How To Use Data To Enhance Your Ecommerce UX

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

Drowning in dashboards? You're not alone. Ecommerce teams usually aren't short on data. What's missing is a clear picture of what that data actually means. In other words, knowing what KIND of data you're sitting on. That's what drives better targeting and scalable growth. I've worked with dozens of ecommerce teams who were data-rich but insight-poor. But once we broke the data down into four clear types, performance started compounding. Here's how each type works and how they fit together: 1️⃣ First-party data ↳ The backbone of lifecycle marketing - Behavior you observe directly - site activity, purchases, email engagement. - Most accurate, privacy-compliant and foundational for retention. - Works for abandoned cart flows, custom segments, triggered emails. 2️⃣ Zero-party data ↳ Gold for personalization - Info customers intentionally share (quizzes, surveys, preference centers). - Reveals intent and helps tailor experiences. - Works for dynamic product recs, personalized SMS, on-site experiences. 3️⃣ Second-party data ↳ An underutilized growth lever - Trusted data shared from partners, like list swaps or co-marketing insights. - Adds reach without sacrificing context or quality. - Works for cross-promos, joint launches, collaborative campaigns. 4️⃣ Third-party data ↳ A fading legacy tactic - Aggregated info from data brokers (usually cookie-based). - Broad but increasingly limited in precision and shelf-life. - Works for paid ads (while they still work). When you know the data types,  You stop guessing and start layering. Layer them well (and connect customer identity across them), and you'll unlock high-quality personalization. That's when performance starts to compound. Where are you in this process currently? ♻️ Share this to help someone who's swimming in data but seeing no results. Follow me, Francesco Gatti, for more ecommerce data insights.

Last month, a jewelry client increased their conversion rate by 32.7% and boosted revenue by 35.7% after implementing a CRO program based on shopper behavioral data in GA4. When they started with us back in September they had almost no data in GA4, and they had some concerns about the investing in Google Analytics implementation: ❌ "What is this going to tell me that my TripleWhale and Northbeam doesn't?" ❌ "Even if I have the insights, who is going to run CRO? Me?!!" ❌ "What if engagement increases but doesn’t translate into sales?" All valid concerns… But we showed them how behavioral research guides the way to greater conversions with statistics and an engineering approach increasing conversions —just by collecting the right data and using our AI to analyze behavior and get test suggestions. So we got to work: 🔹 Implemented tracking on the most important shopping behaviors 🔹 Ran through analysis of what shopping behaviors were correlated to transations 🔹 A/B tested the visibility of features ENCOURAGING those behaviors on PLP pages, measuring whether early exposure influenced conversion rates 🔹 Measured revenue impact to ensure I wasn’t just increasing engagement, but driving real sales Since we did that (+ some consistency), they’ve: ✅ Increased conversion rates +32.7% ✅ Generated 35.7% more revenue in that category. ✅ Built a repeatable, data-backed strategy for using what we learned across the entire website. If you're an eCommerce brand struggling with low conversion rates or uncertain about how to use shopper behavior effectively to run your CRO program. 📩 comment below, and I’ll share with you our templates for how we did it! #EcommerceGrowth #Clickvoyant #ConversionOptimization #googleAnalytics #MarketingAnalytics 🚀

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.