Marketing Analytics Visualization

🌐 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

Every brand has data. But not every brand knows how to interpret their data. To solve this, I combined media buying with Power BI so my clients can understand and take action on their data (instead of letting it collect dust). Here’s how I break up my data visualization inside of Power BI: Dashboard #1: Review This is a granular view of the day-to-day performance across brands, platforms, and sub-platforms. The goal is to highlight the most important metrics at the top, like web revenue and web roas. A daily calendar allows for clear transparency between our agency and our brands. Compare raw and calculated metrics to find day-over-day anomalies. Dashboard #2: Platform Being fluent in 1 platform will cost you. But being fluent in multi-platforms will give you a birds eye view of your data. A deeper understanding of the multi-platform allocation is great because you can use filters. All charts, tables, and calendars crossfilter to tell a deeper story. For example, I can compare Google, Facebook, Instagram, and TikTok ads day over day for the entire month and see how they contribute to the total revenue, conversion rate, and roas - all in one place. Dashboard #3: Compare and Project I can look at multi-performance with the ability to forecast and project, based on data from previous events, months and years. It’s like a zoomed out view of what’s actually happening inside of your business. When you’re able to see this data quickly and consistently, you can make more strategic decisions - quickly and consistently. Why data visualization matters: • You can back your strategy with real numbers and not shoot in the dark • Clear visualizations are 1000x more effective for understanding data compared to a pivot table • You get data FAST, and the quicker you interpret the data, the faster you can make impactful decisions. Media buying + data visualization is a game changer.

Hiring managers don’t care how many charts you know. They care if you know when to use them. Most analysts list “Data Visualization” on their resumes… But when it comes to building dashboards — they use pie charts for everything, or scatter plots with no story. Here’s the truth: The chart you choose decides if your insight lands or gets lost. This visual breaks down 7 essential chart types, when to use them, and real business use cases: What it covers: • Bar Chart → Compare product sales, marketing spends • Line Chart → Show trends like user growth or revenue over time • Pie Chart → Show % breakdowns (only when there are 2–3 categories) • Scatter Plot → Show correlations (e.g., ad spend vs. conversions) • Histogram → Show distributions (like survey age ranges) • Map → Show regional performance or location-specific data • Heatmap → Show intensity, volume, and activity hotspots Before your next dashboard, ask: What’s the one decision this chart should drive? Then pick the visual that makes that decision obvious. Want to learn how to turn these charts into powerful dashboards? Free Power BI Certificate Course by Microsoft: Course Name: Get started building with Power BI (Microsoft Learn) https://lnkd.in/dWPdAZdm What you’ll get: ✅ Official Microsoft Learn badge ✅ Free step-by-step dashboard training ✅ Certificate of completion ✅ No credit card, no fluff Like it if you find this useful Repost it so that can be helpful for others Follow Heena Kouser for more such useful content #DataStorytelling #PowerBI #DataVisualization #Excel #DashboardDesign #DataAnalyst #HeenaKouser #CareerTips #AnalyticsTools #InterviewReady

Transform complex data into actionable insights with network graphs. Here’s how. 👇 What are Network Graphs? Network graphs are visual representations of relationships between entities. Think of them as a map that shows how different points (nodes) are connected by lines (edges). These connections can reveal patterns and insights that are otherwise hidden in raw data. Why Are They Useful? Network graphs are incredibly useful because they make complex relationships easy to understand at a glance. They can show connections, groupings, and hierarchies in a way that’s intuitive and visually appealing. 3 Marketing Analytics Examples: 1️⃣ Customer Journey Mapping: Track and visualize the entire customer journey from initial contact to conversion. Understand how customers move through your funnel and identify key touchpoints to optimize their experience. 2️⃣ Influencer Mapping: Discover key influencers within your network who have the most impact. Understand how information spreads through your customer base and leverage these influencers to amplify your message. 3️⃣ Campaign Effectiveness: Visualize how different marketing channels are connected and how they contribute to overall campaign performance. This helps in optimizing your marketing mix and allocating resources more effectively. By using network graphs, you can turn complex data into actionable insights that drive growth and success. Don't just look at your data – understand it, leverage it, and make it work for you. 🚀 #DataVisualization #MarketingAnalytics #NetworkGraphs #MarketingStrategy