Understanding Ecommerce Analytics Tools

Over the last few weeks, I’ve spoken with 20+ eCommerce and DTC brands about their attribution and LTV challenges. Four consistent themes emerged: 1. Data is too fragmented: Ad metrics live in Facebook or Google, purchases in Shopify, retention in Klaviyo, and nobody’s seeing the full customer journey in one place. 2. iOS 14+ made short-term metrics riskier: With less granular data, many marketers over-index on quick ROAS or CPA wins, ignoring high-LTV segments. 3. Brands want to optimize for retention: Rising CAC and declining LTV is pushing teams to chase repeat buyers instead of just one-and-done conversions. When a brands mention the first three, my advice is bring these insights together by consolidating your data and forecasting beyond the first purchase. And that brings up challenge number 4. 4. Organizational Gaps in Data Expertise: Many mid-market DTC brands have small or overstretched data teams in addition to lacking the technology and analytics expertise. The Takeaway: If you can’t see which channels produce loyal, profitable customers, you can’t truly scale. Based on my numbers that would indicate about 80% of ecommerce DTC Brands are still in the dark. The long term winners will be the 20% that have accurate attribution and real-time data. #DTCgrowth #MarketingAttribution #CustomerLifetimeValue #PredictiveAnalytics #RetentionMarketing #eCommerceStrategy

𝗧𝗟;𝗗𝗥: Amazon Review Highlights shows how an Amazon Web Services (AWS) SageMaker powered offline batch AI can process 𝗯𝗶𝗹𝗹𝗶𝗼𝗻𝘀 𝗼𝗳 𝗿𝗲𝘃𝗶𝗲𝘄𝘀 across 𝟮𝟮 𝗺𝗮𝗿𝗸𝗲𝘁𝗽𝗹𝗮𝗰𝗲𝘀 𝗶𝗻 𝟮𝟴 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀 at remarkably low costs by 𝗯𝗹𝗲𝗻𝗱𝗶𝗻𝗴 𝘁𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗠𝗟 with 𝘀𝗲𝗹𝗲𝗰𝘁𝗶𝘃𝗲 𝗟𝗟𝗠 use. 𝘐𝘯 𝘢 𝘱𝘰𝘴𝘵 𝘺𝘦𝘴𝘵𝘦𝘳𝘥𝘢𝘺 𝘐 𝘨𝘢𝘷𝘦 𝘢𝘯 𝘰𝘷𝘦𝘳𝘷𝘪𝘦𝘸 𝘰𝘧 𝘈𝘮𝘢𝘻𝘰𝘯 𝘙𝘶𝘧𝘶𝘴 (𝘩𝘵𝘵𝘱𝘴://𝘣𝘪𝘵.𝘭𝘺/4𝘭𝘈𝘩𝘯8𝘕). Review Highlights creates 𝗔𝗰𝗰𝘂𝗿𝗮𝘁𝗲 and 𝗗𝗲𝗹𝗶𝗴𝗵𝘁𝗳𝘂𝗹 product reviews in an 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 and 𝗖𝗼𝘀𝘁-𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 manner. It does that by enhancing 6.5 billion customer reviews with pre-computing summaries using aspect extraction and sentiment analysis, making them instantly available when customers view products. 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀:  1. 𝗛𝘆𝗯𝗿𝗶𝗱 𝗧𝗿𝗮𝗱𝗶𝘁𝗼𝗻𝗮𝗹 𝗠𝗟 & 𝗟𝗟𝗠 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: Traditional NLP first clusters reviews and extracts aspects, allowing smaller LLMs to handle summarization.  2. 𝗠𝘂𝗹𝘁𝗶-𝗦𝘁𝗮𝗴𝗲 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲: Semantic clustering → aspect extraction → sentiment analysis → final LLM summarization.  3. 𝗦𝗮𝗴𝗲𝗠𝗮𝗸𝗲𝗿 𝗕𝗮𝘁𝗰𝗵 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺: All powered by SageMaker's capabilities enable processing tens of thousands of reviews per second asynchronously.  4. 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗥𝗲𝗰𝗼𝗺𝗽𝘂𝘁𝗮𝘁𝗶𝗼𝗻: Statistical triggers determine when enough new reviews warrant regenerating summaries.  5. 𝗛𝘂𝗺𝗮𝗻-𝗶𝗻-𝘁𝗵𝗲-𝗟𝗼𝗼𝗽: Annotators evaluate information density, fluency, and accuracy across all languages.  6. 𝗜𝗻𝗳𝟮 𝗛𝗮𝗿𝗱𝘄𝗮𝗿𝗲: Batch processing enables dense packing of inference operations on AWS chips, with 40% better price-performance.  7. 𝗩𝗲𝗻𝗱𝗮𝗯𝗹𝗲 𝗔𝗿𝘁𝗶𝗳𝗮𝗰𝘁𝘀: The pipeline produces structured data beyond visible summaries, including aspect taxonomies and sentiment scores used by systems from search to advertising.  8. 𝗖𝗮𝗻𝗼𝗻𝗶𝗰𝗮𝗹 𝗗𝗮𝘁𝗮: Standardized format maintains consistency across all marketplaces and languages for universal consumption.  9. 𝗖𝗼𝘀𝘁 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆: By using LLMs selectively only where they add unique value, Amazon processes billions of reviews at a fraction of what a pure LLM approach would cost. Watch this to get deeper on the tech details: https://bit.ly/4418TBd (Burak Gozluklu, Vaughn Schermerhorn)

A fake review gets posted. Within 24 hours, 100 people click "helpful." Amazon sees engagement. Assumes it's legitimate. Makes it harder to remove. This is the next level of review attacks. They're not just posting fake critical reviews anymore. They're gaming the social proof that makes Amazon think the review matters. Here's what actually happens: Competitor posts a violation-heavy review (mentions your price, cusses, talks about FBA shipping issues). Then they pay a click farm to mark it helpful 50-100 times. Amazon's system flags it: "High engagement. Users find this valuable." Now when we file to remove it, there's friction. Amazon pushes back because the data says people care about this review. Even though the review clearly violates guidelines. Even though the "helpful" clicks are just as fake as the review itself. The workaround? Get to reviews before anyone can mark them helpful. This is why monitoring daily matters. A fresh violation with zero engagement? We can file immediately and get it down fast. That same review after 100 "helpful" clicks? Still removable, but it takes longer because we're fighting manufactured social proof. If you're doing weekly review checks, you're already behind. The attacks happen fast. The manipulation happens faster. By the time you notice, the damage is baked into Amazon's perception of that review's value. Daily monitoring is the only way to catch this before the helpful buttons get weaponized. How often are you checking your reviews? Weekly? Daily? Never?

😱 “Why is Meta and Google Ads reporting 200 conversions when we only had 100 sales?!” I had a mini heart attack the day I saw this happen. No, we didn’t magically double our sales overnight – our tracking was 𝗹𝘆𝗶𝗻𝗴 𝘁𝗼 𝘂𝘀. It turned out the 𝗙𝗮𝗰𝗲𝗯𝗼𝗼𝗸 𝗽𝗶𝘅𝗲𝗹 & 𝗚𝗼𝗼𝗴𝗹𝗲 𝗔𝗱𝘀 𝗣𝗶𝘅𝗲𝗹𝘀 𝘄𝗲𝗿𝗲 𝗳𝗶𝗿𝗶𝗻𝗴 𝘁𝘄𝗶𝗰𝗲 on our site, double-counting every conversion. This sneaky issue is more common than you might think. If you’ve ever mixed Google Tag Manager, native Shopify/WordPress integrations, or added multiple analytics tools, you might be unknowingly 𝗱𝗼𝘂𝗯𝗹𝗲-𝘁𝗿𝗮𝗰𝗸𝗶𝗻𝗴 your users. Why is that a 𝗯𝗶𝗴 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 for marketers? - 📊 𝗠𝗶𝘀𝗹𝗲𝗮𝗱𝗶𝗻𝗴 𝗱𝗮𝘁𝗮: You’re seeing inflated conversion numbers that aren’t real. - 💸 𝗪𝗮𝘀𝘁𝗲𝗱 𝗯𝘂𝗱𝗴𝗲𝘁: Your ads might be optimizing for ghost conversions, and you could be spending money based on fake data. - 🤷♂️ 𝗕𝗮𝗱 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀: It’s hard to trust your A/B tests or funnel metrics when everything is off balance. I ran into this headache recently and decided to 𝗱𝗶𝗴 𝗱𝗲𝗲𝗽 𝗳𝗼𝗿 𝗮 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻. After some detective work, I discovered 5 different causes for duplicate tracking (from a forgotten second GTM container 🤦♂️ to a well-intentioned Shopify app that over-reported events). Each cause needed its own fix. The good news? I recorded a quick video walking through all 𝟱 𝗳𝗶𝘅𝗲𝘀 𝘀𝘁𝗲𝗽-𝗯𝘆-𝘀𝘁𝗲𝗽. It’s basically a checklist to 𝗰𝗹𝗲𝗮𝗻 𝘂𝗽 𝘆𝗼𝘂𝗿 𝗽𝗶𝘅𝗲𝗹 𝘁𝗿𝗮𝗰𝗸𝗶𝗻𝗴 so you can trust your data again. 𝗪𝗮𝗻𝘁 𝘁𝗵𝗲 𝗹𝗶𝗻𝗸 𝘁𝗼 𝘁𝗵𝗲 𝘃𝗶𝗱𝗲𝗼? Drop a “𝗙𝗶𝘅 𝗶𝘁” in the comments, and I’ll DM you the full rundown. No more phantom conversions, let’s get your analytics back on track! 🔧📈 #AnalyticsWithAhmed #GTM #GA4 #MarTech #GoogleAnalytics #MarketingAnalytics #Facebook #Meta #GoogleAds #WebAnalyticsWithAhmed

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.

Your Ad Spend 30 Days Ago Probably has MORE Impact on Today's Sales, than Today's Spend. What most Premium/Luxury brands forget... People can take weeks, months or years to purchase from you— So how can you judge a new ads performance so quickly? You're killing profitable campaigns before they even have a chance to work. The culprit? Attribution lag and the Ad Stock Effect. 𝐀𝐝 𝐒𝐭𝐨𝐜𝐤 𝐄𝐟𝐟𝐞𝐜𝐭: The lingering impact of advertising that builds cumulatively over time before triggering a purchase. Real industry data reveals: • 97% of retail conversions happen within 10 days of first ad exposure • B2B buyers need an average of 31 touchpoints across 6-12 month cycles • High-ticket items ($500+) require 30+ day attribution windows • Cart abandonment averages 71.3% across all categories - but many of these "lost" sales convert later Most founders judge campaign success on daily metrics, missing the full revenue picture. 𝐓𝐡𝐞 𝐫𝐞𝐚𝐥 𝐒𝐡𝐨𝐩𝐢𝐟𝐲 𝐱 𝐌𝐞𝐭𝐚 𝐫𝐞𝐥𝐚𝐭𝐢𝐨𝐧𝐬𝐡𝐢𝐩: 1️⃣ Today's ad spend often drives sales that show up days later 2️⃣ Post-iOS 14.5, Meta lost 93.5% of its attribution accuracy - only 6.5% of baseline tracking remains 3️⃣ If someone clicks Monday and buys Thursday, Thursday's sales partly came from Monday's spend 4️⃣ Single-day ROAS metrics miss this natural delay in the customer journey 𝐇𝐨𝐰 𝐭𝐨 𝐦𝐞𝐚𝐬𝐮𝐫𝐞 𝐀𝐝 𝐒𝐭𝐨𝐜𝐤 𝐟𝐨𝐫 𝐲𝐨𝐮𝐫 𝐬𝐭𝐨𝐫𝐞: → Attribution Analysis: • Compare 1-day vs. 7-day vs. 28-day attribution windows to see your delay • Meta's Andromeda update (launched Dec 2024) improved Advantage+ ROAS by 22% through better AI matching → Shopify Tools: • Check "Time to Purchase" in your analytics dashboard • Use GA4's "Time Lag" report to visualize your purchase delay distribution → Framework: • Product category matters: Personal care (6.8% conversion rate) vs. home decor (1.4%) • Price point impact: Under $50 = 1-day windows, $500+ = 30+ day windows 𝐖𝐡𝐚𝐭 𝐭𝐡𝐢𝐬 𝐦𝐞𝐚𝐧𝐬 𝐟𝐨𝐫 𝐲𝐨𝐮𝐫 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧-𝐦𝐚𝐤𝐢𝐧𝐠: ✅ Stop using daily 1DC ROAS as your north star metric ✅ Evaluate campaigns using 7-day minimum windows ✅ Implement MER/CMERs (Marketing Efficiency Ratios) ✅ Use server-side and 1st party tracking via Meta's Conversions API/Elevar ✅ Remember: Mobile users browse (77.2% abandonment) but often buy on desktop later The bottom line: What looks like a "failing campaign" on day 1-3 might be your highest-performing revenue driver when measured properly over 14-30 days. Are you making decisions on incomplete data and killing campaigns right when they're about to deliver results? P.S. The chart shows an example ad stock curve, every brand's timing is unique based on product price, category, and customer behavior. Track YOUR specific patterns, don't rely on industry averages.

At 7-figure monthly spend, the difference between profitable and chaotic isn’t just about how good your ads are. It’s how well your system measures what’s working. Here’s the 3-layer tracking system we use across high-spend Shopify accounts to keep performance real and actionable 👇 1️⃣ Platform Layer (Meta / Google / TikTok) Purpose: surface signals. You still need to know what’s driving conversions, but attribution is directional, not truth. → Optimize to in-platform learnings. → Don’t make P&L decisions from Ads Manager. 2️⃣ Source of Truth Layer (Shopify / backend analytics) Purpose: validate revenue and CAC. We match spend to actual customer data, not just pixel fires. → Track new vs returning customer mix. → Align to blended MER and CAC. → Layer in cohort LTV for long-term clarity. 3️⃣ Insight Layer (Automation + AI) Purpose: translate the data into action. We automate a daily Slack digest that shows: • Top and bottom ads by ROAS and CPA • Spend by campaign • Trendline vs last 7 days • Quick AI summary on why performance changed This gives the entire team the same view every morning. No more arguments over attribution or what’s working. The truth: Most 7-figure brands lose efficiency because their data lives in silos. - Creative doesn’t see what finance sees. - Media buyers don’t see what retention sees. When you connect all three layers, your decisions stop being reactive and your scaling becomes predictable.

Amazon just gave brands the clearest signal yet about what matters next. On the Q3 earnings call last week , Andy Jassy said: “Rufus, our AI-powered shopping assistant, has had 250 million active customers this year… Customers using Rufus are 60% more likely to complete a purchase. Rufus is on track to deliver over $10 billion in incremental annualized sales.” That’s $10 billion in AI-driven shopping behavior happening inside Amazon. As a brand, how do you get “Rufus-ready” — optimizing for conversational discovery, natural-language content, and AI-era buying behavior. Here’s the 5-step to-do list you can start acting on right now 👇: ✅ 1. Audit PDPs for conversational discovery Make sure your listings answer shopper-style questions like “Which is best for me?” and “Does it work for ___?” ✅ 2. Scale authentic UGC & reviews Feed Rufus what it loves — real human voice, use-case content, and side-by-side comparisons. ✅ 3. Leverage natural-language private feedback Use real consumer comments (not marketing copy) to rewrite PDPs in their words. Rufus — and shoppers — reward authenticity. ✅ 4. Map the intent-chain & pre-media momentum Build content for every stage (research → compare → decide) and seed early before ads run. ✅ 5. Engineer “trigger phrases” into content Weave shopper-style questions (“Is this better than…?”) into reviews and FAQs — it’s the new AI SEO. 💡 Bonus: Track new AI KPIs — how often your content mirrors real shopper language, and how conversion changes when it does. Brands that adapt to Rufus now will have an early edge the next era of eCommerce. If you’d like the full report — including templates for the AI-Discovery Scorecard, Intent-Chain Grid, and Natural-Language Feedback Framework — drop a note in the comments or DM me “Andy Jassy Rufus Report.”

Let’s say, you're scaling your Shopify store, running ads across Meta, Google, TikTok. Sales are coming in.   Everything looks good - until you check the numbers. 👉 Meta says you made $50K. Google says $40K. Shopify says $30K.  Who’s telling the truth? 👉 Your TikTok ads show “low ROAS,” so you turn them off. But sales dropped because those ads were fueling your funnel. 👉 Your "best-performing" Meta ad keeps getting more budget. But it’s just retargeting people who were going to buy anyway. 👉 GA4 shows a major conversion drop. You panic, but it’s just privacy rules blocking data. The problem isn’t your ads, your creatives, or your strategies it’s your tracking. Ad platforms don’t just compete for your ad dollars, they compete for attribution credit. And that’s why your reports don’t add up. 📌 Meta, Google, and TikTok all claim the same conversion. 📌 Shopify’s reports focus on last-click, missing the bigger picture. 📌 GA4 filters out data due to privacy rules, iOS restrictions, and ad blockers. 📌 Retargeting campaigns inflate ROAS by taking credit for existing buyers. 📌 TikTok and YouTube assist sales, but last-click ROAS hides their impact. The result? You're scaling the wrong ads, cutting the right ones, and misallocating budget. So, how to fix it (before you burn more budget) ✅ Unify your tracking → One source of truth across Shopify, Meta, Google, TikTok. ✅ Set up server-side tracking → Recover lost conversions with Meta CAPI, Google Enhanced Conversions, TikTok Events API. ✅ Stop relying on ROAS alone → Look at blended CAC, profitability, and customer LTV. ✅ Run incrementality tests → Before cutting a channel, check if it’s driving assisted conversions. In 2025, brands that scale profitably won’t just run great ads, they’ll track them better. ___________· ⋅˚₊‧ 🎀 ‧₊˚ ⋅ ·____________ 🌷 Hi, I am Chloé Nguyen from NestScale 🌷 🔔 Follow me for more insightful posts about #ecommerce #shopify #advertising #cookielessfuture #multipletouchattribution #customerjourney #googleads #facebookads #tiktokads #klaviyoemailmarketing #omnichannelmarketing #nestads #nestscale ♻️ Repost if you find it insightful