Web Analytics for Ecommerce Platforms

🚨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

🌐 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 R. for your Help #Datafam #PowerBI #EcommerceAnalytics #MarketingROI #CustomerExperience #DataStorytelling #BusinessIntelligence #DashboardDesign #DataDrivenDecisions #DataStrategy #DataVIZ

Let's talk about e-commerce KPIs that are important, but you may not be tracking. These are great leading indicators to spot if something is amiss and can help you run a more profitable and effective business. 1) Days Between Purchases What is the average number of days between the first and second purchase? In most cases, we are aiming to reduce this over time as customers become much more sticky if they can purchase 2+ times in their lifetimes. A tactical way to close the gap is your post purchase email flows - are you taking advantage of cross-sell/upsell opportunities in high open rate emails such as order and shipment confirmation emails? 2) % of Product Page Views What percentage of your traffic makes it way over to the product pages? We keep an eye on this as a trend in order to see if the quality of our traffic is up to snuff and if the site is developing any unwanted friction between upper level pages vs. pages deeper in the funnel. 3) Add to Cart % & Checkout Passthrough % The cart/checkout experience is one of the most valuable and high impact places on your site. We have often reversed sudden dips in this due to malfunctioning coupon codes, technical issues, pricing presentation issues, etc. and this has saved us and our clients a lot of money! 4) Revenue by New & Returning Customers We analyze trends in this over time to ensure our media mix is achieving its goals and also to see if we have any issues with retaining our customers. We were surprised in the past to see things like slow shipping times heavily affect returning customer revenue over long periods of time. 5) E-Commerce Search % For certain sites/brands, we see great conversion rates (up to 5x higher than average) whenever someone uses the search function on their sites. We aim to slowly increase search usage or experience over time in order to get customers closer to where they need to go. Amazon thinks that this is so critical that the search bar dominates every page on their site. 6) Site load times Site load times are critical to the customer experience and to conversion rates, but are often ignored and not tracked over long periods of time. A key piece of managing this is ensuring third party pixels are behaving well and are not unnecessarily kept on the site as your needs fluctuate and change. 7) Customer NPS / Customer Service Metrics (avg. time to fulfill order, etc) These metrics positively correlate to repeat revenue and order %. It's also a great way to "talk" to your customers since these surveys can be incredibly revealing and surface issues that are holding your business back, such as issues with products being damaged during the shipping process. KnoCommerce is a great tool to execute this! Lastly, we use an extremely customized dashboard from Databox to track and monitor all of these KPIs while being able to see weekly, monthly or quarterly trends. Any other lesser known metrics that are worthy of tracking?

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

In-Shopify conversion funnel metrics are a great start. But they don't give you a full picture of your store's shopping experience or optimization efforts. Here are some other data points you should be routinely monitoring (off-platform): 🏀 Bounce Rate - the % of people who leave your website after only seeing one page. High bounce rates can indicate you have a problem with load-times or responsiveness for certain devices. It can also mean that your website is merchandized poorly and people can't figure out how to find what they want to shop for, so they leave. First impressions matter, and a laggy website could mean you lose a customer for life. 🚪 Exit Rate - not to be confused with bounce rate. The exit rate is the % of people who leave your website after viewing a *specific* page. Very helpful for isolating individual pages that are causing dropoffs in the customer journey. ⬇ Page Depth - measures how many pages a shopper visited before leaving the site. It's a good measurement of engagement, particularly for brands with larger catalogs and longer evaluation windows for purchase. ⏱ Session Duration - gives you a picture of how long shoppers are spending on your store. Longer sessions do not necessarily mean better sessions (i.e. more engagement, higher value customers), but very short sessions durations can indicate an experience issue or traffic-to-page mismatch. 📹 / 🔥 Session recordings & Heatmaps - we are drowning in mountains of data to analyze. It can be difficult to hypothesize why a certain metric is up or down. Sometimes you need to step back and get a visual of the actual shopping experience and the answer becomes much more clear. Session recording software and heatmaps are excellent for these kind of insights. In the end, it all comes down to two things: traffic quality and a well-designed ecommerce store. While we can't help you with the first one, Platter can definitely help you build a high-performing Shopify storefront. If you're interested in a free audit, or want to chat about opportunities to improve your conversion metrics, shoot me a DM.