytics is handy for a quick glance. But let’s be honest, business users often get lost in those native reports. One team looks at Ads Manager, another pulls Shopify dashboards, Finance has its own numbers in Excel… and before you know it, nobody is looking at the same reality. That’s when data silos appear. Teams spend more time debating numbers than actually acting on them. So what’s the alternative? You bring the data together. Now imagine what happens when you combine Shopify data with your other platforms: ⤷ Ads (Meta, Google, TikTok) to connect spend with real sales. ⤷ CRM to track how customers move from first click to repeat order. ⤷ Finance to tie revenue and profitability back to budgets. You’re looking at the entire growth engine of your business. That’s the idea behind this 11th post in the Power BI Breakdown series: a practical use case of Power BI for eCommerce businesses And here’s where things get interesting: once you centralize all these streams into a data warehouse, you’re building a single source of truth. Then, when the CEO, the marketing lead, and the operations manager all log into Power BI and see the same trusted numbers, the conversations change. → You stop asking which number is right? → You start asking what should we do next? This Shopify demo dashboard I built is just one example. It doesn’t just show revenue. It pulls in sales, customers, marketing, operations, and product insights side by side. It ties Shopify’s data foundation with the bigger ecosystem. For the design itself, I took huge inspiration from Nicholas Lea-Trengrouse (especially for the navigation elements and main KPIs). Treating dashboards like web-app products makes adoption so much easier for business users. 🟢 Live Demo Here (Sample Data): https://lnkd.in/eVat6f_m

After Walmart’s partnership with ChatGPT, I knew Amazon wasn’t far behind... Amazon just rolled out a new AI feature called “Help Me Decide.” Amazon’s AI is learning what real shoppers care about: what they click, what they buy, what they return. They’ve already rolled out: - Rufus, the AI shopping assistant that answers questions in real time - Shopping Guides, for expert-backed product picks - Interests, which tracks what you care about to surface new products Now “Help Me Decide” takes all that data and turns it into a single, confidence-boosting recommendation. It looks at your browsing and purchase history, then recommends one product it thinks is right for you (and explains why). This could be a big win for sellers as conversions should improve with customers having better information without jumping over to other AI tools It means your visibility on Amazon will increasingly depend on how well your product aligns with shopper intent and signals, not just your bids. If your listing actually helps people make a confident decision, you win. And when your targeting and data are dialed in, your ads start working smarter, not just harder. Funny enough, I was doing this manually months ago using ChatGPT prompts to compare supplement listings and help me pick the right product. I’m curious if “Help Me Decide” will finally let me make those comparisons directly on Amazon. Haven’t seen it live yet, but I’m hopeful.

Your ad platform is telling you one number. The truth is somewhere else entirely. I ran the same period of data through Google Ads, Meta Ads and independent measurement tools simultaneously. The gaps were not small. Google Ads claimed 9.3x ROAS. Independent measurement showed 3.8x to 6.2x. Meta claimed 11x. Independent measurement showed 2.6x to 6.5x. And GA4 - which most brands are using as their revenue source of truth - undercounted total revenue by 27% versus Shopify. This is not a coincidence. Ad platforms are incentivised to show high ROAS so you keep spending. Their attribution windows are set to maximise credit for their own channel. The practical implication is straightforward: do not make budget allocation decisions based on in-platform ROAS. In this sample, true performance was overstated by between 50% and 330% versus independent measurement. There is a free setup that fixes this. No expensive tools required. Full breakdown in the comments.

Attribution has never been perfect, but for DTC brands, it has become significantly harder in the past few years. Apple’s iOS14 updates, third-party cookie deprecation, and increased privacy regulations have disrupted traditional attribution models. Brands that once relied on last-click attribution, ad platform reporting, or rule-based LTV calculations now face major blind spots in understanding which marketing efforts drive long-term value. Even those investing in first-party data strategies, post-purchase surveys, and media mix modeling (MMM) struggle to fully connect the dots. The reality is that data is still fragmented across multiple platforms such as Shopify, Klaviyo, Google Analytics, ad networks, and third-party analytics tools. Most solutions focus on aggregating data, but aggregation alone doesn’t tell the full story of how customers move through the funnel and what actually drives retention. Rob Markey - In his article, "Are You Undervaluing Your Customers?" published in the Harvard Business Review, Markey emphasizes the significance of measuring and managing the value of a company's customer base. He advocates for creating systems that prioritize customer relationships to drive sustainable growth. Chip Bell - Recognized as a pioneer in customer journey mapping, Bell has contributed significantly to the field of customer experience. In an interview titled "The father of customer journey mapping, Chip Bell, talks driving innovation through customer partnership," he discusses how organizations can co-create with customers to drive innovation and enhance the customer journey. So how do brands solve this? 1. Shift from static LTV models to predictive insights - Traditional LTV calculations are backward-looking, often based on averages that don’t account for future behavior. Predictive analytics, using real-time behavioral and transactional data, can provide a more accurate forecast of customer lifetime value at an individual level. 2. Invest in first-party data strategies that go beyond acquisition - Many brands have adapted to privacy changes by collecting more first-party data, but few are fully leveraging it. Loyalty programs, surveys, and on-site behavioral tracking can provide valuable insights into retention and repeat purchase drivers, helping brands reallocate spend more effectively. 3. Adopt AI-driven segmentation and customer equity scoring - RFM segmentation and standard cohort analysis have limitations. AI-powered models can help identify high-value customers earlier in their lifecycle, predict churn risk, and optimize acquisition based on true long-term value, not just early spend. Markey and Bell have long emphasized that customer loyalty isn’t built on transactions alone, it’s about the entire journey. Brands that can better understand and predict customer value will be the ones that thrive in a world where third-party tracking is no longer a reliable option. #CustomerJourney #Attribution #CustomerEquity

What happens when you align product performance with sessions, conversion rate, advertising spend, stock on hand and sell-through date? You stop guessing and start making commercial decisions with real clarity. The best merchandise planners and marketers already know this: no metric in isolation tells the full story. The strongest teams are combining traditional planning metrics with ecommerce performance data to understand not just what is happening, but why. For DTC brands, bringing these data points together turns a messy performance picture into a simple set of actions: 🔍 1. Decide what to advertise more When a product has strong conversion, healthy margins and enough stock to support demand, but low sessions, it’s usually a sign that it needs more visibility. This is the sweet spot for scaling paid spend: the product already proves it can sell — it just needs more traffic. 💸 2. Identify what to mark down If you’re holding too much stock and the sell-through date is creeping up, yet conversion is weak even with steady sessions, discounting becomes a strategic lever. Markdowns help clear inventory without wasting ad spend on products the customer clearly isn’t choosing at full price. ✋ 3. Know when to pull back advertising High ad spend + plenty of sessions but poor conversion = a red flag. This is where you pause or reduce spend, diagnose the issue (price, positioning, creative, customer reviews), and redirect budget to products with stronger unit economics. Sometimes the best ROI comes from simply stopping the leak. When metrics live in silos, teams argue. When metrics connect, teams act. This is how modern DTC brands protect margin, improve cash flow and scale the right products at the right time.