Analyzing Ecommerce Website Traffic

Killer graph. Out of the £130 billion online non-food purchases we make in the UK, £27 billion of them get sent back to retailers. Our research with ZigZag Global shines a spotlight on the significant challenge online returns cause in the industry, focusing on those consumers who consistently and intentionally over-order - the "serial returners". Key stats ➡️ Around 11% of online shoppers are serial returners (frequently over-ordering with the intention of returning many items) ➡️They account for 24% of all online returns ➡️Serial returners send back, on average, £1,400 worth of online orders per year, compared with an average of £650. ➡️ This amounts to £6.6 billion of returns. ➡️ Almost three-quarters of serial returners are under the age of 45, and they return more than 42% of all their orders. A 1/4 of serial returners admit to over-ordering just to reach a minimum order value (often to trigger free delivery) only to return goods they had no intention of keeping. The same proportion also said they had returned items after finding them cheaper elsewhere or on promotions. While 18% admitted to returning items having already used them for a short period. There is no silver bullet here that is going to fix this issue for retailers. A nuanced understanding of specific triggers and barriers is essential to effectively target returners through pricing and returns options. 💥 For many boardrooms debating whether they should charge for returns, my thoughts are: 💥 The returns equation transcends simple binary choices between free or paid. Retailers must architect differentiated returns propositions that align commercial realities with customer lifetime value. Smart retailers will segment their returns strategy by customer profitability metrics, leveraging AI to identify purchase patterns that predict long-term value. This enables dynamic returns pricing that protects margins while fostering relationships with truly valuable customers. The goal isn't to punish returns – it's to price them according to their true cost to serve, while rewarding profitable shopping behaviours. There's also a paradox at play where customer acquisition costs are optimised but customer profitability is compromised. Many retailers are essentially subsidising unsustainable shopping behaviours at the expense of margin, unknowingly targeting customers they could do without. The real opportunity lies in leveraging returns data as a predictive indicator of customer profitability. By applying advanced analytics to returns patterns, seasonal purchasing behaviours, and cross-category browsing and mining deep behaviour insights, retailers can enable proactive intervention before profitability erodes. This shifts the conversation from universal policies to personalised solutions that can turn returns from a pure cost centre into a strategic lever for customer engagement and loyalty. Full research is available to download here ⬇️ https://lnkd.in/e5paRNWC

Returns affect inventory, cash flow, and staffing—yet many forecasts ignore them. While most companies put effort into forecasting sales, they often underestimate the importance of return forecasts. Return rates can range from 10% to 50% depending on the product category, especially in e-commerce. A good 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗿𝗲𝘁𝘂𝗿𝗻 𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁 helps you: ✅ Plan for peak return periods—not just peak sales ✅ Allocate labor efficiently ✅ Improve cash flow forecasting ✅ Reduce excess inventory and waste 𝗪𝗵𝗮𝘁 𝗱𝗼 𝘆𝗼𝘂 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗰𝗼𝗻𝘀𝗶𝗱𝗲𝗿? 📦 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗰𝗮𝘁𝗲𝗴𝗼𝗿𝘆: Fashion returns more than electronics 📅 𝗧𝗶𝗺𝗲 𝗹𝗮𝗴: Returns don’t happen instantly 👥 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝘀𝗲𝗴𝗺𝗲𝗻𝘁 𝗯𝗲𝗵𝗮𝘃𝗶𝗼𝗿: New vs. loyal customers differ 🛒 𝗦𝗮𝗹𝗲𝘀 𝗰𝗵𝗮𝗻𝗻𝗲𝗹: Marketplace vs. own shop 🔁 𝗖𝗮𝗺𝗽𝗮𝗶𝗴𝗻𝘀 & 𝘀𝗲𝗮𝘀𝗼𝗻𝘀: Flash sales often inflate return rates 📉 𝗛𝗶𝘀𝘁𝗼𝗿𝗶𝗰𝗮𝗹 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀: Use past data to project future behavior Aligning return forecasts across sales, operations, and finance will ensure smoother planning with fewer surprises. At RizonX, we help logistics teams go beyond sales forecasts and build 𝗿𝗲𝘁𝘂𝗿𝗻-𝗮𝘄𝗮𝗿𝗲 𝗽𝗹𝗮𝗻𝗻𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹𝘀. Because what comes back is just as important as what goes out. How would you approach the challenge of forecasting return volumes? ---------------- ♻️ 𝗦𝗵𝗮𝗿𝗲 if you find this post useful. ➕ 𝗙𝗼𝗹𝗹𝗼𝘄 Andy and RizonX for more real-world data use cases. 📩 𝗥𝗲𝗮𝗰𝗵 𝗼𝘂𝘁 to find out how a robust returns forecast can support your business. #dataanalytics #forecast #customerreturns #logistics #rizonx

When I interviewed Stephan Waldeis, VP of eCommerce Europe at Husqvarna Group, he said this about tracking real-time data and retailer partnerships. “We track customer behavior, we track inventory levels at our partners, we track sales performance — and of course, we possibly... we track all of that in real time. Imagine, our robots — at least the ones from the last 10+ years — are all connected. So, we have a lot of insights in which gardens they are driving, when they are operating, etc. And that is data that we are leveraging, but also data that we are sharing with our channel partners. That’s great even for the channel partners who are not really interested in operating an eCom site. We provide them with a lot of insights… what kind of products are interesting in your area, because we know exactly from visits on our site, which products in a particular region are more relevant — in Amsterdam versus in Berlin versus in Munich.” 𝗛𝗼𝘄 𝘀𝗵𝗼𝘂𝗹𝗱 𝘄𝗲 𝘁𝗿𝗮𝗻𝘀𝗹𝗮𝘁𝗲 𝘁𝗵𝗶𝘀 𝗳𝗼𝗿 𝗖𝗣𝗚 𝗯𝗿𝗮𝗻𝗱𝘀 𝗮𝗿𝗼𝘂𝗻𝗱 𝘁𝗵𝗲 𝘄𝗼𝗿𝗹𝗱 𝘁𝗼 𝗳𝘂𝗲𝗹 𝗴𝗿𝗼𝘄𝘁𝗵? 1️⃣ Activate Real-Time Retailer Collaboration Track and share real-time consumer behavior, inventory, and sales data with retail partners — even those with limited digital capabilities — to strengthen joint decision-making, optimize local assortments, and drive smarter sell-through at the shelf. 2️⃣ Localize Product Strategies with Regional Demand Signals Use geo-specific browsing and purchase data to tailor product recommendations, promotions, and stock levels at the city or neighborhood level — what sells in Amsterdam might flop in Berlin if you don’t read the digital shelf signals correctly. 3️⃣ Turn Connected Product Data into a Competitive Advantage Leverage connected device insights (where available) not only for product innovation but as a marketing and retail sales weapon, identifying usage patterns, seasonal trends, and regional preferences that can feed back into supply chain, DTC, and retail media strategies. 𝗧𝗼 𝗮𝗰𝗰𝗲𝘀𝘀 𝗮𝗹𝗹 𝗼𝘂𝗿 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗼𝗹𝗹𝗼𝘄 ecommert® 𝗮𝗻𝗱 𝗷𝗼𝗶𝗻 𝟭𝟰,𝟬𝟬𝟬+ 𝗖𝗣𝗚, 𝗿𝗲𝘁𝗮𝗶𝗹, 𝗮𝗻𝗱 𝗠𝗮𝗿𝗧𝗲𝗰𝗵 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲𝘀 𝘄𝗵𝗼 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲𝗱 𝘁𝗼 𝗲𝗰𝗼𝗺𝗺𝗲𝗿𝘁® : 𝗖𝗣𝗚 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗚𝗿𝗼𝘄𝘁𝗵 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿. About ecommert We partner with CPG businesses and leading technology companies of all sizes to accelerate growth through AI-driven digital commerce solutions. Our expertise spans e-channel strategy, retail media optimization, and digital shelf analytics, ensuring more intelligent and efficient operations across B2C, eB2B, and DTC channels. #ecommerce #dataanalytics #CPG #FMCG #data Milwaukee Tool Bosch Makita U.S.A., Inc. STIHL Mondelēz International Nestlé Mars Ferrero General Mills L'Oréal Henkel Beiersdorf Colgate-Palmolive The Coca-Cola Company Unilever L'Oréal Coty Kao Corporation adidas Nike New Balance PUMA Group the LEGO Group Sony Panasonic North America Bose Corporation

Marketers, are you still measuring email the old way? We get told email is dead, but everyone reading this has most likely read an email, logged in using it & made a purchase with it. So it's not dead, but how we judge its effectiveness hasn’t evolved fast. We’ve relied on open rates & click-through rates (CTR) — metrics that, frankly, are no longer fit for purpose. Why open rates are no longer reliable Open tracking depends on image loading, which Outlook often blocks, & Apple & Gmail preload by default. As a result, you might see machines open, not human ones. And proper visibility is vanishing with more “text-only” creatives or image-blocked environments. And CTR? It’s got its own problems Think about user intent. If a customer reads “50% off this weekend” in your subject line, they may just go straight to your site—no click needed. Even Gmail’s AI summarising content & extracting voucher codes means users engage without clicks. Email is quickly becoming a powerhouse for brand awareness, but it doesn't have the metrics to prove this. So, what should we look at? As the rest of adtech races toward incrementality, attention, and post-impression attribution, email needs to catch up. Here’s how: 1. Conversion Attribution (Beyond Last Click) Don't stop at click-based conversions. Track who received the email, & assign influence weightings to openers, clickers, & even non-clickers who later convert. This mirrors how display and social now assess "view-through" impact. 2. Frequency & Multi-Touch Engagement Did the recipient open on mobile in the morning, revisit via desktop, & convert on payday? That’s a multi-touch journey. Look at repeat site visits, device switching, & re-engagement post-send. 3. Pay Day or Trigger-Based Lift Create holdout groups and measure uplift around high-conversion moments (e.g., end-of-month). This mirrors the incrementality testing often used in paid social or programmatic, proving that email drives behaviour, not just volume. 4. Attention Metrics Use tools to estimate dwell time on emails or the time between opening& clicking. These are soft proxies for intent, similar to how platforms measure scroll depth, hover rate, and ad exposure time in other channels. 5. Site Quality Metrics Did email recipients spend longer on site, view more pages, or have higher AOVs? Your session quality tells you if email delivers high-intent traffic, something brands already monitor from Google Ads or affiliates. 6. Ask them! Simple, but powerful: survey your audience. What emails did they find valuable? Did it change their behaviour? Self-reported attribution, done well, can give you what click-tracking can’t. Email deserves more credit than. If adtech is shifting toward attention, incrementality, & deeper behaviour analysis, email should, too. Let's measure actual impact, not just opens & clicks. I bet you will discover that email isn't just for conversion but also a branding-building superpower.

What happens when you align product performance with sessions, conversion rate, advertising spend, stock on hand and sell-through date? You stop guessing and start making commercial decisions with real clarity. The best merchandise planners and marketers already know this: no metric in isolation tells the full story. The strongest teams are combining traditional planning metrics with ecommerce performance data to understand not just what is happening, but why. For DTC brands, bringing these data points together turns a messy performance picture into a simple set of actions: 🔍 1. Decide what to advertise more When a product has strong conversion, healthy margins and enough stock to support demand, but low sessions, it’s usually a sign that it needs more visibility. This is the sweet spot for scaling paid spend: the product already proves it can sell — it just needs more traffic. 💸 2. Identify what to mark down If you’re holding too much stock and the sell-through date is creeping up, yet conversion is weak even with steady sessions, discounting becomes a strategic lever. Markdowns help clear inventory without wasting ad spend on products the customer clearly isn’t choosing at full price. ✋ 3. Know when to pull back advertising High ad spend + plenty of sessions but poor conversion = a red flag. This is where you pause or reduce spend, diagnose the issue (price, positioning, creative, customer reviews), and redirect budget to products with stronger unit economics. Sometimes the best ROI comes from simply stopping the leak. When metrics live in silos, teams argue. When metrics connect, teams act. This is how modern DTC brands protect margin, improve cash flow and scale the right products at the right time.

🌐 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

A D2C menswear founder I sat across last month showed me his Meta dashboard. ROAS 3.8x. Google Ads showing 3.2x. He was spending ₹15 lakh a month across both platforms and it seems like both channels are working. I asked him one question. What’s your blended ROAS when you check your bank account against total ad spend? He said 1.6x. Meta claims a conversion if someone even viewed an ad and bought within a day. Google claims the same conversion if that person searched the brand name before purchasing. Both platforms report the sale. Only one sale happened. According to a Varos industry benchmark, Meta reports 26% higher conversions on average compared to third-party analytics tools. Google Ads over-attributes by 15 to 20 percent when enhanced conversions are active. But that’s only the first leak. The second one is more expensive. His best-performing Meta ad was a Reel showing a guy walking into a meeting room in a sharp co-ord set. Great hook, strong thumb-stop rate. The ad worked. People clicked. They landed on the homepage. Not the co-ord set. Not even the category page. The homepage, with 400 SKUs and a banner for a monsoon sale that ended weeks ago. The average cart abandonment rate in Indian ecommerce is over 74 percent. Seven out of ten people who were interested enough to click, browse, even add to cart, still didn’t buy. The Reel sold a look. The landing page sold a catalogue. That disconnect between creative and destination is where clicks go to die. the third leak is slowest and difficult to catch - That co-ord set Reel had been running for six weeks. In the first week it was extraordinary. By week three, frequency had climbed, CPM followed, and the algorithm was still pushing it because it had no alternative. When your entire ad account depends on one type of creative, Meta’s delivery system concentrates on it until the audience stops responding. The DSGCP and Meta playbook surveyed over a hundred Indian D2C founders and found that 62 percent cited creative fatigue as a major bottleneck. Not targeting. Not budget. The creative dying while the campaign keeps running and nobody pausing it. Three leaks. Attribution inflates the win. The landing page bleeds the click. The creative fatigue while you’re still enjoying first week performance. The entire D2C performance marketing conversation in India is about the ad. Better hook. Sharper creative. Tighter targeting. Every conference panel, every Twitter thread, every agency pitch deck starts and ends with the ad. Nobody talks about what happens after the click. Nobody talks about what happens when both platforms count the same customer. Nobody talks about the slow death of a creative.

📌📊 eCommerce Google Analytics 4 Dashboard I've recently created a Looker Studio Report for eCommerce brands. The main goal is to analyze website traffic performances using GA4 Data. Having a custom-built solution like this will give marketers an edge over their competition and understand performances from different traffic sources. ➡️ Where do your users come from? The dashboard breaks down traffic by channel, showing the distribution across direct, organic search, referral, and other sources. It also provides geographical data to understand your primary markets. ➡️ Which campaigns/traffic sources bring the most revenue? Revenue is broken down by channel, allowing you to compare the performance of different traffic sources over time. This helps identify which channels are most effective for driving sales. ➡️ What are the key performance indicators? The dashboard tracks crucial eCommerce KPIs including Total Revenue, Purchases, Conversion Rate, and Average Order Value. It also monitors user engagement metrics like sessions, bounce rate, and average session duration. ➡️ How does device type impact user behavior? Device type data shows the distribution of sessions across desktop, mobile, and tablet. This information can help optimize the user experience across different platforms. This level of insight helps marketers make informed decisions to drive better results for their advertising efforts. As a marketer, it has never been easier to manage your marketing data and turn it into actionable insights. ⚙️ Technical note: In this example, I've used Looker Studio native GA4 data connector to import the data 🔍 Demo Version: https://lnkd.in/e4YWQBGv #DataAnalytics #DataVisualization #BusinessIntelligence

Ecommerce Meta Ads Checklist That Actually Drives Sales (Not Just Clicks) Running ads but not getting consistent results? Most brands don’t fail because of budget… they fail because they miss the fundamentals. Here’s a complete Meta Ads checklist every eCommerce brand should follow 👇 🔍 1. Tracking Setup (Foundation of Everything) If your tracking is broken, your ads are blind. ✔ Meta Pixel installed on all pages ✔ Conversion API (CAPI) active (server-side tracking) ✔ Events configured: ViewContent, AddToCart, InitiateCheckout, Purchase ✔ Event deduplication working (Pixel + CAPI) ✔ Domain verified ✔ Aggregated events set (Purchase prioritized) 👉 Without this, scaling is impossible. 🛒 2. Store Readiness (Conversion Matters More Than Traffic) Ads don’t convert — your store does. ✔ High-quality product pages (images, price clarity, CTA) ✔ Mobile-first optimization (fast loading = higher conversions) ✔ Smooth checkout (no friction) ✔ Trust elements (reviews, guarantees, return policy) 👉 Even the best ads fail with a weak landing experience. 🎯 3. Campaign Structure (Clarity Wins) Don’t overcomplicate. ✔ Objective: Sales ✔ Funnel: Prospecting → Retargeting → Scaling ✔ Budget split: 70% Cold | 20% Warm | 10% Hot ✔ ABO for testing, CBO for scaling 👥 4. Audience Setup (Let Algorithm Work Smart) Stop over-targeting. ✔ Broad targeting (minimum restrictions) ✔ Custom audiences (website visitors, ATC, buyers) ✔ Lookalikes (1–5%) ✔ Retargeting windows: 7 / 14 / 30 days 🎥 5. Creative Strategy (This Is Where Winners Are Made) Your creative = your sales engine. ✔ 3–5 creatives per ad set ✔ Multiple angles: problem, benefit, social proof ✔ Strong hook (first 3 seconds decide everything) ✔ Clear offer (discount, urgency, bonus) ✔ Native-style creatives (UGC works best) ✍️ 6. Ad Copy (Sell Emotion, Not Just Product) ✔ Address real customer pain points ✔ Focus on benefits, not features ✔ Strong CTA: Shop Now / Order Today 📊 7. Optimization (Data > Emotions) ✔ Test continuously ✔ Kill losing ads (no conversions = no mercy) ✔ Scale winners gradually ✔ Track key metrics: CPA, ROAS, CTR, CPM 🔁 8. Retargeting (Recover Lost Revenue) ✔ ATC retargeting (3–14 days) ✔ Checkout retargeting (high intent users) ✔ Offer-based retargeting (discount + urgency) 📈 9. Scaling (Smart Growth Only) ✔ Vertical scaling: Increase budget 20–30% ✔ Horizontal scaling: Duplicate winning ad sets ✔ Keep testing new creatives to avoid fatigue 💡 Final Thought: Winning in Meta Ads is not about hacks… it’s about execution. If you fix: 👉 Tracking 👉 Creatives 👉 Funnel You’ll automatically improve ROAS. If you’re running ads and not seeing results, save this checklist and audit your account today. 💬 Need help scaling your eCommerce brand? Let’s connect #MetaAds #FacebookAds #EcommerceGrowth #PerformanceMarketing #DigitalMarketing