Analyzing Ecommerce Website Traffic

Out with Adobe’s forecast for the 2025 holiday shopping season. The headline: we expect U.S. e-commerce to cross $250B (up 5.3% YoY). Noteworthy:   - Cyber Week is expected to drive 17.2% of overall spend this season, at $43.7 billion (up 6.3% YoY) – see below. - For the 2025 season, we expect AI traffic to rise by 520% YoY, peaking in the 10 days leading up to Thanksgiving. - Mobile will cement its position as the dominant shopping platform this season, set to drive a record 56.1% share of online spend (vs. desktop shopping). This represents $142.7 billion (up 8.5% YoY).   Ton more insights, all based on Adobe Analytics data and analyzing over 1 trillion visits to U.S. retail sites, 100 million SKUs and 18 product categories: https://adobe.ly/48del60

CMOs want pipeline. CFOs want unit economics. Marketers tend to segment with metrics like customer count, ACV, or win rate. These are good at first. But they’re incomplete. The next level is to segment like a CFO Customer Lifetime Value (CLV) is a great bridge. CLV doesn’t just measure deal size or ease of closing. It captures *the full value* of a customer or segment over time: initial purchase, gross margin, retention, and expansion. It’s a great metric to tie marketing strategy to business outcomes. Here's an example... Which customer would you rather acquire? Customer A - $120K ACV. - Closed in 60 days - Costs $60K/yr to serve. - Churns in year 2. Customer B - $60K ACV. - Closed in 90 days - Costs $20K/yr to serve. - Expands in year 2 to $80K. - Expands in year 3 to $100K. Clearly B is more valuable in the long-term. The 5-year value (CLV) is ~6x higher. But a lot of times this dynamic gets missed when thinking about ICPs and segments because we stop with pipeline metrics. CLV helps divide your market by long-term value. This is especially key in an ABM motion where you are making big investments into relatively small segments of accounts. You want to spend resources on the accounts that your CFO will love. Want help measuring CLV by segment? DM me. I'm thinking I'd make a template for this during the holidays. #B2B #marketing #sales

Is your D2C brand really growing or are you just spending more to make less? Most founders think sales are the only success metric. But if you’re not tracking these 6 numbers daily, you’re not scaling, you’re surviving. 1. Customer Acquisition Cost (CAC) Every new customer costs you. If this number is rising, your profits are dying. Dial in your targeting, creative, and retention. 2. Conversion Rate 10,000 visitors mean nothing if they don't buy. Are your landing pages optimized? Is your checkout process smooth? A 1% jump in conversion = a major revenue boost. 3. Average Order Value (AOV) Want to earn more without new customers? Focus on AOV. Bundles, upsells, and time-limited deals can easily 2x this metric. 4. Customer Lifetime Value (LTV) A great D2C brand turns one-time buyers into loyal fans. Aim for LTV that’s 4–5x your CAC. If it’s not, prioritize retention strategies like loyalty programs, re-engagement flows, and post-purchase content. 5. Return on Ad Spend (ROAS) If your ads aren’t returning profit, you're just burning cash. Test daily. Better creative, sharper offers, tighter targeting. 6. Refund & Return Rate High returns = leaking revenue. Understand the why. Quality issues? Misleading product descriptions? Fix it now. Track these. Improve these. Scale smart. Which of these metrics are you ignoring today? #businesscoach #businesstips #D2C #founders

Most eCommerce brands obsess over revenue and ROAS. But the real game is in the metrics no one talks about. Here are 10 overlooked KPIs that actually drive growth (and how to optimize them): ~~ 1. LTV:CAC Ratio (The Ultimate Health Check) LTV:CAC = Customer Lifetime Value ÷ Customer Acquisition Cost 1:1 = You’re bleeding money 3:1 = Healthy 5:1+ = Printing cash If you’re below 3:1, either: ✅ Lower CAC (better targeting, UGC ads, referrals) ✅ Increase LTV (subscriptions, upsells, memberships) == 2. 90-Day Repurchase Rate If a customer doesn’t buy again within 90 days, they probably won’t. Fix it by: • Winback campaigns with targeted incentives • Selling bundles that create habits • Building a loyalty program that rewards repeat buyers == 3. Contribution Margin (What’s Actually Left?) CM = Revenue – (COGS + Shipping + Discounts + Ad Spend) If your CM is under 30%, you’re scaling a business that won’t survive. Get margins up by: • Cutting discount dependency • Negotiating lower fulfillment costs • Adding Onward shipping protection == 4. Subscription Churn Rate (The Silent Killer) High churn = your brand is a leaky bucket Fix it by: • Adding pause & skip options via SMS (Skio for example) • Add more delivery options and product variety • Sending an email 7 days before renewal reminding them potential lost perks == 5. Time to Second Purchase (T2P) Track how long it takes for a customer to place their second order—then cut that time in half. Tactics to speed it up: • AI-based Email/SMS flows with hyper-targeted recommendations • Exclusive discounts for second-time buyers • Reorder reminders based on average usage time == 6. Gross Margin per Order (The Scaling Checkpoint) At scale, 40%+ gross margins keep you profitable. If you're below that: • Increase prices (test 10% bumps) • Reduce discounting, do Cashback instead (@ Onward) • Negotiate better supplier terms (carrier rates, 3pl, etc) == 7. Refund & Return Rate A high return rate = a CAC multiplier. Fix it by: • Charging for returns (but offering free exchanges) • Clearer product descriptions & sizing charts • Post-purchase emails on how to use the product == 8. Organic vs. Paid Revenue Ratio If 60%+ of your sales come from paid ads, you’re in trouble. Brands with real staying power win on organic channels. The fix? • SEO & content marketing • Affiliate & referral programs • Retention tactics (VIP, loyalty, subscriptions) == 8. SKU Concentration Risk If 80%+ of your revenue comes from one product, you’re vulnerable. Great brands expand without overextending. Turn one-time buyers into multi-SKU customers with: • Bundles • Exclusive add-ons • Subscription perks == 9. % of Revenue from Returning Customers A healthy DTC brand makes 40%+ of revenue from repeat buyers. If you’re below that, focus on LTV levers: • VIP memberships • Personalized email/SMS offers • Post-purchase nurture flows Follow Josh Payne for deep dives on DTC, SaaS, and investing.

Breaking New Ground in AI-Powered Recommendations: KERAG_R Framework The landscape of recommendation systems is experiencing a paradigm shift with the integration of Large Language Models (LLMs). Researchers from the University of Glasgow have introduced KERAG_R (Knowledge-Enhanced Retrieval-Augmented Generation for Recommendation), a groundbreaking approach that addresses a critical limitation in current LLM-based recommendation systems. >> The Core Challenge Traditional LLM-based recommenders suffer from a fundamental weakness: lack of domain-specific knowledge. While these models excel at contextual understanding, they often "hallucinate" when making recommendations due to insufficient relational knowledge about items in their pre-training corpus. >> Technical Innovation Under the Hood KERAG_R introduces three sophisticated components working in harmony: > Graph Retrieval-Augmented Generation (GraphRAG) - Leverages a Graph Attention Network (GAT) to pre-train item and entity embeddings from knowledge graphs - Implements intelligent triple selection using dot product similarity scores combined with attention weights - Retrieves only the most relevant knowledge graph triples for each user interaction, eliminating noise and redundancy > Knowledge-Enhanced Prompt Construction - Integrates structured relational knowledge directly into LLM prompts - Combines collaborative filtering signals from traditional recommenders (LightGCN) with knowledge graph information - Supports both triple format ("item-relation-entity") and natural language sentence representations > Knowledge-Enhanced Instruction Tuning - Fine-tunes Llama-3.1-8B-Instruct using LoRA (Low-Rank Adaptation) for computational efficiency - Incorporates relational knowledge during the tuning stage rather than just at inference - Optimizes for listwise ranking tasks using cross-entropy loss >> Performance Breakthrough The results speak volumes: KERAG_R significantly outperforms ten state-of-the-art baselines across three public datasets, achieving up to 14.89% improvement over the previous best LLM-based model, RecRanker, on the Amazon-Book dataset. Key Technical Findings: - Single knowledge graph triple per item interaction proves optimal - Triple representation format outperforms natural language sentences - GAT-based triple selection substantially improves over random selection >> Why This Matters This research represents a crucial step toward more intelligent, context-aware recommendation systems that can leverage structured knowledge effectively. By bridging the gap between symbolic knowledge representation and neural language models, KERAG_R opens new possibilities for personalized AI applications across e-commerce, content platforms, and beyond. The work demonstrates that the future of recommendation systems lies not just in scaling model parameters, but in intelligently integrating external knowledge sources with advanced retrieval mechanisms.

📌📊 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

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

How we've leveraged insights from LinkedIn Ads data to narrow in on our ICP When we first launched Impactable's LinkedIn ad service, our approach was "the broader, the better." Like most early startups, we believed that anyone with a business online was a potential client. Over time, the demographic data told a different story. Targeting everyone diluted our impact. Analyzing click-through rates, conversions, and client intake forms began to paint a clearer picture of who was truly benefiting from our services. This insight was a game-changer. We began to pivot, concentrating on sectors where we saw the most traction and intentionally stepping back from markets that, while initially appealing, didn't align with our strengths as we scaled. This strategic shift wasn't just about cutting out less profitable sectors; it was about doubling down on where we could make the most significant difference. By niching down based on data insights, we could tailor our services, hone our expertise, and ultimately, deliver more value to our clients. The shift wasn't just about who we chose to serve but about becoming the best at serving them. #DataDrivenDecisions #MarketingStrategy #LinkedInAds #NicheMarketing

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

Vector Databases: The Math Behind Modern Recommendation Systems 🚀 Just published a new technical deep dive on vector databases and why they're revolutionizing recommendation engines: 🔍 Core Concept: Vector databases store entities (products, users, content) as high-dimensional arrays where mathematical proximity = conceptual similarity 🧮 Technical Advantage: Unlike traditional databases that excel at exact matching (WHERE category='shoes'), vector DBs optimize for finding "similar" items through cosine similarity metrics 💻 Implementation: Simple but powerful data structure: struct VectorDb { vectors: HashMap<u64, Vec<f32>>, dimension: usize } 📈 Business Impact: Major platforms attribute 35-75% of engagement to recommendation engines powered by these systems 🔑 Key Insight: The computational complexity reduction from O(n) to O(log n) through specialized indexing makes similarity search viable at massive scale 💡 Practical Applications: E-commerce: "Similar products" increasing cart size by 30%+ Content platforms: Personalized discovery without explicit preferences Social networks: User similarity for community building 📱 Getting Started: Exact search viable to ~100K items before requiring approximate methods - perfect for many applications Want to dive deeper? I break down the mathematical foundations and implementation patterns in: 🎧 Podcast: https://lnkd.in/e8RAB2vc 📝 Blog: https://lnkd.in/ekTNcg_5 #VectorDatabases #RecommendationSystems #MachineLearning #DataScience #Rust