Utilizing Data For Ecommerce Decision Making

Most ecommerce brands report from the outside in. They obsess over the edge - ROAS, CTR, and CPC - and simply hope those clicks eventually turn into a profitable business. High-performing DTC brands work differently. They build from the inside out, starting with the Unit Economics; LTV, CAC and CM. The 3-Layer Ecommerce Reporting System: Layer 1: Unit Economics. If this is broken, scaling ads just kills the business faster. Metrics: LTV, CAC, LTV:CAC, Payback Period (90/180 days), Contribution Margin (after COGS & Shipping), Cohort Retention. Layer 2: Operational Metrics. This is how you manage the machine. Metrics: New vs. Returning Customers, Marginal CAC, Paid vs. Organic mix, Inventory. Layer 3 are Campaign Metrics. They can be misleading, if read the wrong way. But still important to track. Metrics: ROAS, CTR, Add-to-Cart Rate, Hook Rate. This is the difference between a top-tier ecommerce brand, and everyone else. Comment ECOM + connect with me and I’ll send you my ecommerce tech stack guide.

For half a decade, I thought I was tracking the right metrics I was wrong Revenue. Growth rate. ROAS. Conversion rate. New customers. Repeat revenue All important But they could tell me the business was growing without telling me whether that growth was making the company more valuable You can buy more traffic, discount more aggressively, and acquire less-profitable customers while the top line keeps going up The business gets bigger That doesn’t automatically mean its equity value does A stronger Brand should make future revenue easier to earn, more profitable, and less dependent on buying every sale Here are the 11 metrics I wish I’d started tracking sooner, framed as questions: 1. Are branded organic searches growing faster than revenue? 2. Are contribution dollars and contribution margin going up? Contribution Dollars = Revenue - variable costs like COGS, marketing, and shipping 3. Is direct and branded search revenue growing faster than overall revenue? 4. Is the gap between gross and net sales shrinking? This signals less reliance on discounts and fewer returns 5. Are 30, 60, and 90-day incremental LTV going up, excluding the first purchase? 6. Is reach growing as fast as—or faster than—revenue? 7. Have your worst days gotten better? One way to measure this: is the average of your 30 lowest-revenue days trending up? 8. For organic search, is revenue per session rising while sessions are growing or stable? 9. Is your share of branded organic searches growing versus your competitive set—at both the Brand and category level? 10. Is Baseline Revenue growing, both in dollars and as a percentage of total revenue? I define Baseline Revenue as revenue from direct traffic, organic search, and organic social referrals It’s imperfect. But if it’s rising in dollars AND as a percentage of revenue, good things are generally happening 11. Is Baseline Revenue per branded organic search going up? Branded searches are an imperfect proxy for the Brand you’re building. Baseline Revenue per search shows whether you’re monetizing it better If searches are soaring but Baseline Revenue per search isn’t, that’s something to audit — A few caveats: None of these metrics are perfect. You can game any of them They’re also mostly leading indicators—not the ultimate company scorecard The ultimate outcome is more operating profit and net cash over time The right metrics also change with the company’s stage, economics, and strategy. A five-month-old company shouldn’t use the same scorecard as a 100-year-old company But if you can honestly answer “yes” to most of these questions, there’s a good chance the quality of your growth is improving And that gives you a better chance of building a more valuable company—not just a bigger one Question for the people of the internet: What else do you track to understand whether growth is increasing the quality and equity value of the business?

I have spent years in the highs and lows of the consumer goods industry but never seen a pricing climate quite like this. Manufacturers are getting squeezed from every direction-tariffs, skyrocketing raw material costs, and relentless supply chain disruptions. The old playbook of raising prices to cover costs? That’s dead. Why? Because consumers are feeling the pressure too. A 2024 Nielsen report makes it clear: today’s shoppers are scrutinizing every dollar they spend, and brands that aren’t strategic about pricing risk losing market share fast. Here’s what I’m seeing from top CPG brands that get it: 1️⃣ Walmart is investing heavily in AI-driven pricing models to keep costs competitive-e-commerce now makes up 18% of total revenue. 2️⃣ PepsiCo is doubling down on pack-size innovation, offering smaller, affordable options to maintain volume without excessive discounting. 3️⃣ Luxury brands are using price elasticity models, testing demand thresholds before rolling out increases-avoiding consumer pushback. 4️⃣ Supply chain resilience is non-negotiable. Companies are shifting manufacturing away from China, despite short-term cost spikes, to avoid future geopolitical risks. The smartest brands aren’t just reacting. They’re rethinking. They’re moving toward Revenue Growth Management (RGM) frameworks that help them: ✅ Optimize pricing and promotions (because blanket price hikes are a losing game) ✅ Focus on margin-smart growth, not just revenue ✅ Leverage data analytics to make smarter, faster pricing decisions Brands that don’t evolve risk eroding profitability or pricing themselves out of the market. CPG leaders who master strategic pricing, operational efficiency, and consumer-driven value creation will own the future of this industry. Are you adjusting your strategy, or just reacting to rising costs? Because in 2025, only the most adaptable brands will win. #CPG #FMCG #PricingStrategy #RevenueGrowth #ConsumerGoods

Knowledge Graphs (KGs) have long been the unsung heroes behind technologies like search engines and recommendation systems. They store structured relationships between entities, helping us connect the dots in vast amounts of data. But with the rise of LLMs, KGs are evolving from static repositories into dynamic engines that enhance reasoning and contextual understanding. This transformation is gaining significant traction in the research community. Many studies are exploring how integrating KGs with LLMs can unlock new possibilities that neither could achieve alone. Here are a couple of notable examples: • 𝐏𝐞𝐫𝐬𝐨𝐧𝐚𝐥𝐢𝐳𝐞𝐝 𝐑𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬 𝐰𝐢𝐭𝐡 𝐃𝐞𝐞𝐩𝐞𝐫 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬: Researchers introduced a framework called 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐆𝐫𝐚𝐩𝐡 𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐀𝐠𝐞𝐧𝐭 (𝐊𝐆𝐋𝐀). By integrating knowledge graphs into language agents, KGLA significantly improved the relevance of recommendations. It does this by understanding the relationships between different entities in the knowledge graph, which allows it to capture subtle user preferences that traditional models might miss. For example, if a user has shown interest in Italian cooking recipes, the KGLA can navigate the knowledge graph to find connections between Italian cuisine, regional ingredients, famous chefs, and cooking techniques. It then uses this information to recommend content that aligns closely with the user’s deeper interests, such as recipes from a specific region in Italy or cooking classes by renowned Italian chefs. This leads to more personalized and meaningful suggestions, enhancing user engagement and satisfaction. (See here: https://lnkd.in/e96EtwKA) • 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠: Another study introduced the 𝐊𝐆-𝐈𝐂𝐋 𝐦𝐨𝐝𝐞𝐥, which enhances real-time reasoning in language models by leveraging knowledge graphs. The model creates “prompt graphs” centered around user queries, providing context by mapping relationships between entities related to the query. Imagine a customer support scenario where a user asks about “troubleshooting connectivity issues on my device.” The KG-ICL model uses the knowledge graph to understand that “connectivity issues” could involve Wi-Fi, Bluetooth, or cellular data, and “device” could refer to various models of phones or tablets. By accessing related information in the knowledge graph, the model can ask clarifying questions or provide precise solutions tailored to the specific device and issue. This results in more accurate and relevant responses in real time, improving the customer experience. (See here: https://lnkd.in/ethKNm92) By combining structured knowledge with advanced language understanding, we’re moving toward AI systems that can reason in a more sophesticated way and handle complex, dynamic tasks across various domains. How do you think the combination of KGs and LLMs is going to influence your business?

As an analyst, I was intrigued to read an article about Instacart's innovative "Ask Instacart" feature integrating chatbots and chatgpt, allowing customers to create and refine shopping lists by asking questions like, 'What is a healthy lunch option for my kids?' Ask Instacart then provides potential options based on user's past buying habits and provides recipes and a shopping list once users have selected the option they want to try! This tool not only provides a personalized shopping experience but also offers a gold mine of customer insights that can inform various aspects of a business strategy. Here's what I inferred as an analyst : 1️⃣ Customer Preferences Uncovered: By analyzing the questions and options selected, we can understand what products, recipes, and meal ideas resonate with different customer segments, enabling better product assortment and personalized marketing. 2️⃣ Personalization Opportunities: The tool leverages past buying habits to make recommendations, presenting opportunities to tailor the shopping experience based on individual preferences. 3️⃣ Trend Identification: Tracking the types of questions and preferences expressed through the tool can help identify emerging trends in areas like healthy eating, dietary restrictions, or cuisine preferences, allowing businesses to stay ahead of the curve. 4️⃣ Shopping List Insights: Analyzing the generated shopping lists can reveal common item combinations, complementary products, and opportunities for bundle deals or cross-selling recommendations. 5️⃣ Recipe and Meal Planning: The tool's integration with recipes and meal planning provides valuable insights into customers' cooking habits, preferred ingredients, and meal types, informing content creation and potential partnerships. The "Ask Instacart" tool is a prime example of how innovative technologies can not only enhance the customer experience but also generate valuable data-driven insights that can drive strategic business decisions. A great way to extract meaningful insights from such data sources and translate them into actionable strategies that create value for customers and businesses alike. Article to refer : https://lnkd.in/gAW4A2db #DataAnalytics #CustomerInsights #Innovation #ECommerce #GroceryRetail

Many founders treat pricing as a revenue optimization problem. Figure out the product first, scale usage, then monetize. That's backwards. Pricing isn't about extracting money. It's about discovering whether you built something people actually value. At Gamma, we used pricing as a proxy for value and kept it pretty much the same for over 2 years. Free usage will lie to you (especially for B2B and prosumer products). Usage spikes feel like PMF. They're not. Usage without payment tests your onboarding, not your value. If you come out with too generous of a free plan, you'll never know what true willingness to pay looks like. Here's how to use pricing as a proxy for value: 1. Pick your value metric Choose the thing customers actually hire you for. Documents generated. API calls. Minutes transcribed. At Gamma, we gated by AI credits as the primary value metric, with business levers like custom branding. 2. Draw a hard boundary between free and paid Let people experience the "aha," then stop them at a generous but bounded gate. We gave users plenty of AI credits up front. Once they hit the limit: upgrade for access to more AI. 3. Research your range, then let behavior decide We used Van Westendorp to find our starting range. Ask users four price points: too cheap to trust, good value, getting expensive, too expensive to consider. Plot where these intersect to bracket your range. Then test a few prices within it. Research shows what people say they'll pay - conversion shows what they actually do. We watched free-to-paid conversion and early churn signals, picked the winner, and moved on. 4. Instrument retention and talk to customers Track whether paid users keep crossing your value threshold each week. Stay close to customers through power-user communities or direct outreach. Ask questions like: "What job were you hiring us for?" and "What would justify a higher price?" 5. Treat pricing changes like product pivots Once you've validated pricing, the only reason to change it is if you've fundamentally changed what you're selling. We haven't changed ours in two years because the value metric (AI usage) hasn't changed. Constantly repricing means you're still searching for product-market fit. Why this matters: Pricing early clarifies who values you, which channels convert, and which segments to double down on. You're better off launching pricing way earlier so you can see who's actually willing to pay for it.

Crowning a New Term: “Iceberg Metrics” 🧊 ✨ I’m calling it: Iceberg Metrics represent KPIs that only reveal the tip of what’s really happening below the surface. Metrics like abandoned carts seem simple but often mask much more—checkout friction, hidden costs, trust issues, and more. To truly understand and optimize, we need to dig deeper. Here’s how to dive into the “iceberg” of abandoned cart rates: 1. Establish Baseline Metrics: Start by gathering data on current abandoned cart rates, session times, and bounce rates using heat maps and session recordings to see where users drop off. 2. Segment the Audience: Analyze users by behavior (first-time vs. repeat visitors, mobile vs. desktop) and traffic source (organic, paid, email). 3. Experiment Hypotheses: Develop hypotheses for abandonment reasons—shipping costs, checkout friction, distractions, or lack of trust signals—and test them. 4. Run A/B Tests: Test variations like simplifying the checkout process, showing shipping costs earlier, adding trust badges, or retargeting abandoned cart emails. 5. Use Heat Maps & Session Recordings: Examine user behavior in real time. Look for confusion or hesitation, where users hover, and whether they engage with key information. 6. Contextualize Results: Analyze how changes impact overall user flow. Did simplifying checkout help, or did other metrics like bounce rate increase? 7. Ecosystem Approach: Examine how tweaks affect the full journey—from product discovery to checkout—balancing short-term improvements with long-term goals like lifetime value. 8. Iterate: Refine solutions based on experiment findings and continuously optimize the customer journey. This one’s mine, folks! #IcebergMetrics #OwnIt #DataDriven #EcommerceOptimization #NewMetricAlert Cheers, Your cross-legged CAC and CLV buddy 🤗

Exciting breakthrough in e-commerce recommendation systems! Walmart Global Tech researchers have developed a novel Triple Modality Fusion (TMF) framework that revolutionizes how we make product recommendations. >> Key Innovation The framework ingeniously combines three distinct data types: - Visual data to capture product aesthetics and context - Textual information for detailed product features - Graph data to understand complex user-item relationships >> Technical Architecture The system leverages a Large Language Model (Llama2-7B) as its backbone and introduces several sophisticated components: Modality Fusion Module - All-Modality Self-Attention (AMSA) for unified representation - Cross-Modality Attention (CMA) mechanism for deep feature integration - Custom FFN adapters to align different modality embeddings Advanced Training Strategy - Curriculum learning approach with three complexity levels - Parameter-Efficient Fine-Tuning using LoRA - Special token system for behavior and item representation >> Real-World Impact The results are remarkable: - 38.25% improvement in Electronics recommendations - 43.09% boost in Sports category accuracy - Significantly higher human evaluation scores compared to traditional methods Currently deployed in Walmart's production environment, this research demonstrates how combining multiple data modalities with advanced LLM architectures can dramatically improve recommendation accuracy and user satisfaction.

One of the best conversion wins? Actually listening to your customers. It’s easy to get caught up in optimising buttons, headlines, and landing pages. But often, the real answers are already out there — if you know where to look. Last month, a founder I work with was stuck at a 2% conversion rate. Instead of diving straight into CRO tools, we did something simple: 𝐒𝐩𝐨𝐤𝐞 𝐭𝐨 15 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫𝐬 𝐰𝐡𝐨 𝐡𝐚𝐝 𝐫𝐞𝐜𝐞𝐧𝐭𝐥𝐲 𝐛𝐨𝐮𝐠𝐡𝐭. What we learned: 💡 Their biggest buying fear wasn’t addressed anywhere 💡 The pricing page created confusion rather than clarity 💡 The language on the site didn’t match how customers talked But we didn’t stop there. We also layered in 𝐬𝐨𝐜𝐢𝐚𝐥 𝐥𝐢𝐬𝐭𝐞𝐧𝐢𝐧𝐠 — pulling insights from reviews, competitor reviews, social posts, and forums — to add a broader view on top of the direct conversations. The result? Depth from interviews. Scale from social data. A full picture of what customers really needed. And after updating the messaging, 𝐜𝐨𝐧𝐯𝐞𝐫𝐬𝐢𝐨𝐧𝐬 𝐣𝐮𝐦𝐩𝐞𝐝 𝐟𝐫𝐨𝐦 2% 𝐭𝐨 7.8%. No ad spend. No new tools. Just better understanding. Real growth starts when you stop guessing and start listening — properly. When’s the last time you checked not just what your customers say to you… but what they’re saying when they think you’re not listening? #CustomerInsights #GrowthStrategy #ConversionRateOptimisation

Inflation isn’t just an economic challenge—it’s a test of agility for businesses. As costs rise and purchasing power shifts, companies that rely on gut instinct risk falling behind. The real winners? Those who use data-driven insights to navigate uncertainty. 1️⃣ Understanding Consumer Behavior: What’s Changing? Inflation reshapes spending habits. Some consumers trade down to budget-friendly options, while others delay non-essential purchases. Businesses must analyze: 🔹 Spending patterns: Are customers shifting to smaller pack sizes or private labels? 🔹 Channel preferences: Is there a surge in online shopping due to better deals? 🔹 Regional variations: Inflation doesn’t hit all demographics equally—hyperlocal data matters. 📊 Example: A retail chain used real-time sales data to spot a shift toward economy brands, allowing it to adjust promotions and retain price-sensitive customers. 2️⃣ Pricing Trends: Data-Backed Decision-Making Raising prices isn’t the only response to inflation. Smart pricing strategies, backed by AI and analytics, can help businesses optimize margins without losing customers. 🔹 Dynamic pricing models: Adjust prices based on demand, competitor moves, and seasonality. 🔹 Price elasticity analysis: Determine how much a price hike impacts sales before making a move. 🔹 Personalized discounts: Use customer data to offer targeted promotions that drive loyalty. 📈 Example: An e-commerce platform analyzed customer behavior and found that small, frequent discounts led to better retention than infrequent deep discounts. 3️⃣ Demand Forecasting & Inventory Optimization Stocking the right products at the right time is critical in an inflationary market. Predictive analytics can help businesses: 🔹 Anticipate demand surges—especially in essential goods. 🔹 Optimize supply chains to reduce excess inventory and prevent stockouts. 🔹 Reduce waste in perishable categories like F&B, where price-sensitive demand fluctuates. 📦 Example: A leading FMCG brand leveraged AI-driven demand forecasting to prevent overstocking of premium products while ensuring budget-friendly variants were always available. 💡 The Takeaway Inflation isn’t just about rising costs—it’s about shifting consumer priorities. Companies that embrace data-driven decision-making can optimize pricing, fine-tune inventory, and strengthen customer loyalty. 𝑯𝒐𝒘 𝒊𝒔 𝒚𝒐𝒖𝒓 𝒃𝒖𝒔𝒊𝒏𝒆𝒔𝒔 𝒂𝒅𝒂𝒑𝒕𝒊𝒏𝒈 𝒕𝒐 𝒊𝒏𝒇𝒍𝒂𝒕𝒊𝒐𝒏𝒂𝒓𝒚 𝒑𝒓𝒆𝒔𝒔𝒖𝒓𝒆𝒔? 𝑨𝒓𝒆 𝒚𝒐𝒖 𝒖𝒔𝒊𝒏𝒈 𝒅𝒂𝒕𝒂 𝒕𝒐 𝒓𝒆𝒇𝒊𝒏𝒆 𝒚𝒐𝒖𝒓 𝒔𝒕𝒓𝒂𝒕𝒆𝒈𝒚? 𝑳𝒆𝒕’𝒔 𝒅𝒊𝒔𝒄𝒖𝒔𝒔 𝒊𝒏 𝒕𝒉𝒆 𝒄𝒐𝒎𝒎𝒆𝒏𝒕𝒔! #datadrivendecisionmaking #dataanalytics #inflation #inventoryoptimization #demandforecasting #pricingtrends