E-commerce Data Insights

Certainly, while wishlists have emerged as a valuable tool for gauging consumer interest, there are several other methods and metrics that e-commerce platforms can use to measure consumer interest: 1. Cart Abandonment Rate: Observing how many customers add products to their carts but don't complete the purchase can provide insights into potential hesitations or barriers. 2. Product Views: The number of times a product is viewed can indicate its popularity or interest level. 3. Time Spent on Page: Monitoring the average time consumers spend on product pages can hint at their level of interest. 4. Product Reviews and Ratings: A high number of reviews or ratings, even if mixed, can signify strong interest or engagement with a product. 5. Search Query Analysis: Observing which products or categories users are searching for on the platform can indicate trending interests. 6. Social Media Engagement: Shares, likes, comments, and mentions related to products can provide insights into consumer preferences. 7. Referral Traffic: Analyzing traffic from external sites or social media can show where the interest is coming from and which products are driving it. 8. Customer Surveys and Feedback: Directly asking customers about their preferences or interests can yield detailed insights. 9. Sales Data: A straightforward metric, but analyzing which products are selling the most can clearly indicate consumer interest. 10. Click-Through Rate (CTR): Observing how often people click on a product after seeing it in a recommendation or advertisement can be a strong indicator. 11. User-Generated Content: If consumers are posting pictures, videos, or blogs about a product, it showcases genuine interest and engagement. 12. Repeat Purchases: Products that are frequently repurchased can indicate high levels of satisfaction and interest. 13. Customer Service Inquiries: The number and nature of questions related to a product can offer insights into areas of curiosity or concern. 14. Heatmaps: Tools that show where users most frequently click, move, or hover on a page can help in understanding which products or sections grab their attention. 15. Newsletter and Email Open Rates: If consumers are frequently opening emails about specific products or categories, it can be an indication of their interest areas. 16. Retargeting Campaign Success: The conversion rate of retargeting campaigns can provide insights into the residual interest of consumers after their initial interaction. By leveraging a combination of these methods, brands can gain a comprehensive understanding of consumer interest, helping them to tailor their offerings and marketing strategies more effectively. #ecommerce #LinkedInNewsIndia

🌐 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

Let's consider a real-world example of how connecting KPIs can lead to valuable insights and informed decision-making: Imagine you're managing an e-commerce business, and you're keen to boost sales. You have several KPIs, including: 𝐂𝐨𝐧𝐯𝐞𝐫𝐬𝐢𝐨𝐧 𝐑𝐚𝐭𝐞 (𝐂𝐑): The percentage of website visitors who make a purchase. 𝐀𝐯𝐞𝐫𝐚𝐠𝐞 𝐎𝐫𝐝𝐞𝐫 𝐕𝐚𝐥𝐮𝐞 (𝐀𝐎𝐕): The average amount spent by a customer in a single order. 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐀𝐜𝐪𝐮𝐢𝐬𝐢𝐭𝐢𝐨𝐧 𝐂𝐨𝐬𝐭 (𝐂𝐀𝐂): The cost of acquiring a new customer. 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐋𝐢𝐟𝐞𝐭𝐢𝐦𝐞 𝐕𝐚𝐥𝐮𝐞 (𝐂𝐋𝐕): The predicted revenue a customer will generate during their relationship with your business. Here's how you might relate these KPIs: 𝐂𝐨𝐫𝐫𝐞𝐥𝐚𝐭𝐢𝐨𝐧 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: You notice a positive correlation between CR and AOV. As the average order value increases, the conversion rate also goes up. This suggests that strategies aimed at increasing AOV, like offering bundled products or discounts for higher cart values, could lead to improved conversion rates. 𝐂𝐨𝐡𝐨𝐫𝐭 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: You group customers by their acquisition channel and analyze their behavior over time. You find that customers acquired through social media have a higher CLV compared to those acquired through paid search. This insight allows you to allocate more resources to social media marketing. 𝐁𝐞𝐧𝐜𝐡𝐦𝐚𝐫𝐤𝐢𝐧𝐠: You compare your AOV to competitors in the same niche. If your AOV is significantly lower, it might indicate an opportunity to increase prices or implement cross-selling and upselling strategies. 𝐂𝐚𝐮𝐬𝐞-𝐚𝐧𝐝-𝐄𝐟𝐟𝐞𝐜𝐭 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: You discover that a spike in CAC is associated with a drop in CLV. Upon investigation, you realize that a recent advertising campaign increased acquisition costs without proportionally increasing customer value. You decide to optimize your marketing strategy to maintain a healthy balance. 𝐒𝐜𝐞𝐧𝐚𝐫𝐢𝐨 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: You create scenarios to test the impact of different strategies on your KPIs. For instance, you simulate the results of offering free shipping for orders above a certain value. This could lead to higher AOV and potentially increased CR, but it will also affect CAC and, in turn, CLV. By connecting these KPIs and analyzing their relationships, you gain a comprehensive view of your e-commerce performance. This empowers you to make data-driven decisions to optimize your sales strategy, allocate resources effectively, and ultimately grow your business. Remember, the key is not just to collect KPIs but to understand how they influence one another and how you can leverage this knowledge to drive business success

🛒 How Basket Analysis Can Drive eCommerce Growth: A Bangladeshi Scenario As eCommerce continues to grow rapidly in Bangladesh, businesses are dealing with more and more customer data. One of the most valuable and often overlooked ways to make sense of that data is through basket analysis. Whether you’re working at a platform like Daraz, Chaldal, Pickaboo, or even running your own online shop, basket analysis can help uncover what products people are buying together. These insights can help you make smarter decisions when it comes to marketing, product placement, bundling, and personalized offers. 🔍 What is Basket Analysis? Basket analysis (also known as market basket analysis) is a method used to find associations between products based on customer purchase history. For example: - What do people usually buy with rice? - Are customers who buy smartphones also buying covers or screen protectors? - Are snack items more popular during weekends? By identifying patterns like these, eCommerce platforms can: - Increase average order value - Run more effective cross-sell campaigns - Deliver personalized recommendations - Make better inventory decisions 🧺 Real-Life Example: A Case Based on Chaldal While analyzing data from Chaldal, one of Bangladesh’s largest online grocery platforms, we noticed something interesting. Many customers in areas like Dhanmondi and Mirpur were buying instant noodles and tomato ketchup together, especially during the evening. This pattern suggested a common need: quick dinner solutions, likely for students or working professionals. Based on this insight, we tested a few simple strategies: - Introduced a combo offer with noodles and ketchup - Showed both products in the “Frequently Bought Together” section - Ran targeted push notifications in the evening with a message like “Need a quick dinner? Grab our Noodles + Ketchup combo now!” The early results were promising: - Better product visibility - More engagement during evening hours - A small bump in basket size for repeat users We’re still monitoring the data, but it’s a great example of how even small insights can be turned into smart decisions. 💡 Final Thoughts You don’t need AI or complex tools to start using basket analysis. A simple SQL query or spreadsheet analysis can help you uncover product relationships that lead to real business value. #eCommerce #BasketAnalysis #DataAnalytics #DigitalBangladesh #CustomerInsights #BusinessGrowth #SQLforBusiness #OnlineGrocery #MarketingStrategy #StartupBangladesh

As a director of e-commerce, I tried growing without the right marketing tools. It did not go well. At first, I thought I could make it work. Google Analytics for user behavior tracking. Meta Ads Manager for attribution. Google Tag Manager for A/B testing. A scrappy growth stack. Cheap. Efficient. Genius. It failed. GA4 made tracking impossible. Meta and Google both swore they drove 100% of our revenue. GTM required a developer for the smallest experiment ever. I spent more time debugging than actually growing the business. That’s when I realized: You can’t grow what you can’t see. Without the right data, every decision is a guess. So we stopped piecing things together and built a marketing stack that actually gives us reliable insights. Here’s what actually moved the needle: Heap | by Contentsquare: user analytics, heatmaps & session recordingsGA4 is a disaster. Heap auto-tracks user behavior, so we can see where revenue is leaking and fix it, fast. Crazy Egg: user surveys. Data only tells you what’s happening. Surveys tell you why. We use Crazy Egg to collect real feedback on why customers don’t buy. Zoom→ customer interviews. LTV comes from repeat buyers. We talk to our best customers every month to understand what keeps them coming back. Optimizely→ A/B testing & personalization. Most teams “experiment” without real insights. Optimizely helps us run controlled tests that impact conversion rates, AOV, and retention. Triple Whale: attribution & performance insights. Ad platforms take credit for every sale. TripleWhale gives us a real source of truth for attribution, so we can optimize smarter. Segment: customer data platform (CDP)Your data is fragmented across tools. A CDP makes sure every marketing channel has clean, consistent tracking. SendGrid: automated and marketing emailsBetter deliverability = higher retention and more repeat purchases. SendGrid makes it easy to iterate and improve. Most e-commerce teams don’t fail because of bad ideas. They fail because they can’t see what’s actually happening. If you don’t have the right insights, how can you optimize RPV and LTV? How do you ever know what experiment to run? E-commerce teams, what’s in your growth stack? What’s missing? Let me know if there is a tool you think is better.

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.

The most underrated report in Google Search Console Most eCommerce founders log into GSC, check clicks, impressions, and average position then log out. But that’s just scratching the surface. If you want real insights that lead to growth, there’s one report you should be checking every single week: The “Search Queries by Page” report Here’s how to get there: Performance → Pages → Click on a product/collection page → Queries tab This is where Google quietly tells you how it sees your page. And that data is gold for eCommerce. → You’ll find new keywords Google is testing your page for often untapped opportunities for optimization. → You’ll spot irrelevant queries, a signal that your intent, title, or headings might need refining. → You’ll uncover page 2 keywords, the ones close to breaking into page 1 with a bit of on-page improvement or internal linking. This report helps you shift from “how much traffic did we get?” to “what does Google think this page should rank for and are we aligned with that intent?” It’s not about chasing more clicks. It’s about understanding why you’re getting them (or not). So if you only have 15 minutes this week to spend on SEO skip the overview graphs. Open a key product page → click on “Queries” → and study what’s actually driving visibility. That’s where your next organic growth opportunity lives.

‎𝗪𝗵𝗲𝗻 𝗥𝗲𝘁𝘂𝗿𝗻 𝗥𝗮𝘁𝗲𝘀 𝗦𝘁𝗮𝗿𝘁 𝗘𝗮𝘁𝗶𝗻𝗴 𝗶𝗻𝘁𝗼 𝗛𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲 𝗘-𝗰𝗼𝗺𝗺𝗲𝗿𝗰𝗲 𝗣𝗿𝗼𝗳𝗶𝘁𝘀 ‎ ‎Not long ago, I got a call from the sales lead at 𝗧𝗵𝗲𝗿𝗮𝗽𝗶𝘅𝗼𝗫, a healthcare e-commerce company in India. ‎"𝘑𝘪𝘮𝘮𝘺, 𝘰𝘶𝘳 𝘳𝘦𝘵𝘶𝘳𝘯 𝘳𝘢𝘵𝘦𝘴 𝘩𝘢𝘷𝘦 𝘴𝘩𝘰𝘵 𝘶𝘱 𝘢𝘤𝘳𝘰𝘴𝘴 𝘐𝘯𝘥𝘪𝘢. 𝘖𝘳𝘥𝘦𝘳𝘴 𝘢𝘳𝘦 𝘨𝘦𝘵𝘵𝘪𝘯𝘨 𝘳𝘦𝘧𝘶𝘴𝘦𝘥 𝘪𝘯 𝘵𝘳𝘢𝘯𝘴𝘪𝘵... 𝘢𝘯𝘥 𝘪𝘵’𝘴 𝘩𝘶𝘳𝘵𝘪𝘯𝘨 𝘰𝘶𝘳 𝘮𝘢𝘳𝘨𝘪𝘯𝘴." ‎ ‎We jumped on a consultation call to unpack their business pain-points and strategic goals. Soon after, their dataset landed in my inbox, and the deep-dive began. ‎ ‎𝗢𝘂𝗿 𝗠𝗶𝘀𝘀𝗶𝗼𝗻: Find root causes, map patterns, and recommend strategies to reduce return rates. ‎ ‎𝗞𝗲𝘆 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 ‎ ‎☑️ 𝗢𝘃𝗲𝗿𝗮𝗹𝗹 𝗥𝗲𝘁𝘂𝗿𝗻 𝗥𝗮𝘁𝗲: 12%, alarmingly above the e-commerce average (<10%) for healthcare. ‎☑️ 𝗖𝗮𝘁𝗲𝗴𝗼𝗿𝘆 𝗜𝗺𝗽𝗮𝗰𝘁: Certain product categories had return rates exceeding 15%, suggesting category-specific issues. ‎☑️ 𝗣𝗿𝗶𝗰𝗲 𝗦𝗲𝗻𝘀𝗶𝘁𝗶𝘃𝗶𝘁𝘆: High-value products showed a disproportionate return rate, hinting at pricing or trust barriers. ‎☑️ 𝗚𝗲𝗼𝗴𝗿𝗮𝗽𝗵𝗶𝗰 𝗖𝗹𝘂𝘀𝘁𝗲𝗿𝘀: Some Indian states had 2–3× the national average in returns, pointing to potential logistics or regional marketing gaps. ‎☑️ 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗕𝗲𝗵𝗮𝘃𝗶𝗼𝗿: First-time buyers accounted for 70% of returns, a major retention opportunity. ‎ ‎𝗥𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝗧𝗵𝗲𝗿𝗮𝗽𝗶𝘅𝗼𝗫: ‎ ‎✅ 𝗧𝗮𝗿𝗴𝗲𝘁 𝗛𝗶𝗴𝗵-𝗥𝗶𝘀𝗸 𝗟𝗼𝗰𝗮𝘁𝗶𝗼𝗻𝘀: Bengaluru, Manipur, Tripura show significantly higher return rates; deploy localized communication, pre-delivery confirmations, and incentive adjustments. ‎✅ 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝗛𝗶𝗴𝗵-𝗥𝗶𝘀𝗸 𝗖𝗮𝘁𝗲𝗴𝗼𝗿𝗶𝗲𝘀: Weight-loss products dominate return volume; improve product imagery, descriptions, and expectation management. ‎✅ 𝗜𝗻𝘁𝗲𝗿𝘃𝗲𝗻𝗲 𝗮𝘁 𝗠𝗶𝗱-𝗩𝗮𝗹𝘂𝗲 𝗣𝗿𝗶𝗰𝗲 𝗕𝗮𝗻𝗱: Mid-tier products see high returns; experiment with free samples, testimonials, or quality guarantees. ‎✅ 𝗔𝗱𝗱𝗿𝗲𝘀𝘀 𝗖𝗢𝗗 𝗥𝗶𝘀𝗸𝘀: Most returns are likely COD refusals; strengthen pre-delivery verification and flexible payment conversions. ‎✅ 𝗟𝗼𝘄𝗲𝗿 𝗔𝘃𝗴. 𝗥𝗲𝘁𝘂𝗿𝗻 𝗥𝗶𝘀𝗸 𝗦𝗰𝗼𝗿𝗲: Use targeted interventions (above) and monitor the KPI weekly to push it below 2.0. ‎ ‎In e-commerce, return orders aren’t just a logistics headache, they’re a profit drain and a brand trust risk. And in healthcare, every undelivered order could mean a patient not getting the product they urgently need. ‎ ‎𝗗𝗶𝘀𝗰𝗹𝗮𝗶𝗺𝗲𝗿: 𝘚𝘩𝘢𝘳𝘦𝘥 𝘸𝘪𝘵𝘩 𝘤𝘭𝘪𝘦𝘯𝘵 𝘱𝘦𝘳𝘮𝘪𝘴𝘴𝘪𝘰𝘯. “𝘛𝘩𝘦𝘳𝘢𝘱𝘪𝘹𝘰𝘟” 𝘪𝘴 𝘢 𝘧𝘪𝘤𝘵𝘪𝘵𝘪𝘰𝘶𝘴 𝘯𝘢𝘮𝘦 𝘵𝘰 𝘱𝘳𝘰𝘵𝘦𝘤𝘵 𝘵𝘩𝘦 𝘤𝘰𝘮𝘱𝘢𝘯𝘺’𝘴 𝘱𝘳𝘪𝘷𝘢𝘤𝘺. ‎#EcommerceAnalytics #HealthcareEcommerce #BusinessIntelligence #ReturnsManagement #SupplyChainOptimization #DataAnalytics #Datafam #Excel #DataVisualization #Healthcare #HealthcareAnalytics #Ecommerce

The bottom line for the first quarter data on e-commerce sales is that demand was exhausted after the prospect of trade policy raising prices and reducing supplies sent consumers to shop online for imported items in the third and fourth quarter 2024. Now the question is if demand will recover in the second quarter 2025 or if deeply pessimistic consumers are cutting back to shopping for essentials. E-commerce sales accounted for 16.2% of total retail sales in the first quarter, the same as in the prior two quarters. This is a record high after the Census Bureau made their annual revisions. It is possible that the upward trend for consumers online shopping has reached a place where further increases will be harder to achieve for the e-commerce sector. Total retail sales are up 0.4% quarter-over-quarter in the first quarter 2025 while e-commerce is flat. E-commerce sales were up 2.3% and 1.7% in the third and fourth quarters 2024, respectively. Total retail sales are up 4.5% year-over-year with e-commerce sales up 6.1% in the first quarter 2025. This is the slowest annual increase for e-commerce since up 5.4% in the fourth quarter 2022. #ecommerce Please do not use without attribution. Prepared without use of AI. Copyright © Theresa A Sheehan