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.

{🤹♀️ E-commerce Metrics} What matters at each stage There are tons of metrics, but you don't need to juggle them all. Focus on the important stuff at each stage of development for a smoother ride to success. 🌱 When you have less than 500 orders per month: 1/ Conversion rate → [❤️ healthy value on this stage = 2%-5%] 2/ Customer Acquisition Cost (CAC) → [$10-$30] 3/ Customer Feedback and Satisfaction → [NPS = 20-30] 🧭 When you have 500-1,500 orders per month: 1/ Average Order Value (AOV) → [$100-$150] 2/ Return on investment (ROI) → [300%-500%] 3/ Inventory Turnover Ratio → [4-6 times per year] 🚗 1,500-3,000 orders per month: 1/ Customer Lifetime Value (#LTV) → [Order fulfillment accuracy = >$300] 2/ Churn Rate → [5%-8%] 3/ Traffic Sources and Channels → [Share of top-performing channels 30%-40%] 🚀 3,000-10,000 orders per month: 1/ Repeat Purchase Rate → [20%-30%] 2/ Operational Efficiency Metrics → [Fulfillment time = 1-2 days; Support resolution time = <24 hours] 3/ Market Expansion Metrics → [Growth rate = 15%-20%] 🏆 >10,000 orders per month: 1/ Supply Chain Performance → [Inventory turnover ratio = 8-10 times per year; Order fulfillment accuracy = 99%] 2/ Global Expansion Metrics → [Growth rate = 20%-30%] 3/ Brand Equity and Recognition → [Brand NPS = 40-50] By paying close attention to these #metrics as your company grows, you'll be able to make smart choices that lead to lasting success and scalability. Or you think these sets should be changed? #ecommerceanalytics

🚨The greatest drop-off is from Product Details Page To Cart Page, so we must improve our Product Details Page! Not so fast ✋ In today's age of data obsession, almost every company has an analytics infrastructure that pumps out a tonne of numbers. But rarely do teams invest time, discipline & curiosity to interpret numbers meaningfully. I will illustrate with an example. Let's take a simple e-commerce funnel. Home Page ~ 100 users List Page ~ 90 users Product Display Page ~ 70 users Cart Page ~ 20 users Address Page ~ 15 users Payments Page ~12 users Order Confirmation Page ~ 9 users A team that just "looks" at data will immediately conclude that the drop-off is most steep between Product Details Page & Cart Page. As a consequence they will start putting in a lot of fire power into solving user problems on Product Display Page. But if the team were data "curious", would frame hypothesis such as "do certain types of users reach cart page more effectively than others?" and go on to look at users by purchase buckets, geography, category etc and look at the entire funnel end to end to observe patterns. In the above scenario, it's likely that the 20 cart users were power users whilst new & early purchasers don't make it to this stage. The reason could be poor recommendations on the list page or customers are only visiting the product display page to see a larger close up of the product. So how should one go about looking at data ? Do ✅ Start with an open & curious mind ✅ Start with hypothesis ✅ Identify metrics & counter metrics that will help prove/disprove hypothesis ✅ Identify the various dimensions that could influence behaviours - user type, geography, category, device type, gender, price point, day, time etc. The dimensions will be specific to your line of business. ✅ Check for data quality and consistency ✅ Look at upstream and downstream behaviour to see how the behaviour is influenced upstream and what happens to the behaviour downstream. ✅ Check for historical evidence of causality Dont ❌ Look at data to satisfy your bias ❌ Rush to conclude your interpretation ❌ Look at data in isolation - - - TLDR - Be curious. Not confirmed. #metrics #analytics #productmanagement #productmanager #productcraft #deepdiveswithdsk

A client came to us frustrated. They had thousands of website visitors per day, yet their sales were flat. No matter how much they spent on ads or SEO, the revenue just wasn’t growing. The problem? Traffic isn’t the goal - conversions are. After diving into their analytics, we found several hidden conversion killers: A complicated checkout process – Too many steps and unnecessary fields were causing visitors to abandon their carts. Lack of trust signals – Customer reviews missing on cart page, unclear shipping and return policies, and missing security badges made potential buyers hesitate. Slow site speeds – A few-second delay was enough to make mobile users bounce before even seeing a product page. Weak calls to action – Generic "Buy Now" buttons weren’t compelling enough to drive action. Instead of just driving more traffic, we optimized their Conversion Rate Optimization (CRO) strategy: ✔ Simplified the checkout process - fewer clicks, faster transactions. ✔ Improved customer testimonials and trust badges for credibility. ✔ Improved page load speeds, cutting bounce rates by 30%. ✔ Revamped CTAs with urgency and clear value propositions. The result? A 28% increase in sales - without spending a dollar more on traffic. More visitors don’t mean more revenue. Better user experience and conversion-focused strategies do. Does your ecommerce site have a traffic problem - or a conversion problem? #EcommerceGrowth #CRO #DigitalMarketing #ConversionOptimization #WebsiteOptimization #AbsoluteWeb

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

Inflation often forces businesses into a dilemma—raise prices and risk losing customers, or keep prices stable and shrink margins. But what if data could help strike the perfect balance? 🚀 Challenge: Flipkart, one of India’s largest e-commerce platforms, noticed fluctuating customer retention rates and declining repeat purchases, especially during inflationary periods. Traditional deep-discount campaigns led to short-term sales spikes but failed to build long-term customer loyalty. 🔎 Solution: Data-Driven Discounting Strategy Flipkart’s analytics team uncovered a key insight: Small, frequent discounts (e.g., 5-10% on repeat purchases) led to higher engagement. Personalized offers based on purchase history encouraged repeat buys. A/B testing revealed that customers preferred consistency over occasional deep discounts. 💡 Implementation: Using AI-driven dynamic pricing, Flipkart rolled out: ✅ Tiered discounts for loyal customers. ✅ AI-powered coupon recommendations. ✅ Targeted email campaigns promoting small, time-sensitive discounts. 📈 Results: After three months of testing, Flipkart saw: ✔️ 17% increase in repeat purchases ✔️ 12% uplift in customer retention ✔️ Higher profit margins vs. deep discounting 🎯 Key Takeaway: In an inflationary environment, data-driven pricing isn't just about maximizing revenue—it’s about customer psychology. Businesses that personalize their offers and optimize discounts intelligently can boost retention while protecting margins. 𝑾𝒉𝒂𝒕 𝒑𝒓𝒊𝒄𝒊𝒏𝒈 𝒔𝒕𝒓𝒂𝒕𝒆𝒈𝒊𝒆𝒔 𝒉𝒂𝒗𝒆 𝒘𝒐𝒓𝒌𝒆𝒅 𝒇𝒐𝒓 𝒚𝒐𝒖𝒓 𝒃𝒖𝒔𝒊𝒏𝒆𝒔𝒔 𝒊𝒏 𝒄𝒉𝒂𝒍𝒍𝒆𝒏𝒈𝒊𝒏𝒈 𝒕𝒊𝒎𝒆𝒔? #datadrivendecisionmaking #DataAnalytics #DiscountStrategy #BusinessStrategies

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

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.

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