The Importance Of Cohort Analysis In Ecommerce Segmentation

For the last 6 years, I've taught a class at LBS on applied data science, AI and customer value management. For the next few weeks, I plan to share the main ideas that have resonated with students. 𝗜𝗱𝗲𝗮 #𝟭: 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀𝗲𝘀 𝗹𝗶𝘃𝗲 𝗼𝗿 𝗱𝗶𝗲 𝗯𝘆 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗵𝗲𝗮𝗹𝘁𝗵 𝗮𝗻𝗱 𝗱𝘆𝗻𝗮𝗺𝗶𝗰𝘀 𝗼𝗳 𝘁𝗵𝗲𝗶𝗿 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗯𝗮𝘀𝗲. A challenge for all businesses is setting an achievable plan, and the key to this is to understand how much of this year’s revenue and profit will come from the existing customer base and how many new customers are required to deliver the plan.  Unfortunately, for many businesses, this often ends up being an exercise in multiplication rather than based on a fundamental understanding of customer dynamics.  Customer cohorts are the key to make sense of customer behaviour. And here I am defining cohorts as a group of customers acquired in a particular period. A segment can represent any group of customers but cohorts have a specific meaning and a special role. Why?  • Isolates Behavioural Changes: Unlike demographic analysis, cohorts prevent confusing changes in your customer base composition with actual shifts in individual customer behaviour. Seeing your "age 25-34" spending increase might just mean you acquired more high-spending new customers in that group, not that existing customers are spending more. Cohorts show the true story of a specific group acquired at the same time.  • Tracks Customer Lifecycle: Demographics are static, but customers evolve. Cohorts follow the same group over time, revealing their lifecycle patterns and how their behaviour changes as they stay with you.  • Measures Impact of Interventions: Launched a new campaign? Cohort analysis lets you compare the behaviour of customers acquired before and after, helping you isolate the true impact of your efforts.  • Simple and Powerful: Cohorts are MECE (mutually exclusive and collectively exhaustive), persistent (membership doesn't change), and interpretable (easy to understand), making them a robust starting point for analysis.  • Diagnoses Performance Issues: Cohorts immediately help distinguish between acquisition and retention challenges. If it's a retention problem, are all cohorts affected (market issue) or just specific ones (acquisition quality issue)?  • Fundamental Truth: In customer-centric businesses, new customers are acquired over time, and cohorts naturally decline. Overall growth is the balance of these dynamics, making time-based cohorts the basic ground truth. #CustomerBaseAudit Bruce Hardie Peter Fader Daniel McCarthy https://lnkd.in/e_vUzSfD https://lnkd.in/edK_aDky https://lnkd.in/e9d3uX-U

This is the most important table in e-commerce—but no one ever talks about it and it's costing you MILLIONS. It's not a cap table. It's not an AOV table. It's the returning customer cohort table. It shows by month acquired, how much customers are worth on first order and each month thereafter. Why it's the most important thing in ecom: 1. True Customer Value Revealed A $50 first order may become $120 over 6 months. This changes everything - suddenly, that "expensive" acquisition cost is a bargain. Many brands have ROAS targets that are too high, they aren't accounting for 60-90D value. 2. Market Domination Justify higher CAC by looking at long-term value. If 90-day value is $200, you can afford $100 CAC while competitors cap at $50. Dominate your market. 3. Cohort Analysis Insights Discover which channels bring high-value customers. FB ads might cost more but deliver 3x lifetime value vs. Google. Optimize spend accordingly. 4. Cash Flow Management Predict payback periods accurately. If cohorts show 60-day breakeven, confidently reinvest every two months. Scale aggressively but safely. 5. Product Strategy Identify which products create loyal customers. If Product A has 70% retention vs 30% for B, prioritize A in marketing and development. 6. Forecasting Precision If cohorts consistently grow 20% monthly, project revenue 6-12 months out with confidence. Plan inventory, hiring, and expansion strategically. Master the cohort table to build a customer value engine that compounds over time. This is how category-defining brands are built. Not by having the highest ROAS.

The Power of Cohort Analysis: Unlocking Deeper Insights In data analytics, understanding not just what is happening but why can make all the difference. Enter Cohort Analysis—a powerful technique that segments data into groups (or cohorts) based on shared characteristics, allowing us to track and compare behaviors over time. Example: While working on customer retention for an e-commerce client, we applied cohort analysis to see how different customer groups (cohorts) behaved after their first purchase. By segmenting users based on the month they made their first purchase, we uncovered that customers acquired during a major sale had lower repeat purchase rates. This insight led us to refine our post-sale engagement strategies, tailoring them specifically for these cohorts. As a result, we improved retention by 15% in just three months. Cohort analysis goes beyond surface-level metrics, offering actionable insights into customer behavior, product usage, and more. If you're not leveraging this technique, you might be missing out on critical opportunities to optimize your business strategy

You’re spending six figures a month. ROAS looks healthy. CAC isn’t going haywire. Everything seems fine… until you zoom out. Some of your best-performing audiences might actually be the worst for long-term growth. They convert quickly, but they never return. That’s where cohort analyses come in. It shows you the difference between what looks good in-platform and what actually drives sustainable growth. We had a client with a hero product that Meta loved. Low CPAs, decent ROAS, and steady conversions. Naturally, the algorithm kept pushing more budget there. But when we looked at the cohorts, we saw the problem. Customers who bought that product rarely came back. Meanwhile, there was another product with worse performance on the surface. Higher CPA, weaker ROAS on first purchase. But the customers it attracted? They were loyal. They returned to purchase more of that product and other products from the brand. We restructured campaigns to focus on that product instead. The short-term efficiency dipped a bit, but revenue became more stable and LTV grew. It was a much healthier foundation to scale from. Cohort analysis isn’t just about looking backward. It’s about making smarter decisions going forward. If you’re not doing it, you may be optimizing for the wrong outcome.

Seniority of data people doesn't (shouldn't?) depend on tools, years of experience or degrees But rather on the sophistication of their answers in simple business questions ❓ Sales dropped 10% this quarter. Why❓ 🟥 𝐉𝐮𝐧𝐢𝐨𝐫 "I looked at sales data. Biggest drop was in Athens by 15% and Product B by 20%. Here's a chart showing monthly sales decline. It seems that sales dropped mainly because of fewer orders." 📜 Why it's junior: - Just numbers - Limited data exploration - Key takeout could be deducted by any random non-data person who just has common sense 🟨 𝐌𝐢𝐝 "Sales dropped 10%, mainly driven from Athens which also shows a 12% increase in customer churn. I also found that competitor promotions increased during the same period using market data. A correlation analysis suggests competitor discounts have an impact on user retention. So competition and loss of key customers contributed significantly in the drop" 📜 Why it's mid: -Diagnostic analysis -Use of external data -Key takeout isn't sophisticated enough, but remains solid and supported by reasonable assumptions / stats 🟩 𝐒𝐞𝐧𝐢𝐨𝐫 "The -10% sales decline is primarily driven by a +12% customer churn among our top-tier segment in Athens. Cohort analysis shows that customers acquired 12-18 months ago are churning at a higher rate due to recent competitor pricing strategies. Predictive models indicate that unless retention improves, we may lose an additional 5% next quarter. I recommend immediate targeted retention campaigns for the top decile customers in that region and a reassessment of our competitive pricing strategy. Additionally, I’ve identified early churn indicators we can track going forward." 📜 Why it's senior: -Good summary at the top (Minto pyramid anyone?) -Focus on business impact -Predictive and causal thought process -Proactive and actionable recommendations -Strategic thinking beyond the immediate question 💡 Long story short, if the key findings and suggested actions of an analysis are based on rough estimates and common sense rather than precise numbers, provable assumptions and cross-departmental inputs, "data-driven" approach is an overstatement. Just ask a random employee next time to save time #data #analytics #seniority #sales_are_down_because_orders_dropped #we_should_limit_costs

Why Cohort Analysis Unlocks True Retention Insights 🚀 We recently ran a cohort analysis in AMC to measure not just how many customers buy, but how many come back. Instead of looking at broad repeat rates (which often blend new and long-time buyers), this approach isolates first-time purchasers in a single month and then tracks their behavior over time. Here’s what we found looking at January 2025 first-time buyers: 🔶 1,442,328 customers made their very first purchase with the brand in January. 🔶 440,385 of those customers returned within 2 months. 🔶 408,808 returned again within 4 months. 🔶 238,677 returned again within 6 months. This isn’t just a measure of volume — it’s a direct look at customer stickiness and the long-term impact of acquisition campaigns. But the real power of cohort analysis is how it guides audience strategy: 🔸 Exclusions: If you know a product typically lasts 4–6 months, you can exclude recent purchasers from campaigns in that window, avoiding wasted impressions. 🔸 Retargeting timing: Once you see when repurchase behavior spikes (e.g., months 4–6), you can retarget those exact customers with replenishment messaging right before their expected reorder. 🔸 Campaign efficiency: This ensures DSP and Sponsored Ads are working together — prospecting when buyers are new, suppressing them while they’re “in the product lifecycle,” and re-engaging them at the optimal moment to maximize LTV. By running the same query across multiple months, brands can: 🔸 Benchmark retention and spot seasonal dips. 🔸 Identify which products bring in high-LTV buyers vs. one-time shoppers. 🔸 Align DSP + Sponsored Ads investment with long-term growth, not just immediate ROAS. #DSP #AMC #Amazonads #advertising #amazondsp #BTR #BTRmedia

𝗖𝗼𝗵𝗼𝗿𝘁 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗶𝗻 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 Ever wondered what happens after customers make their first purchase? Do they come back next month? Do they stay loyal? Or do they disappear? That’s where 𝗖𝗼𝗵𝗼𝗿𝘁 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 comes in. A 𝗰𝗼𝗵𝗼𝗿𝘁 is a group of customers who made their first purchase in the same time period (for example, January 2025). Instead of analyzing all customers together, Cohort Analysis tracks each group separately over time. It helps answer: 𝗥𝗲𝘁𝗲𝗻𝘁𝗶𝗼𝗻 – How many customers return in Month 1, Month 2, Month 3? 𝗕𝗲𝗵𝗮𝘃𝗶𝗼𝗿 – Which acquisition month performed best? 𝗟𝗧𝗩 – Which cohort generates the highest lifetime value? 𝗗𝗿𝗼𝗽-𝗼𝗳𝗳 – When are customers most likely to churn? This version is built using dynamic 𝗗𝗔𝗫 + 𝗛𝗧𝗠𝗟 rendering inside Power BI, creating: • A retention heatmap (M0–M6) • Cohort-based LTV comparison • Visual retention trend tracking Found this helpful? Here’s how you can support: ➠ 𝗟𝗶𝗸𝗲 if this added value ➠ 𝗥𝗲𝗽𝗼𝘀𝘁 to share with your network ➠ 𝗙𝗼𝗹𝗹𝗼𝘄 for more advanced Power BI builds