Using Data To Identify New Ecommerce Opportunities

Amazon just launched Discover Unmet Demand inside Product Opportunity Explorer. And it is showing something most sellers have never been able to see clearly before. Here is what it actually is. Instead of tracking one keyword at a time it groups similar customer search terms into a cluster and shows you where conversion is falling below the benchmark for that product type and price range. That benchmark is the key part. Amazon is not comparing your data to the whole marketplace. It is comparing it to products in the same category at the same price point. That is accurate signal, not noise. Take the whipped tallow balm cluster right now. 298,730 impressions. 3,815 clicks. 43.39% click rate. 14.15% purchase rate. 6.14% search conversion rate. Buyers are searching. They are clicking. But conversion is sitting below benchmark. That gap between intent and purchase is the opportunity. And because you are looking at a cluster not a single keyword the pattern is reliable. One keyword can spike for seasonal reasons and look like a trend. A cluster shows you what is actually happening in buyer behavior across the whole category. What this data tells you practically: High impressions low clicks across the cluster, main image problem. Everyone is appearing but nobody is winning the click. High clicks low purchase rate, listing problem. Buyers land but the content is not converting confidence into a sale. Conversion below benchmark with strong search volume, real product gap. Something buyers want that the market is not delivering well yet. This is the data that tells you whether to enter a niche, fix your listing, or build a product nobody has gotten right yet. Most sellers look for high volume. Smart sellers look for high intent with low conversion. That gap is where brands get built. Are you using Product Opportunity Explorer in your research process?

What if you could identify, at a ZIP code level, exactly where your best customers live and where your next "best markets" are emerging? That’s what we set out to do this quarter with a geospatial analysis 🌎 for one of our clients. Part 1: Geo-Spatial Analysis (partner KnoWhere Analytics) Part 2: Incrementality Testing (partner Stella | Growth Intelligence) 𝗧𝗵𝗲 𝗴𝗼𝗮𝗹 𝘄𝗮𝘀 𝘀𝗶𝗺𝗽𝗹𝗲: 👉 Identify ZIP codes across the US with 𝙪𝙣𝙧𝙚𝙖𝙡𝙞𝙯𝙚𝙙 𝙨𝙖𝙡𝙚𝙨 𝙤𝙥𝙥𝙤𝙧𝙩𝙪𝙣𝙞𝙩𝙮 👉 Build a smarter pool of high-potential customers to make Q4 as impactful as possible Here is how it works ⬇️ 𝗜𝗻𝗽𝘂𝘁𝘀: ▪️ 2-3 years of customer data (Shopify, BigCommerce, etc) ▪️ 2020 & 2023 Census data (population, income, demographic data) ▪️ Custom Development Index derived from federal imagery and national atlas datasets 𝗢𝘂𝘁𝗽𝘂𝘁𝘀: 🔵 National coverage, ZIP code-level scoring system broken into 3 groups & 3 tiers (e.g. Strong Tier 1, As Expected, Opportunity Tier 2) 🔵 General Targeting Score (GTS) - a full ranking of all zip codes within the nation and propensity for sales 🔵 Full range of high resolution maps and clickable KML files for Google Earth to aid visualization 🌎 (see image attached) 🔵 Key Audience (sales drivers) variable importance list 𝗧𝘂𝗿𝗻𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗜𝗻𝘁𝗼 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀: Once the model runs, it tells you which variables are most correlated to your customers - because this is independent of surveys, ad platform data, and other information, the result is an independent validation of current understanding of the audience, and provides unique insight into consumer qualities. Maybe it’s ZIPs with more renters, and maybe it’s zips with a certain range of renters to buyers. Maybe it’s higher education levels or higher income clusters. The result is the ability to see your customer DNA, geographically mapped across the U.S. 𝗛𝗼𝘄 𝗪𝗲 𝗨𝘀𝗲𝗱 𝗜𝘁: That’s where things get fun. For this client, we identified 1.6K ZIP codes classified as “Opportunity” areas. Those zip codes were match markets to our top-performing ZIPs, but they were underindexing with sales. Next, we took those 1600 zip codes and ran an incrementality test across Meta and YouTube. Then we used Stella to measure lift. There are many tests for this model and data which can be tested and applied: ▪️Use the Opportunity ZIPs for out-of-home or direct mail testing ▪️ Double down on your strongest ZIPs for customer acquisition and retention during promo 𝗔 𝗻𝗼𝘁𝗲 𝗼𝗻 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 𝗶𝘁𝘀𝗲𝗹𝗳: Our end goal was to find 𝗼𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝘆 𝗺𝗮𝗿𝗸𝗲𝘁𝘀 to better direct ad spend. And while we’re always trailing real data, we think because demographic structures shift slowly; relative differences between ZIPs remain meaningful. If you’re interested in Google Ads, or about running one of these analyses, feel free to connect with me. Banfana, Stella | Growth Intelligence

🛒 How Basket Analysis Can Drive eCommerce Growth: A Bangladeshi Scenario As eCommerce continues to grow rapidly in Bangladesh, businesses are dealing with more and more customer data. One of the most valuable and often overlooked ways to make sense of that data is through basket analysis. Whether you’re working at a platform like Daraz, Chaldal, Pickaboo, or even running your own online shop, basket analysis can help uncover what products people are buying together. These insights can help you make smarter decisions when it comes to marketing, product placement, bundling, and personalized offers. 🔍 What is Basket Analysis? Basket analysis (also known as market basket analysis) is a method used to find associations between products based on customer purchase history. For example: - What do people usually buy with rice? - Are customers who buy smartphones also buying covers or screen protectors? - Are snack items more popular during weekends? By identifying patterns like these, eCommerce platforms can: - Increase average order value - Run more effective cross-sell campaigns - Deliver personalized recommendations - Make better inventory decisions 🧺 Real-Life Example: A Case Based on Chaldal While analyzing data from Chaldal, one of Bangladesh’s largest online grocery platforms, we noticed something interesting. Many customers in areas like Dhanmondi and Mirpur were buying instant noodles and tomato ketchup together, especially during the evening. This pattern suggested a common need: quick dinner solutions, likely for students or working professionals. Based on this insight, we tested a few simple strategies: - Introduced a combo offer with noodles and ketchup - Showed both products in the “Frequently Bought Together” section - Ran targeted push notifications in the evening with a message like “Need a quick dinner? Grab our Noodles + Ketchup combo now!” The early results were promising: - Better product visibility - More engagement during evening hours - A small bump in basket size for repeat users We’re still monitoring the data, but it’s a great example of how even small insights can be turned into smart decisions. 💡 Final Thoughts You don’t need AI or complex tools to start using basket analysis. A simple SQL query or spreadsheet analysis can help you uncover product relationships that lead to real business value. #eCommerce #BasketAnalysis #DataAnalytics #DigitalBangladesh #CustomerInsights #BusinessGrowth #SQLforBusiness #OnlineGrocery #MarketingStrategy #StartupBangladesh

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

💪 David v Goliath.... ... How to compete using a Smart Data Strategy... The biggest brands in the category can often easily outspend competitors when it comes to investment in data & insight, and this can give them a clear competitive edge. Smaller businesses are unlikely to be able to match their spend, but they can spend *smarter* to compete more effectively. Here’s how: 🚀  1. Start with High-Impact Data ↳ Market Overview Reports: Affordable sources like Mintel or Euromonitor provide a snapshot of market size, trends & competitor positioning. This helps identify category trends & establish the right areas or Shoppers to target without the ongoing cost of continuous data feeds. ↳ Focus on Key Business Questions: Pinpoint where insight will make the biggest impact e.g. - Detailed understanding of Retailer category performance ahead of a range review to help secure new distribution. - Identifying target consumers & optimal outreach strategies to boost penetration. 🔍  2. Leverage Selective EPOS & Loyalty Data ↳ Market-Level EPOS Data: This can be invaluable for insight into category dynamics & benchmarking KPIs vs competitors whilst avoiding high costs of retailer-specific feeds. ↳ Loyalty Card Data: Although this will only cover one retailer (so no total market read) it can give you very granular insights on sales performance as well as WHO is buying your brand. 🎯 3. Focus on Actionable Insights ↳ Prioritize Impactful Data: Concentrate on insights that can directly drive product development, pricing & promotions. Avoid ‘nice-to-have’ data that doesn’t materially impact your business. ↳ Make the most of the data you need DO have: Manage scope to only buy the data you *need* & make sure each source is *fully* mined. Investing time in analysis instead of buying new data can yield deeper understanding & more opportunities to optimise your brand performance. 📈 4. Scale Data Investments with Business Growth ↳ Mix One-Off & Continuous Feeds: Start with one-off data sources, then add targeted continuous data feeds as you scale. Regularly review usage & actionability & stop reports which don't add value. 🧠 5. Outsmart, Don’t Outspend --> Be Agile ↳ Develop a *Learning* culture : Smaller businesses can move around the Build/Measure/Learn loop much faster than bigger brands - Insight is the rocket fuel you need to power this. Key Takeaway: Strategic Data Use Although small & medium sized businesses will inevitably have less data, if they use what they can afford to answer the right questions & act quickly to execute then they can find a competitive edge of their own. What are your thoughts & experiences - let us know in the comments. Want to find out more? This week's #CategoryWins newsletter digs into this subject in much more detail : See link in comments or my bio ♻️ & if you enjoyed this post, please like & share it with your network. #CategoryManagement #FMCG #CPG #DataStrategy #CompeteSmarter