Ecommerce Market Segmentation Analytics

POST-4/7👉 Email used to be a megaphone. In 2025, it’s a whisper in a very specific ear. Gone are the days when “blast to all” could pass as a strategy. In fact, that approach in 2025 is actively hurting your deliverability. Email Service Providers (ESPs) like Gmail, Yahoo, and Outlook are no longer just evaluating your IP health—they’re scoring your sender behavior at the recipient level. That means if 40% of your list is cold or disengaged, Gmail sees you as the problem—not just the user. ⚠️ Real Consequence: 1. We audited an ecommerce fashion brand with 220K contacts. Over 92K of them hadn’t clicked a single email in 90+ days. Gmail flagged them for bulk spam behavior, and inboxing fell from 78% to 46% overnight. 2. They were running promos weekly. Nothing was technically broken—but nothing was relevant. That’s what got them crushed. What Micro-Segmentation Solves in 2025: ✅ Reduces spam complaints ✅ Increases engagement velocity ✅ Signals positive intent to inbox providers ✅ Unlocks higher revenue per send with smaller cohorts Micro-Segmentation Tactics That Work Now: 1. Behavior-Based Journeys: Forget static tags. If someone viewed winter boots but didn’t buy, your next 3 emails better talk about warmth, snow, or style—not your general spring lookbook. ✅ Klaviyo + Shopify data lets you trigger flow branches based on: Last viewed product category Cart abandonment by SKU group Pages viewed in session (via UTMs or on-site behavior) Pro Tip: Use dynamic content blocks inside campaigns to adjust hero sections based on browse activity without cloning entire flows. 2. Lifecycle Automation by Spend Velocity This isn’t “new vs returning” logic anymore. In 2025, flows shift based on: Time since last order AOV trends SKU replenishment cycles Example: First-time customer who hasn’t returned in 30 days → “2nd purchase incentive” High-value buyer within 7 days → “VIP early access” Customer inactive 60+ days → Winback + dynamic offer block + channel sync suppression 3. AI-Supported Clustering Tools like RetentionX, Lexer, and even Klaviyo’s predictive analytics are now building multi-dimensional customer clusters using: Purchase frequency Channel source Time to second order Category loyalty It’s loyal mid-value buyers who shop monthly but only when free shipping is offered. ✅ What to do: Export these clusters to your ESP Build messaging that maps exactly to their past actions Suppress low responders from paid channels and warm email instead. Ready to Execute? Create 5 foundational micro-segments: 1. High spenders 2. First-time buyers 3. VIPs (CLV > 2.5x avg) 4. Dormant >90 days 5. Active clickers, no conversion Test 2 cadences per segment: VIPs: 4x/month + early access Dormant: 1x/month reactivation with content—not promos Use Recency, Frequency, and Monetary score buckets to tag customers and let your automations react to movement between them. #EmailMarketing #email

🛒 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

𝗧𝗵𝗲 𝗼𝗻𝗲 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗜 𝗰𝗮𝗻’𝘁 𝗴𝗲𝘁 𝗲𝗻𝗼𝘂𝗴𝗵 𝗼𝗳? Customer segmentation by size, industry, and geography. Why? Because when you stop treating all customers the same, you start growing 𝗳𝗮𝘀𝘁𝗲𝗿, more 𝗽𝗿𝗼𝗳𝗶𝘁𝗮𝗯𝗹𝘆, and with fewer 𝘀𝘂𝗿𝗽𝗿𝗶𝘀𝗲𝘀. This analysis is the unlock for: 📈 Smarter growth strategies 💰 Healthier margins 🤝 Happier customers 𝗪𝗵𝘆 𝘀𝗲𝗴𝗺𝗲𝗻𝘁 𝗯𝘆 𝘀𝗶𝘇𝗲, 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝘆, 𝗮𝗻𝗱 𝗴𝗲𝗼𝗴𝗿𝗮𝗽𝗵𝘆? ✅ 1. Sales & service effectiveness • A $250M CPG distributor in the Midwest doesn’t need or want the same approach as a $7bn manufacturer in Germany. • Segmentation helps you sell and support the right way - for the right customer. ✅ 2. Better strategic & operational decisions • Want to know which customers are high-effort but low-margin? Which industries are expanding the fastest? Which region has the stickiest customers? • Segmentation brings that clarity. ✅ 3. Improved customer experience • Customers don’t expect to be treated equally - they expect to be treated relevantly. • When all your teams understand the nuances of the customer they're serving, retention and satisfaction go up. 𝗛𝗼𝘄 𝘁𝗼 𝗱𝗼 𝗶𝘁 𝘄𝗲𝗹𝗹: 1️⃣ Group customers by: • Size (revenue or headcount) - a useful proxy for complexity • Industry (manufacturing & industrials, tech, services, life sciences & healthcare, CPG, etc.) • Geography (region, market, country) 2️⃣ For each segment, analyze: • Profitability • Support/service effort • Sales cycle and retention • Volumes, expansion or upsell potential 3️⃣ Find your high-leverage segments 4️⃣ Align GTM, finance, ops, and support around them 5️⃣ Refresh regularly - your base will evolve 𝗧𝗵𝗲 𝗕𝗼𝘁𝘁𝗼𝗺 𝗟𝗶𝗻𝗲 • Customer segmentation isn’t just a data exercise. It’s a strategic advantage hiding in plain sight. • When you know who your best customers really are - you build better, sell smarter, and scale faster. #CustomerStrategy #Operations #Finance #Growth #Segmentation #BusinessStrategy #fpanda

As promised, I’m sharing the first project from my series on the Top 5 Data Science Projects that every data scientist should tackle to gain a deeper understanding of the retail industry: Customer Segmentation using the K-means approach 🛒 Before diving into my approach, let’s first explore why customer segmentation is so important for retail businesses. Customer segmentation empowers businesses to better understand their customers, personalize experiences, and optimize strategies. For retail, this is key as it allows companies to: a. Target Specific Customer Groups: Customize marketing campaigns, products, and promotions based on customer preferences and behavior. Improve Retention: Identify loyal or high-value customers and offer personalized rewards to encourage repeat purchases. b. Optimize Resources: Adjust inventory, staffing, and other resources based on the distribution of customer segments. c. Create Personalized Experiences = Higher Conversions: Tailor experiences for different customer groups, leading to increased sales and customer satisfaction. The Approach In this project, I used Machine Learning to segment customers based on their purchasing behavior with the KMeans clustering algorithm. The goal was to identify patterns in customer data and classify them into meaningful segments for targeted strategies. Here’s a quick breakdown of the approach I followed: 1. Data Collection: I used retail transaction data from the UCI Machine Learning Repository. 2. Preprocessing: Cleaned and transformed the data, handled missing values, and dealt with outliers. After that, I standardized the features through scaling. 3. Clustering: I applied the KMeans clustering algorithm to group customers into 3 distinct segments, based on their RFM model (Recency, Frequency, and Monetary value): Recency: How recent was the customer’s last transaction? Frequency: How often does the customer make a purchase? Monetary value: How much has the customer spent in total? 4. Visualization: I visualized the segmentation using box plots, elbow curves, and cluster snapshots to better interpret the patterns within each group. Explore the Full Project Check out the full project on GitHub, where I’ve shared the code and detailed steps for replicating the analysis: Link to GitHub Project- https://lnkd.in/gKFXkFN2 Also sharing some cluster visualizations snaps below to see the results of the segmentation! ✨ Stay tuned for the next project in this series! I’ll be diving deeper into more advanced data science techniques that drive success in the retail industry. Don't miss out—follow me to get notified! #CustomerSegmentation #DataScience #MachineLearning #KMeansClustering #RetailAnalytics #DataScienceInRetail #CustomerInsights #DataAnalysis #RFMModel #MarketingOptimization #RetailStrategy

I've audited >10 Klaviyo 7 figure e-com brand accounts in the last week. The #1 issue I see popping up all the time that is costing brands $100,000s... Not segmenting customer's properly. Here's what most brands get wrong: They're either sending to their whole list. Or they're over-segmenting into dozens of micro-groups. Both approaches are killing your revenue. After generating $100M for 120+ DTC brands since 2019, here's what actually works: Realise that the Pragmatic Approach generates 45% more revenue with 67% less work. Essentially stop hyper-segmenting because you're leaving money on the table. Core High-Impact Segments: Engaged Subscribers (opened/clicked in last X days) Window Shoppers (active on site, haven't purchased) Winback Opportunities (previous customers outside repurchase window) Unengaged Subscribers (no opens/clicks in 90-180 days) That's it. The Real Numbers From Our Data: 80%-90% of buyers convert in first 24 hours of signing up Engagement drops drastically after 30 days Becomes almost negligible after 90 days Personalized emails deliver 6x higher conversion rates What Actually Matters: Email engagement segmentation Store engagement segmentation Location segmentation Product-interest segmentation Not "one-legged customers who only buy on Tuesdays." Just smart, behavior-based segmentation. (We use these same strategies with $100M attributed revenue for 100+ brands) P.S. I see 8-figure brands missing out on 20% of potential revenue from incorrect segmentation. Do you want to be one of them?

My Favorite Analyses: the Recency-Frequency matrix. This simple yet powerful framework goes beyond traditional segmentation to provide actionable insights into customer behavior. By focusing on how recently and how often customers engage with your brand, you can tailor your strategies to maximize lifetime value. Why it works: - Recency: Customers who have purchased recently are more likely to purchase again. It's a strong indicator of engagement and future behavior. - Frequency: Customers who purchase more often demonstrate loyalty and satisfaction, leading to a higher customer value. Recency and Frequency are the most important indicators of customer value, exhibiting more correlation to CLV than Monetary Value which is the third component in traditional RFM analyses. The Recency-Frequency matrix helps you categorize your customers into segments based on behaviors instead of factors like demographics or psychographics that imply actions. The analysis reveals distinct customer segments that require unique marketing strategies, including your Champions, the customers who Need Attention, and those who have Already Churned. Implementing the Matrix: Depending on the size of your customer dataset, the Recency-Frequency matrix can be built in a spreadsheet or a more hefty tool like SQL or R. - Excel/Google Sheets: Use `MAXIFS`, `COUNT`, `PERCENTRANK`, and a pivot table to build the Recency-Frequency matrix, but watch out for row limits. - SQL: Leverage functions like `DATEDIFF` and `COUNT` to calculate metrics, and segment with `NTILE`. - R: The `RFM` package handles large datasets with ease, offering advanced segmentation and visualization. This approach isn’t just theory — it’s a data-backed method for ensuring your marketing dollars are spent where they’ll make the most impact. DM me if you'd like to learn more, including the marketing strategies that I most commonly recommend for each Recency-Frequency matrix customer segment. Art+Science Analytics Institute | University of Notre Dame | University of Notre Dame - Mendoza College of Business | University of Illinois Urbana-Champaign | University of Chicago | D'Amore-McKim School of Business at Northeastern University | ELVTR | Grow with Google - Data Analytics #Analytics #DataStorytelling #MyFavoriteAnalyses #ROI #MROI

Are you struggling to identify who your customer is? Here are 5 tried & and tested ways that I've helped small business owners that work. 1. Market Research and Analysis: Conduct comprehensive market research to understand consumer behaviours, preferences, demographics, and purchasing patterns. Use surveys, interviews, and data analytics to gather insights into who is buying your products, why they are buying them, and what drives their purchasing decisions. 2. Create Customer Personas: Develop detailed customer personas that represent different segments of your target audience. These personas should include demographic information (age, gender, income), psychographic details (lifestyle, values, interests), and buying behaviour (preferences, needs, challenges). This helps in visualising and understanding your customers better. 3. Track and Analyse Sales Data: Utilise sales data and analytics tools to track and analyse customer buying behaviour. Look for patterns in purchasing frequency, preferred products, average order value, and the channels through which they make purchases (in-store, online, mobile). 4. Engage with Customers: Interact with your customers through various channels—social media, surveys, feedback forms, or direct communication—to gather their opinions, preferences, and feedback. Engaging with them helps in understanding their needs, pain points, and desires better. 5. Competitor Analysis: Analyse your competitors' customer base. Understand who their target customers are and what strategies they use to attract and retain them. This analysis can reveal potential gaps or opportunities in the market that you can capitalise on to attract a specific customer segment. By combining these methods, you can create a comprehensive understanding of your target customer, allowing you to tailor your products, marketing strategies, and customer experiences to better meet their needs and preferences. I'm Bradley, an e-commerce expert with over 25 years of retail experience. If you would like to know how I may be able to help your business, feel free to drop me a DM. We can then have a no-obligation chat together.

One of our eCommerce customers is managing over 352,512 customer *𝘴𝘦𝘨𝘮𝘦𝘯𝘵𝘴* (and they're only a team of four). 🤯 Think about for a second... The average eCommerce company might have millions of customers, but they only typically manage a small handful of segments. Sometimes they use basic demographic segmentation (male vs. female or split by ages or locations) and sometimes they use basic logic to segment customers by product category (e.g. if you bought a laptop before, you must be interested in electronics), but 352,512 customer segments is UNHEARD of. So, how do they do it? 👉 Semantic tagging. Basically, every message they send has 1-6 tags attached to it that explain what the message content is. So, for example, a message like "Our stock of Nike Air Jordans is running out! Grab yours now for 20% off! 👟" is labeled with things like: 👉 Offering: Shoes (Recommender system: Air Jordans) 👉 Brand: Nike 👉 Tone: FOMO 👉 Incentive: 20% off 👉 CTA: "Grab yours now" Then, by sending out all of these messages and tracking who responds, this customer can build really, really specific segments. (e.g., "Customer XYZ primarily responds to [Brand: Logitech] [Category: Electronics]. They're focused on [Convenience] (vs. Value) and prefer messages written in a [joyful] tone. They respond primarily to [Recommender] driven messages and [Emojis]") So, what's the point? Well, obviously, this is too much for any one team (especially a team of four!) to manage effectively. That's why they use Aampe to manage it for them. Aampe's system allows them to attach these tags to their messages, track which users respond to which messages, and then determine the next-best messages that should go to each user based on their demonstrated preferences (and the preferences of other similar users). ...and how does this pan out? Just about ~$7m in sales came just from events that happened within the first hour after an Aampe message (push, SMS, etc.) was sent. 📬 So, yeah managing over 300,000 customer segments isn't common. ...well, unless you use Aampe. 😉

Most of us think we have a clear ICP. But when you look at the pipeline? It’s a wild mix of company sizes, industries, and personas — all getting the same campaigns & pitch. 3. Some deals move fast. Others stall for months. 2. Some channels print money. Others burn cash. 1. Some personas love the product. Others ghost after a demo. This isn’t a sales problem. It’s a segmentation problem. If we don’t know who our best-fit customers are, we’re running blind. Here’s how I segment 👇 Side note: Get the spreadsheet template along with step-by-step guide from my newsletter. Click the link in my profile to get a copy. 📌 Step 1: Pull Closed-Won Deals Your best customers leave clues — follow them. - Pull closed-won deals from the last 6-12 months. - Grab key data: Job titles, company size, industry, ACV, deal cycle. - Clean up your CRM (because it’s always messy). Why? Real data > gut feelings. Sell to who’s already buying. 🔍 Step 2: Enrich Your Data CRM data alone won’t cut it. Use Clay to enrich contacts (seniority, decision-making power). Pro Tip: Integrate Keyplay to your CRM have accurate industry tags added to your account. Add growth signals (hiring, funding, ad spend). Think of it as turning an old map into GPS with live traffic. 📊 Step 3: Find Your Winning Segments Look for patterns in your best deals: - Which industries & company sizes close the fastest? - What roles drive decisions? - Which channels bring in high-ACV deals? Example: Demos from Marketing VPs at Mid-market Dental SaaS = High ACV & 2x faster close rate. When they come from Paid Channel, the sales cycles are longer compared to when they come organically. Once you see the patterns, targeting becomes easy. ❌ Step 4: Learn from Closed-Lost Deals Your losses reveal what’s broken. - Pull & enrich closed-lost deals. - Identify why deals fell through — wrong fit? Wrong persona? Budget? - Which channels did these closed lost deals come from? - Compare all of these with your closed won patterns. Red flags to watch: - High demo volume, low conversion → Fix qualification/messaging. - Some industries never close → Stop targeting them. - Prospects ghost post-demo → Value prop isn’t landing. 📈 Step 5: Prioritize, Cut, Scale Put your segments into a 2x2 matrix: - High demo volume, high conversion → Scale this segment fast. - High demo volume, low conversion → Fix qualification/messaging. - Low demo volume, high conversion → See if it makes sense to prioritize based on if you have enough time, money, and people. - Low demo volume, low conversion → Stop wasting effort. Why? More focus = more predictable pipeline 🚀 👆Link to the template along with the full guide in my latest newsletter. Grab it by clicking on the link in my profile.

Looking for some segmentation inspiration? Here are the 4 foundational types of segmentation with some marketing & technical examples for each! --- 1. Demographic Segmentation 🧑🎓 Example: A university targets potential students by advertising scholarships to applicants from low-income families based on household income data. SFMC Application: Use Data Extensions to filter contacts based on demographic criteria like income, ensuring campaigns are only sent to those who meet the criteria. --- 2. Behavioral Segmentation 🛍️ Example: An e-commerce store segments customers who abandoned their cart and sends them personalized reminders showcasing the items they left behind. SFMC Application: Implement behavioral triggers in Journey Builder to automate emails when users abandon carts. You can top it off by leveraging DESelect to cross-reference historical purchase information to increase the cart size. --- 3. Geographic Segmentation 🌍 Example: A retail chain sends promotions for winter clothing only to customers in colder regions. SFMC Application: Geographic data may be stored at the level of the customer, but also at the level of the most recent store visit. For the latter, you can create a 'recurring selection' in DESelect or otherwise automate a SQL query to feed into a Journey for near-real time targeting. --- 4. Psychographic Segmentation 🧠 Example: A fitness brand targets users interested in wellness with content about mental health and mindfulness based on their interaction with similar topics. SFMC Application: Analyze engagement data from Email Studio / query Data Views with SQL/DESelect Segment to identify interests and segment users accordingly, ensuring content resonates more deeply. --- Leveraging Salesforce Marketing Cloud and DESelect simplifies the complexity of executing these strategies, ensuring you can deliver the right message at the right time, efficiently. 📈 What's a cool strategy you've used? I'd love to hear it. #segmentation #salesforcemarketingcloud #deselect