Understanding Ecommerce Analytics Tools

Introducing the web's first market map of the Product Analytics Market: I was floored when I couldn't find one of these online. Surely, Gartner or CBInsights or A16Z would have created one? It turns out not. So I spent the past 3 months: • Talking with 25 buyers • Researching the space myself • Interviewing 5 product leaders at key players This is what I learned about the most significant players in each space: (that PMs and product people need to know) 1. Core Product Analytics Platforms     The foundational tools for tracking user behavior and product performance Amplitude : The leader, an all-in-one platform for PMs to master their data Mixpanel : The leader in easy UX and pioneer in event-based analytics Heap | by Contentsquare: The automatic event tracking and real-time insights leader 2. A/B Testing & Experimentation     Platforms for analysis Optimizely : The premier tool for sophisticated A/B and multivariate testing VWO : The best for combining A/B testing with heatmaps and session recordings AB Tasty: The all-in-one solution for testing, personalization, and AI-driven insights 3. Feedback & Session Recording     Capture qualitative insights and visualize user interactions Medallia: The top choice for comprehensive experience management Hotjar | by Contentsquare: The go-to for visual feedback and user behavior insights Fullstory: The best for detailed session replay and user interaction analysis 4. Open-Source Solutions     Customizable, free analytics platforms for data sovereignty Matomo: The robust, privacy-focused open-source analytics platform Plausible Analytics: The lightweight, privacy-first analytics solution PostHog: The versatile, open source product analytics tool 5. Mobile & App Analytics     Specialized tools for mobile and app performance analysis UXCam: The best for in-depth mobile user interaction insights Localytics: The leader in user engagement and lifecycle management Flurry Analytics: The comprehensive, free mobile analytics platform 6. Data Collection & Integration     Gather and unify data across platforms Segment: The top choice for effortless customer data unification Informatica: The enterprise-grade solution for data integration and governance Talend: The flexible, open-source data integration tool 7. General BI & Data Viz     Non-product specific tools for data analysis and visualization Tableau: The leader in interactive, rich data visualization Power BI: The best for deep integration with Microsoft tools Looker: The modern BI tool for customizable, real-time insights 8. Decision Automation & AI     Systems for automated insights and decisions Databricks: The unified platform for data and AI collaboration DataRobot: The leader in automated machine learning and AI Alteryx: The comprehensive solution for analytics automation Check out the full infographic to see where your favorite tools fit and discover new platforms to enhance your product analytics stack.

🚨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

You’re not growing ...because your analytics are lying Quietly. Repeatedly. So we found 266 reasons why. Not theory. Real mistakes in tracking, calculation, and interpretation. Use this to: - Audit reports - Train your team - Avoid wrong calls - Debug dashboards Covers 8 key areas: 1/ Product Performance 2/ Conversion Funnel 3/ Traffic Attribution 4/ Revenue Metrics 5/ Tech Accuracy 6/ Segmentation 7/ Retention 8/ Email Each mistake includes: - Category - Type - Description - Impact level - Prevalence - Checklist how to fix - How It Looks in Reality - Misleading Outcome - Related Metrics - Sources Built as a searchable, filterable Airtable database Perfect for audits, onboarding, or daily use. 𝗪𝗵𝗼 𝘄𝗮𝗻𝘁𝘀 𝗮 𝗹𝗶𝗻𝗸? 💎 Available publicly until April 10 only. No exceptions #analytics #marketing #ecommerce

Fraudulent activities pose a significant threat to many businesses, making it crucial to detect and prevent them to protect both the company's reputation and bottom line. In a blog post by the engineering team from Booking.com, they share their innovative approach to combating fraud using graph technology. The rationale behind leveraging graph technology for fraud detection is straightforward: often, there are hidden links between various actors, identifiers, and transactions. For example, if an email address has been previously associated with fraudulent activity, it provides valuable context for future detection. This interconnected nature makes graph-based features highly effective for identifying fraud. The team at Booking built a graph using historical data, such as reservation requests. In this graph, nodes represent transaction identifiers like account numbers and credit card details, while edges connect identifiers that have been observed together before. When assessing fraud risk, they query the graph database to build a local graph centered around the request identifier, which helps to evaluate the likelihood of fraudulent behavior. One aspect that stands out is the dynamic visual representation of how the graph evolves with customer interactions, making it easier to understand the benefits of graph technology in fraud detection. It serves as a nice introduction to the potential of graph technology in combating fraudulent activities. #machinelearning #graph #datascience #analytics #fraud #detection – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gQAwSz7D

Generative AI surveys: where your feedback is interactive, valued, and promptly discarded. But hey, at least it’s efficient! Sorry, I know it’s a bit early to be snarky. Seriously though, closing the loop with your customers on their feedback - solicited or unsolicited - is a game changer. Start by integrating customer signals/data into a real-time analytics platform that not only surfaces key themes, but also flags specific issues requiring follow-up. This is no longer advanced tech. From there, create a workflow that assigns ownership for addressing the feedback, tracks resolution progress, and measures outcomes over time. With most tech having APIs for your CRM, also not a huge lift to set up. By linking feedback directly to improvement efforts, which still requires a human in the loop, and closing the loop by notifying customers when changes are made, you transform a simple data collection tool into a continuous improvement engine. Most companies are not taking these critical few steps though. Does it take time, effort, and money? Yes it does. Can it help you drive down costs and drive up revenue? Also, a hard yes. The beauty of actually closing the loop is that the outcomes can be quantified. How have you seen closing the loop - outer, inner, or both - impact your business? #cx #surveys #ceo

Inflation isn’t just an economic challenge—it’s a test of agility for businesses. As costs rise and purchasing power shifts, companies that rely on gut instinct risk falling behind. The real winners? Those who use data-driven insights to navigate uncertainty. 1️⃣ Understanding Consumer Behavior: What’s Changing? Inflation reshapes spending habits. Some consumers trade down to budget-friendly options, while others delay non-essential purchases. Businesses must analyze: 🔹 Spending patterns: Are customers shifting to smaller pack sizes or private labels? 🔹 Channel preferences: Is there a surge in online shopping due to better deals? 🔹 Regional variations: Inflation doesn’t hit all demographics equally—hyperlocal data matters. 📊 Example: A retail chain used real-time sales data to spot a shift toward economy brands, allowing it to adjust promotions and retain price-sensitive customers. 2️⃣ Pricing Trends: Data-Backed Decision-Making Raising prices isn’t the only response to inflation. Smart pricing strategies, backed by AI and analytics, can help businesses optimize margins without losing customers. 🔹 Dynamic pricing models: Adjust prices based on demand, competitor moves, and seasonality. 🔹 Price elasticity analysis: Determine how much a price hike impacts sales before making a move. 🔹 Personalized discounts: Use customer data to offer targeted promotions that drive loyalty. 📈 Example: An e-commerce platform analyzed customer behavior and found that small, frequent discounts led to better retention than infrequent deep discounts. 3️⃣ Demand Forecasting & Inventory Optimization Stocking the right products at the right time is critical in an inflationary market. Predictive analytics can help businesses: 🔹 Anticipate demand surges—especially in essential goods. 🔹 Optimize supply chains to reduce excess inventory and prevent stockouts. 🔹 Reduce waste in perishable categories like F&B, where price-sensitive demand fluctuates. 📦 Example: A leading FMCG brand leveraged AI-driven demand forecasting to prevent overstocking of premium products while ensuring budget-friendly variants were always available. 💡 The Takeaway Inflation isn’t just about rising costs—it’s about shifting consumer priorities. Companies that embrace data-driven decision-making can optimize pricing, fine-tune inventory, and strengthen customer loyalty. 𝑯𝒐𝒘 𝒊𝒔 𝒚𝒐𝒖𝒓 𝒃𝒖𝒔𝒊𝒏𝒆𝒔𝒔 𝒂𝒅𝒂𝒑𝒕𝒊𝒏𝒈 𝒕𝒐 𝒊𝒏𝒇𝒍𝒂𝒕𝒊𝒐𝒏𝒂𝒓𝒚 𝒑𝒓𝒆𝒔𝒔𝒖𝒓𝒆𝒔? 𝑨𝒓𝒆 𝒚𝒐𝒖 𝒖𝒔𝒊𝒏𝒈 𝒅𝒂𝒕𝒂 𝒕𝒐 𝒓𝒆𝒇𝒊𝒏𝒆 𝒚𝒐𝒖𝒓 𝒔𝒕𝒓𝒂𝒕𝒆𝒈𝒚? 𝑳𝒆𝒕’𝒔 𝒅𝒊𝒔𝒄𝒖𝒔𝒔 𝒊𝒏 𝒕𝒉𝒆 𝒄𝒐𝒎𝒎𝒆𝒏𝒕𝒔! #datadrivendecisionmaking #dataanalytics #inflation #inventoryoptimization #demandforecasting #pricingtrends

💭 Ever faced the challenge of keeping your data consistent across regions, clouds, and systems — in real time? A few years ago, I worked on a global rollout where CRM operations spanned three continents, each with its own latency, compliance, and data residency needs. The biggest question: 👉 How do we keep Dataverse and Azure SQL perfectly in sync, without breaking scalability or data integrity? That challenge led us to design a real-time bi-directional synchronization framework between Microsoft Dataverse and Azure SQL — powered by Azure’s event-driven backbone. 🔹 Key ideas that made it work: Event-driven architecture using Event Grid + Service Bus for reliable data delivery. Azure Functions for lightweight transformation and conflict handling. Dataverse Change Tracking to detect incremental updates. Geo-replication in Azure SQL to ensure low latency and disaster recovery. What made this special wasn’t just the technology — it was the mindset: ✨ Think globally, sync intelligently, and architect for resilience, not just performance. This pattern now helps enterprises achieve near real-time visibility across regions — no more stale data, no more integration chaos. 🔧 If you’re designing large-scale systems on the Power Platform + Azure, remember: Integration is not about moving data. It’s about orchestrating trust between systems. #MicrosoftDynamics365 #Dataverse #AzureIntegration #CloudArchitecture #PowerPlatform #AzureSQL #EventDrivenArchitecture #DigitalTransformation #CommonManTips

𝑩𝒆𝒚𝒐𝒏𝒅 𝒕𝒉𝒆 𝑶𝒓𝒅𝒊𝒏𝒂𝒓𝒚: 𝑬𝒍𝒆𝒗𝒂𝒕𝒊𝒏𝒈 𝒀𝒐𝒖𝒓 𝑨𝒅 𝑪𝒂𝒎𝒑𝒂𝒊𝒈𝒏𝒔 6 days. That’s how long it took Gap to back-peddle its new logo in 2010. Probably at great expense. The American fashion brand replaced its iconic blue square with a “more modern, sexier, cooler” image. It immediately flopped. The moral of the story is: a) Test major changes with a small sample of your audience; and  b) Monitor the impact.  Whether marketing mobile apps, B2B services or directly to B2C audiences, the aim is always the same: To be remembered. For the right reasons, unlike Gap 👆 That means combining: 🔸 Creative – compelling copy, attention-grabbing media, to-the-point messaging 🔸 Strategy – right channels, right audience, right goals 🔸 Audience insights – know your customer, understand behaviours, tailor the user experience Marketing tools can help you hit your goals more efficiently and take the guesswork out of your campaigns. I pulled together some of the most popular ad tools that provide insights into how to enhance your campaigns: → PPC Audit & Monitoring e.g. TrueClicks & Adalysis Optimise your Google Ads accounts, monitor campaign performance, and fine-tune your best performers. → Competitive Ad Analysis e.g. Adbeat, SpyFu & iSpionage Understanding your competitors’ strategy is as important as knowing your own. These tools reveal your competitors’ keywords, ad copy, and PPC strategies. → Data Aggregation & Reporting e.g. Supermetrics, Databox & DashThis Your campaigns don’t run on a single channel, nor should your reporting tools. Aggregate your data to cut out noise and make like-for-like comparisons. Bonus: You’ll also find tools that will help you create copy, optimise your landing pages, research keywords, and more. These tools are a useful starting point to improve your ROAS. However, in many cases, it’s not a lack of data that’s an issue. It’s having more data than you have the time or skills to decipher. That’s when tapping into our marketing expertise can help. We rigorously tested and analysed home decor brand, Lick’s social media campaigns to reach their target audience, resulting in: ✔ 6000% increase in purchase volume ✔ 437% ROAS Your numbers tell more than how many impressions, clicks, and conversions you received. They tell a story. Your customer’s desires. How you’re perceived. What you need to change. Listen. What tools would you add? 👇 Like this? Give me a follow for more expert-led marketing strategies. #marketing #adtools #adROI

Energy Trading with Apache Kafka and Flink: Real Time Decisions in Action Real time data has transformed how the #energy sector operates. In energy trading, every second matters. Prices change fast. Supply and demand fluctuate. Weather patterns shift. #IoT sensors and smart meters constantly feed new information into trading systems. That is where #DataStreaming with #ApachaKafka and #ApacheFlink comes in. Together, they power the real time pipelines that make energy markets more transparent, responsive, and predictable. Leading companies such as #Uniper, #realto, and #Powerledger already rely on this architecture. Their results show how scalable, reliable, and event driven data streaming brings measurable business impact: • Faster decision making and improved risk management • Automated trading workflows and event driven alerts • Real time integration of IoT data from energy grids and sensors • Improved forecasting with fresh, contextual data Uniper uses Kafka and Flink to process millions of messages per day across trading, dispatch, and invoicing systems. Confluent Cloud provides the scalability and SLAs for mission critical workloads. Powerledger combines Kafka and #blockchain to enable peer to peer energy trading and renewable energy certificate tracking. re.alto connects smart meters, APIs, and #IIoT systems for solar and smart charging use cases. These examples show how Data Streaming creates the foundation for next generation #EnergyTrading systems, uniting financial and IoT data to deliver real time insights, flexibility, and compliance. In a world where milliseconds can mean millions, Apache Kafka and Apache Flink are not just technologies. They are strategic tools for modern energy companies. How is your organization preparing to handle the growing demand for real time data in trading and energy operations? #DataInMotion #IoT #StreamingAnalytics #EnergyInnovation #AI #EventDrivenArchitecture https://lnkd.in/eHAdJEcg