Understanding Ecommerce Analytics Tools

🚨Breaking🚨 Amazon just turned Rufus into an agent. Every AI shopping assistant waits for you to ask. Amazon just stopped waiting. Amazon rolled out 'Scheduled Actions' in Rufus last week. It offers a handful of suggested use cases to get shoppers started, and the one I clicked on for the video below was "Set a birthday reminder with gift ideas." Sounds boring, but it's actually quite a bit shift in capability. Gift discovery is one of the biggest use cases driving people to ChatGPT and other general-purpose LLMs for shopping help. "What should I get my brother-in-law who likes cycling and hates gadgets" is exactly the kind of query Amazon can't afford to lose to OpenAI. Scheduled Actions does two jobs at once: it pushes Amazon further into agentic commerce, and it builds a moat around many of retail's most lucrative, most AI-native categories. Scheduled Actions lets Rufus act on a schedule instead of only responding to live queries. Amazon's other starter suggestions include monthly coffee recommendations, new book alerts, and automated cleaning supply restocks. But the suggested prompts aren't the story. They're training wheels. The real shift is that shoppers can define their own. 🗣️ Remind me to buy running shoes every 18 months. 🗣️ Alert me when the price on [specific product] drops below $X. 🗣️ Send me a new hot sauce every fortnight. 🗣️ Remind me about pet food before it runs out, based on the pack size. 🗣️ Suggest new swimwear before the kids outgrow them. Amazon just handed shoppers a programmable layer over their own commerce life. Why the birthday example is an intersting one: 🛒 Gift-giving is one of the highest-intent, most brand-agnostic queries in commerce. "Gift ideas for mum" doesn't care whose brand ranks, it cares about fit. 🛒 Birthdays are recurring and predictable. Amazon now has a calendar of future purchase intent, not a guess. 🛒 It captures relationship data Amazon didn't own before. Who matters to you, their age, what you bought last year, what they actually liked. 🛒 It moves discovery off Google and ChatGPT and into Amazon's own interface, where the shelf is stocked and checkout is one tap. For brands, this rewrites the brief. If your product isn't in Amazon's logic for specific occasions, usage patterns, replenishment cycles, and relationships, you don't get scheduled. Structured attributes stop being a hygiene factor and become the entire shelf. For retailers, the warning is sharper. Every scheduled action Amazon captures is a future purchase locked in before anyone else gets to pitch. No ad auction. No comparison tab. No competing retailer even in the room - it's definitely time to begin exploring your own conversational assistant. The interesting question isn't which use cases Amazon suggests💡 It's what shoppers will invent once they realise Rufus can just do it for them 🤔

🚨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.

Crowning a New Term: “Iceberg Metrics” 🧊 ✨ I’m calling it: Iceberg Metrics represent KPIs that only reveal the tip of what’s really happening below the surface. Metrics like abandoned carts seem simple but often mask much more—checkout friction, hidden costs, trust issues, and more. To truly understand and optimize, we need to dig deeper. Here’s how to dive into the “iceberg” of abandoned cart rates: 1. Establish Baseline Metrics: Start by gathering data on current abandoned cart rates, session times, and bounce rates using heat maps and session recordings to see where users drop off. 2. Segment the Audience: Analyze users by behavior (first-time vs. repeat visitors, mobile vs. desktop) and traffic source (organic, paid, email). 3. Experiment Hypotheses: Develop hypotheses for abandonment reasons—shipping costs, checkout friction, distractions, or lack of trust signals—and test them. 4. Run A/B Tests: Test variations like simplifying the checkout process, showing shipping costs earlier, adding trust badges, or retargeting abandoned cart emails. 5. Use Heat Maps & Session Recordings: Examine user behavior in real time. Look for confusion or hesitation, where users hover, and whether they engage with key information. 6. Contextualize Results: Analyze how changes impact overall user flow. Did simplifying checkout help, or did other metrics like bounce rate increase? 7. Ecosystem Approach: Examine how tweaks affect the full journey—from product discovery to checkout—balancing short-term improvements with long-term goals like lifetime value. 8. Iterate: Refine solutions based on experiment findings and continuously optimize the customer journey. This one’s mine, folks! #IcebergMetrics #OwnIt #DataDriven #EcommerceOptimization #NewMetricAlert Cheers, Your cross-legged CAC and CLV buddy 🤗

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.

A client came to us frustrated. They had thousands of website visitors per day, yet their sales were flat. No matter how much they spent on ads or SEO, the revenue just wasn’t growing. The problem? Traffic isn’t the goal - conversions are. After diving into their analytics, we found several hidden conversion killers: A complicated checkout process – Too many steps and unnecessary fields were causing visitors to abandon their carts. Lack of trust signals – Customer reviews missing on cart page, unclear shipping and return policies, and missing security badges made potential buyers hesitate. Slow site speeds – A few-second delay was enough to make mobile users bounce before even seeing a product page. Weak calls to action – Generic "Buy Now" buttons weren’t compelling enough to drive action. Instead of just driving more traffic, we optimized their Conversion Rate Optimization (CRO) strategy: ✔ Simplified the checkout process - fewer clicks, faster transactions. ✔ Improved customer testimonials and trust badges for credibility. ✔ Improved page load speeds, cutting bounce rates by 30%. ✔ Revamped CTAs with urgency and clear value propositions. The result? A 28% increase in sales - without spending a dollar more on traffic. More visitors don’t mean more revenue. Better user experience and conversion-focused strategies do. Does your ecommerce site have a traffic problem - or a conversion problem? #EcommerceGrowth #CRO #DigitalMarketing #ConversionOptimization #WebsiteOptimization #AbsoluteWeb

🌐 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

Traffic you cannot explain is budget you cannot justify. After 10 years in marketing measurement, the most common thing I see is attribution reports that look clean but 60%+ of the data is not actionable. My latest article is about “direct” and “organic” as channels, and the data they’re usually hiding. 1️⃣ Direct is not always someone typing your URL. It can be a tracking failure: UTMs stripped by redirects, in-app browsers breaking referrer data, iOS privacy changes. 2️⃣ And when direct IS tracked correctly? It's usually recall, not discovery. Someone saw your brand somewhere, remembered it, and typed your URL. The touchpoint that matters is the one that's invisible in your report. 3️⃣ Organic search looks like an initiative, but brand search lives inside that number. From my experience, most companies' organic search conversions are 70%+ people searching for their own brand name. Again: recall, not discovery. It flatters your SEO and hides whatever channel actually created the awareness. 4️⃣ When direct and organic dominate your report, you're not looking at a clean model. You're looking at where your tracking gaps ended up. In the article, I cover why the above happens, and how you can use different data sources to uncover the true meaning of your organic + direct conversions. My rule of thumb for clients: direct above 10% of attributed conversions means you have recovery work to do. Read the full article here: https://lnkd.in/ec4SAt7D As usual, drop thoughts and questions as comments!

I've worked with more than 750+ eComm brands on their data connection between Shopify and Meta/Facebook. There's tons of problems I've found, but these are the top 5 data & tracking issues brands have (and don't even realize). ❌ Landing Pages drop tracking code - there's a ton of excellent third-party landing page platforms out there. Most people don't realize that they drop tracking code and lead to data gaps. (You need custom code that properly passes tracking code from the Landing page to the Shopify Checkout) ❌ Click data missing from Checkout - lots of customers need multiple web sessions to go from ad click to product purchases. Most people don't realize this leads to dropped click data and purchases that look like direct traffic (but should be attributed to ad clicks). (You need code that matches sessions and stitches the data together to ensure click data is included with all Purchase events, when available) ❌ Over-counting from non-web orders - A lot of brands have Shop orders, subscription renewals, and offline/draft orders get processed through the Shopify checkout. A basic CAPI connection will send Purchase events for these orders, which leads to misattribution and over-counting. (You need code that is smart enough to see the order source and re-route non-web orders to separate events) ❌ Light payloads with low EMQ - Most brands and most developers don't realize just how much data you can send in any given payload. If your data payloads are missing external id, FBP, and phone info, it leads to low EMQ scores and limits the performance of your ads. (You need an advanced CAPI connection that sends the upper limit of all data, ensuring maximum data coverage) ❌ Data volume too low - Many brands fail to hit the minimum volume of 50 conversions per ad set per week. Under this threshold, Meta simply isn't getting enough data to exit the learning phase and will optimize to clicks instead of conversions. (You need to either increase spend or consolidate your campaigns to ensure you have 50+ weekly conversions) --- If you're using the free/native Shopify CAPI connection, you likely have 3 or more of these issues. Even brands using paid CAPI solutions usually have 1 or more of these issues. If you need help assessing and/or fixing your data and tracking setup, comment below or shoot me a DM.