Using Customer Data To Boost Ecommerce Sales

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.

I remember years ago working with a coffee brand, and we discovered some fascinating insights from analyzing customer buying behavior. We had two types of purchases: subscriptions and one-time buys. When we dug into the data, we found a significant pattern. Only 18% of one-time buyers made a second purchase. But if they did, there was an 85% chance they’d order a third time, and the repeat order rate stayed high after that. This showed us a major bottleneck. The founder initially wanted to focus all incentives on attracting first-time buyers, but the data told a different story. We saw the value in driving that crucial second purchase. So, we overhauled our approach: 1. Revamped Fulfillment Kits: The first order kit included incentives for a second purchase. 2. Updated Email Campaigns: Emails were tailored to encourage a second buy. The results? We boosted the second purchase rate to nearly 30%, leading to a significant increase in overall sales and customer lifetime value (LTV). Even with pushing more people into that second order, we only saw a small dip in the number of people who went from a 2nd to a 3rd order, moving from 85% to 83%. This experience shows the power of slicing your data by cohorts to uncover bottlenecks and then addressing them directly. Sometimes, the biggest gains come from focusing on the steps beyond the initial sale.

Ever watch a brand spend $500K on "growth hacks" only to acquire low-value, one-time purchasers? True story from last week... A DTC brand was hemorrhaging cash on Meta ads. 70% CAC increase YoY. 🥵 "We need better customers, not just more customers," the CEO told me. So we dug into their data. Here's what we found: Their top 20% of customers were driving 67% of revenue. And they all shared something interesting... These weren't the customers coming from their influencer campaigns or flash sales. They were coming from a tiny email segment: "Early Access VIPs" who'd bought their hero product at full price. The fix? We rebuilt their entire acquisition strategy around Customer Value Optimization (CVO). Here's the framework: 1. Identify your BEST customers (not just any customer) 2. Reverse engineer everything to get more of them Results after 90 days: - CAC down 41% - AOV up 28% - Repeat purchase rate: +15% The brands crushing it right now? They're all doing some version of this: > Vuori knows their highest-value customers start with men's ABC pants > Skims found their "fits everybody" line creates the most loyal customers > Athletic Greens identified their "travel packs" as the gateway to subscription Here's why this works: Every dollar spent acquiring a high-value customer compounds. They buy more, return less, and bring their friends. Want the exact playbook we used? Drop a "+" below and I'll share the step-by-step process we used to identify and scale their best customer segment. #ecommerce #dtc #customeracquisition #growth

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