Understanding Ecommerce Analytics Tools

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

You don’t need more dashboards. You need a proper routine health check. Most e-commerce teams are already overwhelmed by numbers. Revenue, traffic, ROAS, CR… dashboards show them all – but rarely show what actually matters. A real 🩺 health check is different. It looks at the whole system: – Are profits growing with revenue? – Are repeat customers buying faster or slower? – Is CAC payback still acceptable? – Which “stable” metrics are hiding dangerous shifts? We built a one-page checklist that turns this into a routine: + Core metrics you must review + Driver breakdowns for CR, AOV, LTV, Margin + Segmentation lenses to catch blind spots + Red flag indicators when numbers contradict each other + A simple framework to turn anomalies into testable hypotheses Run it monthly. Run it after every major campaign. And you’ll catch the leaks before they turn into losses. Save this and share with your team.

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.

Subscription services need strong analytics to build smarter & strategically strong plans. 🚀 Subscription models aren’t just a trend anymore—they’re shaping the future of eCommerce. 🛍 But are you leveraging data & analytics sufficiently, to iteratively build your strategy, & have your customers coming back? Here’s why you should make data analytics an integral part of your business approach: 🎯 Customer Retention Isn’t a Guessing Game Many eCommerce businesses still rely on gut feeling & high level market trends when deciding what keeps their subscribers happy. What if you could make smarter, data-driven decisions instead? Here’s how: 1️⃣ Understand User Behavior at a Granular Level Accurate analytics helps you spot patterns in how your subscribers behave. 👉 For example, a fitness app found that users who completed daily workouts stayed subscribed longer. With this insight, the app focused on features that encourage consistent engagement, boosting retention. 2️⃣ Personalize the Experience Analytics isn’t just about numbers—it’s about the people behind them. By segmenting your customers based on their behavior & psychographics, you can create personalized experiences that drive loyalty. 👉 Example: Netflix tailors its show and movie recommendations at a segment of one level, making subscribers feel seen and valued, while also making their life easier! 3️⃣ Track Key Metrics Keep an eye on crucial metrics such as Churn Rate, Average Order Value (AOV), & Customer Lifetime Value (CLTV). These metrics tell you what’s working, & where you need to pivot. 👉 For instance, a music app discovered that users who created personalized playlists were less likely to churn. Now they focus on promoting playlist creation to keep users engaged. 4️⃣ Leverage Predictive Analytics Want to predict churn before it happens? Predictive analytics can highlight warning signs of disengagement so you can take action before your subscribers leave. 👉 Takeaway: With predictive analytics you can send personalized reminders, special incentives, or tips to at-risk users, keeping them engaged. 5️⃣ Test, Learn, Optimize Don’t settle for your first plan. A/B testing helps you experiment with different subscription models, pricing, & features to arrive at the best. 👉 Example: A video streaming service can test different pricing structures & tiers, & find the best pricing plans that maximize sign-ups, market share, & retention. Bottom line: Subscription analytics give you the insights you need to understand, retain, & grow your subscriber base. Embracing smart data, & analyzing it while keeping the people behind it in your mind can create more personalized, engaging, & profitable subscription model. At Appstle Inc. there are 30,000+ eCommerce businesses that hands-on use our granular analytics to make impactful data driven customer retention strategies. The analytics are an integral part of Appstle Subscriptions. Because there is no better way to profitably scale!

AI is BS. Not the technology. The talk track. I attended the NRF Foundation Big Show this week, and everything was “AI-something.” AI for inventory. AI for pricing. AI for customer service. AI for world peace (okay, maybe not yet). With all that noise, it’s easy to feel overwhelmed—and a bit cynical. The possibilities are incredible, but slapping “AI” on everything doesn’t make it useful. Understanding how these tools work to solve actual business problems is critical. I’ve found it’s helpful to kind of simplify it into the two categories that really matter: ✍️ Generative AI is like an extremely knowledgeable friend who can produce new things—written content, images, & beyond—if asked in just the right way. A chatbot interface makes that generative AI friend more accessible: you give it a prompt (for example, “Write a short product description for a new running shoe”), and it instantly creates a response from all the information it has internalized. 🕵️♀️ Agentic AI goes further. It's more like a proactive personal assistant with the same deep knowledge. Instead of waiting on precise prompts, it can infer tasks and even carry them out automatically. For example, it can figure out when stock is running low & reorder items without being explicitly told every step to take. How retailers might use each: 1️⃣ Generative AI: Product Descriptions: Automatically create rich, engaging product descriptions for online catalogs that match the brand’s voice. Marketing Content: Draft email campaigns, social media copy, & blog posts. Store Layouts & Visuals: Suggest store display ideas or mockups, using AI-generated images to spark new merchandising concepts. 2️⃣ Agentic AI: Inventory Management: Monitor incoming sales data & reorder items proactively before inventory runs out. Customer Service Automation: Act on customer requests (like returns or shipping updates) without a staff member walking it through each step. Dynamic Pricing: Continuously check market trends, competitor prices, and demand patterns, then adjust product prices accordingly—without needing a person to oversee it all. I think Agentic AI will provide the biggest benefits and the biggest disruptions because consumers love convenience and businesses love efficiency – and it delivers both. AI is evolving faster than Moore’s Law—doubling every 3 months instead of 18. Do the math—it’s mind-blowing. Moore’s Law gets you 10X improvement in 5 years. At this pace, AI could be 1,000,000X in 5 years! (h/t Kasey Lobaugh) In just a few years, we could see retail transformed by super-powered sales associates, hyper-personalized shopping journeys, and supply chains optimized to unimaginable levels. But first we have to cut through the noise to make sure we’re making the right choices. Are you experimenting with any tools successfully—or are you overwhelmed by the hype (or both!)? #AI #agenticAI #agents #retail #NRF

If you make large marketing strategic and budget decisions based on one attribution view, you will miss or cut critical work contributing to revenue. Attribution models are meant to be guides, not deciding factors. Yet, we see them as the end-all-be-all and the answer to our questions to decide. The marketing (and GTM) world has long debated the right attribution model. First touch. Last touch. Conversion touch. Multi-touch. Time decay. W-shaped. Custom weighted. (The list goes on.) None of these will be the right model by themselves, nor will they ever give you 100% of the right answer. ✨The magic happens when you use multiple, depending on the answer you’re looking to solve. ✨✨And the even bigger performance magic happens when you *consult* the data, with multiple views, to make an educated decision instead of trying to be “data-driven” off singular views. Disclaimer: I’m NOT saying attribution is bad; you shouldn’t use it, or you shouldn’t be making data-informed decisions. The power of marketing comes from the fundamentals: reaching the right audience with the right message and creating a memorable experience so the audience purchases (hopefully again and again). Some interactions will never be seen in tracking, regardless of all the sophisticated martech software. And these interactions need to happen frequently before most marketing data points even show up. I know I’m preaching to the choir here, so what’s the solution? 1. Change your mindset to outline data discovery questions vs. finding one view 2. Use the right attribution model to answer the question you’re looking to solve. ● What brings our audience to us for the first time? ⋆ First touch ● How does our audience remember us for the first time? ⋆ Self-reported/sales self-reported/surveys ● What trackable actions happen in the buying journey? ⋆ Multi-touch (etc.) 3. Be able to compile all these different data points, analyze them, and make *educated* decisions based on them. 4. Have program-level performance tracking 5. Know when to look at performance in a micro and macro view. 6. Be able to answer the CEO/board questions of ● How much revenue do we generate for every $1 marketing spend? ● How efficient are we? ● If we spend X, what can we expect? ● How much marketing fuel do we need to hit our growth goal of X? ● How is marketing doing? ● What’s working? What should we do more of? ● What’s not working? What should we do less of? ● What are we learning from marketing for the overall business? ● How can we help? 7. Be able to coach and educate your team on how you grade performance, analyze the data, view attribution, and how you make your strategic/budget decisions. 8. Accept it will never be 100% a mathematical equation, and marketing is equal parts science and math - this type of analysis takes business acumen, marketing knowledge, and experience. At the end of the day, attribution models are all about gathering insights.

Ever launched a product or feature, only to see users drop off without knowing why? You check the analytics - traffic looks fine, but engagement is slipping. Where are users struggling? Why do some breeze through while others get stuck? Traditional metrics like bounce rates and session counts barely scratch the surface. This is where session analysis becomes a game-changer. It moves beyond surface-level metrics to uncover hidden behavioral patterns - why users hesitate, get frustrated, or abandon tasks entirely. One of the biggest challenges in UX research is understanding friction points in real time. Hesitation detection reveals where users pause too long, signaling uncertainty or cognitive overload. Rage click detection catches moments of frustration - those rapid, repeated clicks that scream, "Why is this not working?" But frustration does not always look the same. Some users walk away silently. Task abandonment analysis helps us detect disengagement before it is too late, using behavioral trends rather than arbitrary cutoffs. Dwell time analysis adds another layer, showing how long users actively engage before losing interest. Of course, not all users behave the same way. Clustering techniques help group them based on interaction styles, making personalization and targeted interventions possible. And we can take it further - predictive modeling, like logistic regression, helps forecast dropout risk, allowing us to act proactively rather than reactively.

How I find conversion rate opportunities by breaking down the shopping funnel: Instead of looking at your entire funnel conversion rate (2-3% on average)... Step 1. Break it into parts. 1. All traffic 2. Non-bounce (% Sessions viewing 2+ pages) 3. Product Viewers (% Sessions viewing 1+ product) 4. Add to Cart (% Sessions adding 1+ product to cart) 5. Checkout Start (% Sessions starting checkout) 6. Checkout Complete* (% Sessions completing 1+ orders) *You can also break down the checkout flow further: Billing/Shipping > Review > Thank You As a percent of the total, a typical e-commerce site might be: 1. All traffic: 10,000 sessions - 100% 2. Non-bounce: 7,000 sessions - 70% 3. Product Viewers: 3,000 sessions - 30% 4. Add to Cart: 800 sessions - 8% 5. Checkout Start: 400 sessions - 4% 6. Checkout Complete: 300 sessions - 3% Step 2. Calculate the % moving to the next step The KEY is to look at the conversion rate between steps. Calculate by dividing the sessions on each step over the sessions from the previous step. 1. All traffic: NA 2. Non-bounce: 7,000 / 10,000 = 70% 3. Product Viewers: 3,000 / 7,000 = 43% 4. Add to Cart: 800 / 3,000 = 27% 5. Checkout Start: 400 / 800 = 50% 6. Checkout Complete: 300 / 400 = 75% Step 3. Look for trends You don't need to worry about ecommerce benchmarks. Your marketing channel mix, product type, and audience will all influence your numbers. Focus on YOUR numbers. This is your baseline. Trend these rates over time, and watch for anomalies. Step 4. Improve each step methodically Does your checkout completion rate look low (75%)? Maybe consider: - Checkout Form optimization - Adding new payment types - Simpler discount codes - Accurate delivery estimates Is your Add-to-Cart rate low (27%)? Maybe consider: - Pricing optimization - Additional social proof on PDP - Improved product images and videos - Digging into inventory and availability Step 5. Track your results As you make improvements (or run experiments) measure your intra-funnel rates. It's much easier to track improvements compared to looking at your aggregate conversion rate. Are you breaking down your e-commerce funnel? #cro #conversionrate #ecommerceanalytics

Most Businesses Don’t Know How to Measure Their Own Performance. They think they do. They track revenue. They look at ad spend. But they don’t have a structured way to evaluate why they’re winning—or losing. I use a three-part growth cycle with all my eCommerce clients. And one of the most game-changing phases? EVALUATE. I do this monthly, quarterly, and yearly. It's the second part my the growth cycle. Since it’s a new month, here’s exactly how I run my monthly evaluation process: Step 1: Audit 🔍 Before we analyze anything, we collect information. Here’s what matters: 📊 Sales Performance: Gross Sales, Top & Bottom SKUs by Revenue & Volume 📈 Paid Performance: Total Ad Spend, Blended CPA & ROAS 🎯 Customer Behavior: Conversion Rate, AOV, Return Rate, UPT, New Emails Collected 📬 Marketing Impact: % of Revenue by Channel, Discounts Used, Discount Rate Key rule: No opinions. No overthinking. Just information. Step 2: Analyze 📉 Now, we turn numbers into insights: ✅ Did we hit our forecast? Where did we fall short? 📆 How do we compare to the same time last year? 📩 What was the best and worst-performing email content? 📢 What paid ads crushed it? What completely flopped? This is where we face reality. No wishful thinking. No excuses. Just truth. Step 3: Ask the Hard Questions 🤔 Information is useless without decisive action. 🔹 What needs to change in the next 30 days? 🔹 What processes are slowing us down? 🔹 What will we test next month given what we now know? 🔹 What marketing insights need immediate action? 🔹 Who on the team needs to level up? 🔹 What needs to be adjusted in the yearly forecast? The best businesses don’t just look at the numbers. They act on them. Many skip this process. The best live by it. Want to learn more about my 3 step growth cycle that I use year over year? Go Here: https://lnkd.in/gdZgCZQD

Many people get 'tipping point' attribution wrong. Here's why: They think it's how you perfectly assign marketing attribution credit. They think it's trying to be a perfect model to measure every type of marketing activity. (Note: 'tipping point' attribution is tagging what the signal/source was that triggered sales to engage with the buyer. Could be a demo request, account-based signal, partner lead, a specific outbound sales play, etc. It's NOT what most people think of as first touch or last touch or multi-touch) Reporting on the tipping point is best at understanding what you're sending to sales and if it's worth their time and worth investing more into.   And "worth their time" means are these sources/signals turning into meetings/pipeline/revenue. You send 1000 demo requests to sales and 80% convert into deals You get sales to follow up on 500 event attendees and 20% convert into deals Sales follows up on account intent data and prospects into 1000 buyers and 5% convert into deals You get 100 partner referrals and 30% convert into deals This is helpful data to dig into to try and understand what sales should be focused on to make them more efficient. And what marketing can do better to drive the right conversations. The goal isn't to prove marketing's value across the full buyer's journey. The journey is too complex for one model to do it all. The key is to know the model's strengths and weaknesses. And tipping point can: Identify which signals are worth sales' time Stop wasting sales' time on low-converting activities Scale what is working at this stage of the buying journey