Data Analytics for Mobile Commerce

We increased Conversion Rate by 88% Wanna know how? We exposed Search on Mobile (instead of hiding it behind a search icon). How did we know to test this? During our comprehensive CRO Insights Service, we analysed heatmaps and session recordings, along with Shopify and GA4 data to understand user behaviour. And we uncovered two key insights: 1. Mobile sessions were higher than desktop 2. Users who engaged with the search bar showed a strong intent to purchaseBased on this, we hypothesised that making the search bar more accessible on mobile, we would create a smoother user experience, leading to higher conversion rates. Then we A/B tested it.And the results: ✅ 126% increase in search trigger clicks ✅ 23% increase in engagement with 'Looking for any of these' ✅ 109% increase in Average Purchase Revenue per User ✅ 30% increase in Add to Cart per sessionAnd of course, 88% increase in Conversion Rate.

🌐 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

Retailers have no shortage of data - but are you surfacing the insights that truly matter? E-commerce leaders track AOV, ROAS, NPS, and churn, but knowing what’s changing isn’t enough—you need to know why. Traditional products analytics often leave teams reacting to trends instead of driving them. That’s where Loops comes in. Our AI-powered analytics platform helps large retailers uncover the real drivers behind KPI shifts and make data-backed decisions with confidence, with: 1️⃣ Root Cause Analysis: Automatically identify the reasons behind fluctuations in key metrics such as Average Order Value (#AOV), Return on Ad Spend (#ROAS), Net Promoter Score (#NPS), and inventory turnover. This proactive approach allows you to address issues before they impact your bottom line. 2️⃣ Real-Time Gen-AI Alerts Insight Summaries: Receive personalized alerts and insight updates on trends, anomalies, forecasts, and the impact of recent initiatives directly through Slack, Microsoft Teams, or email. This ensures your team stays informed and agile in responding to changes in your top KPI. 3️⃣ Product Release Impact Analysis: Measure the effect of every product change on your KPIs with over 90% accuracy of standard A/B testing but with minimal traffic, time, and resources. Loops' causal models account for variables like performance improvements, marketing promotions, seasonality, pricing adjustments, experiments, product errors, and user mix changes, providing a clear view of each change's impact. 4️⃣ User Journey Optimization: Identify and rank user paths that significantly influence your KPIs at every stage of the customer lifecycle. By understanding these journeys, you can optimize marketing strategies, landing pages, and the entire user funnel to drive conversions and retention. Proven Results with Loops: 🔥 ✅ 200% Increase in Conversions: Achieved through Loops' "User Journey" insights at Wahi Real Estate. ✅ $5 Million Revenue Saved: Through causal analysis of a core KPI drop at a major consumer goods retailer, enabling a partial release with a negative impact to be rolled back before it hit all users. ✅ 15% Increase in Day 2 Retention: Observed at 18Birdies, enhancing customer engagement and loyalty. Move beyond traditional dashboards, uncover hidden growth opportunities, and make data-driven decisions that propel your retail business forward. Discover how Loops can unlock your company's potential. #RetailAnalytics #AI #DataDrivenDecisionMaking #EcommerceGrowth #eCommerce #retail #CausalAI National Retail Federation, Shoptalk

2024, New York - the day I decided to take retail analytics to the next level. I never anticipated how much these insights would transform our understanding of customer journeys. Here's what we built: 🚀 The Beginning:   We started with multiple retail touchpoints—web, mobile apps, and POS systems—operating in silos. It was a challenge to get a unified, real-time view of user behavior and funnel performance across channels. 🌧️ The Challenges:   We faced inconsistent data streams, difficulty tracking drop-offs precisely, and delayed insights that hampered quick decision-making. There were moments when our merchandising efforts felt reactive rather than strategic. 💡 The Turning Point:   The breakthrough came when we integrated streaming data from all touchpoints using Kafka, Kinesis, and Spark. It was a moment of clarity—seeing every interaction as part of a larger, real-time story. 📈 The Growth:   Armed with a unified analytics layer structured via Delta Lake and stored in S3 and Snowflake, we built dashboards that updated hourly. Tracking funnel drop-offs in real time allowed us to identify friction points instantly and adjust campaigns based on location-specific insights. Results? Faster feedback loops, optimized product placements, and a clearer understanding of what truly drives conversions across digital and physical channels. 🏆 The Lessons:   1. Streaming events stitch together the customer journey seamlessly.   2. Structured data layers enable faster, more accurate analysis.   3. Real-time insights empower proactive decision-making. This journey has been transformative. If you’re looking to turn retail data into actionable stories, remember: the key is capturing user interactions as streaming signals and structuring them for rapid insights. What’s your experience with real-time retail analytics? Let’s share and learn together.  #DataEngineering #RetailAnalytics #StreamingData #Spark #Kafka #DeltaLake #Snowflake #RealTimeInsights #CustomerJourney #BigData #ETL #POSData #DigitalTransformation #BusinessDecisions