Understanding Ecommerce Analytics Tools

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

AI won’t save your marketing. A clear use case and the right tool will. I see it all the time: Marketers using AI… with zero lift in performance. Why? Because “using AI” is not a strategy. You need to tie each tool to a clear use case. Here’s what’s actually worked for me 👇 ✅ ChatGPT + Claude AI – For rapid ad copy testing → Generate 10+ ad angles in different tones (funny, urgent, emotional) → Saves HOURS of brainstorming and testing ✅ Midjourney – For ad creatives that stop thumbs → Create lifestyle visuals and UGC-style images on demand → Helps me beat creative fatigue in 48 hrs, not 2 weeks ✅ AdCreative.ai – For platform-native ad creatives → Generates scroll-friendly formats for Meta and Google Ads → Fast + performance-aligned designs ✅ Windsor.io – For personalized video retargeting → AI-personalized videos based on user actions → Insane CTRs for abandoned cart sequences ✅ Madgicx.com – For AI-powered ad automation → Pauses low-performing ads and allocates budget smartly → Reduce manual tweaks by 60% ✅ Segment + Clearbit – For dynamic landing page personalization → Changes content based on data → Boosted lead gen CVR by 30% AI is just a toolbelt. Without a blueprint (aka use case), you’re just swinging hammers at air. Start with the bottleneck. Then pick the tool. What AI use case changed YOUR campaign results? Let me know if you want this turned into a carousel post or a swipe-style image! #PerformanceMarketing #AIinMarketing #AdTech #GrowthMarketing #MarketingTools

As someone leading marketing and growth for tech driven businesses, AI isn't just a buzzword... it’s become an essential part of my workflow. From planning performance campaigns to streamlining content creation, AI tools have drastically improved my speed, accuracy, and creativity. Here’s how I’m currently using AI across my daily routine 𝗖𝗮𝗺𝗽𝗮𝗶𝗴𝗻 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 & 𝗠𝗮𝗿𝗸𝗲𝘁 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 Tools like ChatGPT and Perplexity AI help me summarize market reports, extract insights from competitor ads, and validate campaign ideas. 𝘐𝘵’𝘴 𝘭𝘪𝘬𝘦 𝘩𝘢𝘷𝘪𝘯𝘨 𝘢 24𝘹7 𝘢𝘴𝘴𝘪𝘴𝘵𝘢𝘯𝘵 𝘧𝘰𝘳 𝘴𝘵𝘳𝘢𝘵𝘦𝘨𝘺 𝘴𝘶𝘱𝘱𝘰𝘳𝘵. 𝗖𝗼𝗻𝘁𝗲𝗻𝘁 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗣𝘂𝗿𝗽𝗼𝘀𝗲 For ad copy, email subject lines, and landing page variants, I often start with AI-generated drafts (using ChatGPT + Jasper). 𝘉𝘶𝘵 𝘐 𝘴𝘵𝘪𝘭𝘭 𝘣𝘦𝘭𝘪𝘦𝘷𝘦: 𝘈𝘐 𝘢𝘴𝘴𝘪𝘴𝘵𝘴, 𝘯𝘰𝘵 𝘳𝘦𝘱𝘭𝘢𝘤𝘦𝘴. 𝘛𝘩𝘦 𝘧𝘪𝘯𝘢𝘭 𝘷𝘰𝘪𝘤𝘦 𝘢𝘭𝘸𝘢𝘺𝘴 𝘢𝘭𝘪𝘨𝘯𝘴 𝘸𝘪𝘵𝘩 𝘣𝘳𝘢𝘯𝘥 𝘵𝘰𝘯𝘦 𝘢𝘯𝘥 𝘩𝘶𝘮𝘢𝘯 𝘪𝘯𝘴𝘪𝘨𝘩𝘵. 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 & 𝗔𝗱 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 We use Looker Studio + AI driven analytics to analyze campaign performance across Meta, Google & LinkedIn. 𝘛𝘩𝘪𝘴 𝘩𝘦𝘭𝘱𝘴 𝘶𝘴 𝘱𝘳𝘰𝘢𝘤𝘵𝘪𝘷𝘦𝘭𝘺 𝘵𝘸𝘦𝘢𝘬 𝘢𝘥 𝘴𝘱𝘦𝘯𝘥𝘴 𝘣𝘢𝘴𝘦𝘥 𝘰𝘯 𝘙𝘖𝘈𝘚 𝘢𝘯𝘥 𝘈/𝘉 𝘵𝘦𝘴𝘵 𝘳𝘦𝘴𝘶𝘭𝘵𝘴. 𝗦𝗘𝗢 & 𝗔𝗦𝗢 𝗘𝗻𝗵𝗮𝗻𝗰𝗲𝗺𝗲𝗻𝘁 Tools like SurferSEO and Writesonic help refine keyword strategies and generate optimized blog structures, improving search rankings across web and app stores. 𝗦𝗼𝗰𝗶𝗮𝗹 𝗟𝗶𝘀𝘁𝗲𝗻𝗶𝗻𝗴 & 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁 With AI-powered tools like Sprout Social, Inc. and Brandwatch, we monitor sentiment, spot trends early, and automate responses to FAQs, especially during high-traffic campaigns. 𝘈𝘤𝘤𝘰𝘳𝘥𝘪𝘯𝘨 𝘵𝘰 McKinsey & Company’𝘴 𝘭𝘢𝘵𝘦𝘴𝘵 𝘳𝘦𝘱𝘰𝘳𝘵, 𝘮𝘢𝘳𝘬𝘦𝘵𝘪𝘯𝘨 𝘪𝘴 𝘢𝘮𝘰𝘯𝘨 𝘵𝘩𝘦 𝘵𝘰𝘱 3 𝘣𝘶𝘴𝘪𝘯𝘦𝘴𝘴 𝘧𝘶𝘯𝘤𝘵𝘪𝘰𝘯𝘴 𝘴𝘦𝘦𝘪𝘯𝘨 𝘵𝘩𝘦 𝘩𝘪𝘨𝘩𝘦𝘴𝘵 𝘷𝘢𝘭𝘶𝘦 𝘧𝘳𝘰𝘮 𝘈𝘐 𝘪𝘯𝘵𝘦𝘨𝘳𝘢𝘵𝘪𝘰𝘯. Source: https://lnkd.in/gj8fXwqP AI won’t replace marketers... but marketers who use AI will outperform those who don’t. If you’re not yet using AI to support your workflow, start small. 𝘌𝘹𝘱𝘦𝘳𝘪𝘮𝘦𝘯𝘵. 𝘓𝘦𝘢𝘳𝘯. 𝘐𝘵𝘦𝘳𝘢𝘵𝘦. #MarketingStrategy #PerformanceMarketing #DigitalMarketing #AIAutomation #Leadership #MarTech #FutureOfWork 

Subscription fraud is often invisible - but its impact is significant. Fake free trials and recurring payment abuse rarely appear fraudulent at the start. They typically mimic legitimate user behavior, making detection challenging. Common fraud patterns in subscription businesses • Multiple accounts created by the same user • Use of temporary emails and shared or stolen cards • Abnormal usage during trial periods • Intentional chargebacks after extensive consumption Business impact • Revenue leakage • Increased chargeback ratios • Payment gateway penalties • Distorted growth and retention metrics • Higher customer acquisition costs How fraud is detected effectively • Device and IP intelligence • Behavioral signal analysis • Payment reuse and failure patterns • Usage anomalies during trials and renewals Prevention strategies that scale • Limit free trials per device and payment method • Apply step-up verification for high-risk users • Monitor usage prior to renewals • Block bots and high-risk IP ranges • Leverage AI models to identify evolving fraud patterns Outcomes of a strong fraud strategy • Reduced fake users • Lower chargebacks • Accurate business metrics • Protected recurring revenue • Improved trust with genuine customers Fraud prevention is not friction. It is a safeguard for legitimate users and sustainable growth.

🚨 BREAKING: Shopify just quietly upgraded Analytics in a big way. You can now use metafields as dimensions and filters in reports. Translation: Your custom business data finally lives inside Shopify Analytics. Until now, metafields were great for storefront logic but useless for real insights. So teams exported CSVs, built spreadsheets, or relied on external BI. Messy. Slow. Fragmented. Now you can: • Segment sales by material, ingredients, or custom attributes • Analyze performance by loyalty tier • Filter orders using your own business logic • Compare variants based on metafields All directly in Shopify. At first glance, this looks like a reporting feature. It’s not. It’s operational clarity. One subtle but powerful shift: Product, marketing, ops, and leadership now work from the same data model. Same dimensions. Same source of truth. Faster decisions. Classic Shopify move: Less fragmentation. More platform-native insight. If you care about data-driven commerce, enable “Use in Analytics” on your metafields today. #Shopify #Ecommerce #Analytics #ShopifyPlus #Operations #CX #ProductData

I’ve never seen a time like this in fraud prevention. The surface area is wild. You’re managing fake users, mule accounts, bots, friendly fraud, stolen cards, and abusive power users. Often all in the same day. But most teams still treat each attack as isolated. That’s the real risk. After working with dozens of operators this year, I can tell you that the teams making the biggest leap forward aren’t just reacting faster. They know their risk surface area cold. They’ve connected every attack type to specific signals. And they’ve aligned their toolset to those signals. Normally I tell you what to start doing. But today, here are 4 things I see slowing teams down because they don’t realize they’re bottlenecks: 1 - Focusing on fraud types, not attack paths It’s not about ATO vs refund fraud. It’s about how fraudsters move across your platform. Register → redeem offer → fake support ticket → initiate refund. That’s the pattern to map. 2 - Buying tools, not signals Most tools don’t solve problems. They expose signals. But if you don’t know what signals matter to your risk surface, your stack becomes noise instead of insight. 3 - Mapping signals to owners across the customer journey Fraud touches the whole platform Login, checkout, promos, support, payouts. But the signals that matter live everywhere. Product owns session data. Marketing owns attribution. Support owns refund flows. If you don’t map out who owns what, key signals get lost in the gaps. No single tool or team sees the full picture. 4 - Writing rules before understanding signals Most rule logic I see is reverse-engineered from what the tool can do - not what the fraudster did. Great strategy starts with the story, not the syntax. Why am I sharing this? Because my goal is to have you uncover relationships and risks you never knew existed. That is my goal. To help.

Big news (other than tariffs) this week - Amazon just unlocked 5 years of purchase history through a new AMC dataset. This extension of the lookback window from the current 13 months was announced last year but we first noticed it in the ad console this week. Nice find, Josh Helmer! It’s important to note this isn’t a blanket 5-year lookback across all datasets. It’s a single, new dataset, focused on order item events (product-level purchase data, per customer). It’s not yet available for audience building, though Amazon has signaled that functionality is coming. Still, this is sweet to see. The data comes via this new AMC dataset: Amazon Retail Purchases, which is part of AMC’s Paid Features portfolio. I think the Paid Features side of AMC is usually around $500. Here are some reasons why to use this new dataset: - Better-informed Customer Lifetime Value models - More accurate New-to-Brand measurement - Deeper understanding of lapsed shoppers and seasonal buying behavior - Identification of gateway products over time For durable goods and seasonal products especially, this is a big update. A 13-month window has always been a bottleneck in understanding longer purchase cycles. Five years gives us a much clearer picture of how customers engage with a brand over time and how advertising can influence those journeys.

Why Use Campaign Manager 360 When DV360 Already Tracks Performance? In programmatic conversations, one question often pops up: “DV360 already tracks impressions, clicks, and conversions, so why do we still need CM360 to track them?” It’s a fair question. But the answer lies in what CM360 does differently and better. Even in DV360-only campaigns, Campaign Manager 360 (CM360) plays a critical role in delivering accurate, cross-platform insights that DV360 alone cannot provide. 💡 CM360’s Key Strengths 🔹 Floodlight Tags CM360 uses Floodlight tags to capture a broader range of post-ad-serving metrics, like viewability, clicks, and conversions, across platforms. This enables deeper insights into user behavior. 🔹 Centralized Campaign Management CM360 acts as a command center for your digital campaigns. It lets you manage and track ads across Google Ads, TTD, and more, not just DV360. This unified view simplifies reporting and ensures you're not working in silos. 🔹 Attribution Flexibility CM360 supports advanced attribution models, allowing marketers to choose how conversions are counted and attributed. This flexibility is key to understanding true channel impact. 🔹 Greater Reporting Accuracy CM360 offers conversion deduplication across platforms, which makes the reporting more accurate. 🔹Comprehensive Measurement Backbone CM360 is often considered the primary source of truth for campaign measurement because of its comprehensive reporting capabilities, which make it the go-to platform for accurate, actionable insights. ✅ Bottom Line - DV360 is excellent for managing and optimizing programmatic campaigns within its own environment. - CM360 delivers a complete, granular picture of campaign performance across platforms and environments. In your experience, where does CM360 add the most value beyond DV360’s capabilities?