Audience Cohort Analysis

It's common to see cumulative revenue curves for freemium digital products fitted across groups of users to singular point estimates at various cohort days, eg., Day 90. This can be helpful for guidance, but there are a few drawbacks to approaching the aggregation in this way: - it masks variation in payer behavior and the vast differences in values across the two classes of users; - the singular point estimate is misleading and may inspire too much confidence for certain use cases (eg., setting advertising bids). Another way of approaching the analysis is to segment users by cohort in some defined time window, truncate their individual cumulative revenue curves so that they're all of the same length, and to treat each individual curve as a time series that a curve can be fitted to. Those fitted individual cumulative revenue curves can then be projected and bootstrap sampled, and confidence intervals can be constructed on the means. I published the first in a multi-part series about this concept this week. In the next part, I'll explore using fixed effects to control for hierarchical variation across cohorts or acquisition sources.

How I Went From Reporting Numbers to Driving Strategy Last week, my post about failing a data analyst interview reached over 18,000 impressions. Many of you asked, "How exactly are you bridging that gap?" Here's the honest breakdown, no fluff, just what's working. THE PROBLEM I IDENTIFIED: I was stuck in descriptive analytics (what happened?) while businesses needed prescriptive analytics (what should we do?). I could tell you sales dropped 15% last quarter. But I couldn't explain: • WHY it dropped (diagnostic) • WHICH customers might churn (predictive) • WHAT actions to take (prescriptive) That's the gap I'm closing. WHAT I'M LEARNING: Instead of just mastering more tools, I'm learning strategic frameworks that change how I view data: 1. RFM Analysis (Recency, Frequency, Monetary)* Segments customers into Champions, At-Risk, Lost, and Potential Loyalists.   Example: "These 12% of customers generate 34% of revenue but haven't purchased in 60 days; a retention campaign is needed." 2. Customer Lifetime Value (CLV) Predicts the long-term value of customer segments.   Shifts focus from single transactions to relationship value. 3. Cohort Analysis Tracks customer groups over time and reveals retention patterns.   Example: "Q1 customers have 40% better retention than Q3; what did we do differently?" 4. Churn Prediction Identifies at-risk customers before they leave.   Example: Customers with 3+ support tickets and expiring contracts have a 67% churn risk. 5. Market Basket Analysis Reveals products bought together for cross-selling strategies. Example: 80% of customers who buy Product A also buy Product B within 30 days THE MINDSET SHIFT: Before: Looking at data and asking, What can I calculate? Now: Looking at business challenges and asking, What data do I need to solve this? I've learned to think in four levels: Level 1 (Descriptive): Sales decreased 15% Level 2 (Diagnostic): Top 3 customers cut orders by 40% Level 3 (Predictive): We'll likely lose 2 more major customers in Q1  Level 4 (Prescriptive): Launch a targeted retention campaign. Estimated ROI: 3.5x" Most analysts stop at Levels 1-2. The job market rewards Level 3-4 thinking. RESOURCES HELPING ME: Learning: • Kaggle Learn - Free short courses   • Mode Analytics SQL Tutorial - Advanced SQL techniques   • StatQuest YouTube - Statistics explained simply   • Google Data Analytics Certification - Solid foundation  Practice: • Kaggle datasets - Real messy data to work with   • Maven Analytics - Free datasets with business context   Currently reading Storytelling with Data by Cole Nussbaumer Knaflic  TO EVERYONE WHO REACHED OUT: Your messages reminded me that I'm not alone in this journey. My challenge: Pick ONE framework, find a Kaggle dataset, build something this weekend, and share what you learned. Let's level up together. #DataAnalytics #CareerDevelopment #LearningInPublic #DataScience #BusinessIntelligence

Most audits stop at the ad platform. That is where the biggest mistakes begin. Last week, I reviewed a subscription CPG brand that looked healthy on the surface. Revenue was growing. The dashboard showed a few “winners.” Under the hood, a different story: CAC on new customers was far above target 60 plus ad sets with too many ads per set, so no real learning Platform “cost per purchase” mixed new and returning buyers, which hid the truth Here is the exact process I used to get clarity: Step 1. Rebuild the picture from finance, not the ad platform Pull total marketing spend, total revenue, and calculate true MER. Split prospecting spend and new-customer revenue to get aMER. Step 2. Separate cohorts Create cohorts for evergreen buyers, promo buyers, and recent seasonal buyers. Track LTV by cohort at 30, 60, 90, and 180 days. Do not let promo cohorts inherit core LTV. Step 3. Tie CAC to contribution margin and payback Define contribution margin after COGS, shipping, discounts, and processing. Set a break-even CAC per cohort based on your target payback window. Step 4. Fix the testing structure Move to a disciplined setup: 3 to 5 ads per ad set, one concept per ad set, clear kill targets, and winners promoted to scale campaigns using the same post IDs. Step 5. Read time frames like an operator Audit performance in yesterday, 3-day, 7-day, and 14-day windows. If a creative looks fine at 14 days but is sliding at 3 days, it is fatigued. Rotate it before it drains margin. What changes after using this workflow: CAC decisions align with contribution margin, not vibes aMER exposes where new-customer growth was actually sustainable The account starts learning again because the structure forced equal spend and faster feedback Scaling is not about hunting for a hero ad. It is about building a system where every dollar has a job and every cohort has a plan. If you want the one-page template I use for MER, aMER, contribution margin, and cohort LTV, comment COHORT in the comments and I'll send it over.

Is one of your goals to improve the quality of your acquisition pipeline? Your first step should be cohort reporting. Topline weekly or monthly metrics tell you how much you’re growing. They don’t tell you much about whether that growth is any good. Cohorts do. A cohort report groups users based on when they signed up or a shared trait, then tracks how that group behaves over time. Viewed cumulatively or over time, this makes it much easier to see whether today’s acquisition decisions actually hold up. But they get even more valuable when you start slicing cohorts further based on specific traits. Here are three of my personal favourite views to get you started: ⸻ 1/ Acquisition source cohorts Decision: Should we scale this channel, fix it, or stop it? Two sources can drive similar signup volume and look fine in aggregate. But when you break them into cohorts, the differences show up quickly: • Some sources create users who stick around and form habits • Others spike signups and fade • Others, while small in volume, could yield an outsized conversion impact Without cohorts, it’s easy to keep feeding channels that feel productive but don’t compound. ⸻ 2/ User segment or complexity cohorts Decision: Who are we actually building for? Cohorting by user type (individuals, SMBs, teams, etc.) usually shows very different activation and retention curves. Often, the largest segment by volume is not the one that performs best over time. This is where teams realize they’ve been optimizing onboarding, messaging, or pricing around the easiest users, not the most durable ones. ⸻ 3/ Early product behaviour cohorts Decision: What should users do first? Who a user is matters, but what they do in their first session often matters more. Early setup choices and first actions tend to correlate strongly with long-term behaviour. Cohorting on those early actions shows which paths lead to habit formation and which ones stall or drop out of your product flow entirely. ⸻ Now start digging in!

I have acquired 4M+ newsletter subscribers, and one of the biggest MYTHS I see is that “low CPL is always good.” Here’s an example of why that’s wrong + how not to fall into that trap: For context, a large publisher came to us excited: They were buying subscribers at a $1.50 CPL. Dashboard looked great. But sponsors started to churn. Why? Those cheap subscribers didn’t click on sponsor links. Engagement lagged, sponsor ROI fell, and the “win” turned into a revenue hole. That’s the trap: CPL tells you what you paid. It doesn’t tell you what you bought (quality or not). We rebuilt their performance system around LTV, and it doubled their CTR and subsequently sponsorship revenue. Here’s the playbook we used: 1. Cohorting What to do: • Don’t look at the whole list. Group subscribers by cohort (month/week they signed up) • Track opens, clicks, and unsubscribes by cohort over time Why it matters: • Aggregate metrics mask shifts. A cheap ad can flood your list with low-engagement readers, and the average looks “fine,” until sponsors start complaining. • Cohorts reveal the true path: which ads bring engaged readers and which bring dead weight. How to implement: • Export subscribers by signup date from your ESP • Build a simple sheet with: cohort, CPL, opens (30/60/90 days), CTR (30/60/90 days), unsubscribe rate • Compare cohorts of the same age (e.g., 30-day performance for January cohort vs. February cohort) 2. Bounded LTV The problem: Older cohorts have had more time to generate revenue, so raw LTV comparisons are unfair. Solution: Measure bounded LTV (e.g., value per cohort in their first 30–45 days) Here’s how: a) Decide the window (30 or 45 days) b) Calculate revenue-per-open (example): CPM = $40 → revenue per 1,000 opens = $40 → $0.04 per open. If a cohort opens 11.25 emails in month 1 (45% open rate × 25 sends) → month revenue ≈ $0.45. c) Sum the first 30–45 days (and optionally project 1 year or 2 years once you have retention curves) Why this helps: • You get an actionable snapshot to compare ad concepts quickly. • If cohort A’s bounded LTV is 30% higher than cohort B’s, you can reallocate spend now. No need to wait a year. 3. Click Score When you can’t wait for full LTV, use a simple, quick metric: Click Score. Formula : Click Score = CPL/CTR Why it works: CPL alone misses quality. CTR alone misses cost. The ratio balances cost vs. initial engagement, which correlates with downstream LTV. Example: Ad A: CPL = $1.50, CTR = 3% (0.03) → Click Score = 50 Ad B: CPL = $1.60, CTR = 6% (0.06) → Click Score ≈ 26.7 Both ads cost roughly the same, but B pulls far more engaged readers per dollar.

You’re analyzing product engagement trends — and the numbers tell a concerning story. User retention at Day 30 has dropped from 45% to 28%. The startup founder wants answers before the next product sprint. “Where are users falling off? Is it onboarding? Feature fatigue? Timing?” Retention rate is an average. It hides everything. Cohort analysis shows the real problem. This is a cohort analysis problem Here's the 5-step proven framework we used: 1/ Segment by signup month → Isolate cohorts Shows if it's a product issue (all cohorts declining) or timing issue (recent cohorts fine) 2/ Calculate days since signup → Bucket into windows (0-7, 8-14, 15-30, 31+) Reveals the exact day users disappear 3/ Build the heatmap → Plot cohorts vs. retention windows The cliff pattern tells you everything (onboarding issue? Feature fatigue? Timing?) 4/ Segment by plan & geography → Find which users hurt most Free tier dropping 28% but Enterprise at 65%? Your pricing tier is the problem. 5/ Interview the droppers → Validate hypotheses Data shows WHERE they leave. Interviews show WHY. The Cliff Pattern: Our users dropped off between Day 15-30. That's the aha moment phase. Meaning: They get past initial exploration but don't hit the habit-forming feature. What This Meant: Not an onboarding problem (would see Day 7 cliff). Not a pricing problem (Pro tier is better, so it's not "too expensive"). It's a feature discovery problem. Users aren't finding the thing that makes us unique. What We Did: 👉 Added guided tour at Day 7 👉 Created email nudge at Day 10 highlighting power users' most-used feature 👉 Shipped contextual help for that feature 👉 Ran cohort test on new signups Results: New cohort (Oct) → 35% Day 30 retention (vs. 22% in Sept) Not there yet, but moving.

Cohort Analysis: step-by-step guide I use this to group users by shared traits and track their behavior over time. It's key to understanding retention, engagement, and trends. Group users by traits like signup date or first feature used. 𝐖𝐡𝐲 𝐔𝐬𝐞 𝐈𝐭?: Measure retention and feature adoption to optimize user experience. Let me walk you through the steps.. 𝟏. 𝐃𝐞𝐟𝐢𝐧𝐞 𝐘𝐨𝐮𝐫 𝐂𝐨𝐡𝐨𝐫𝐭 ⤷ How to Define a Cohort: Group users by signup date or feature usage. ⤷ Example: Create cohorts based on the week of signup to track retention over time. 𝟐. 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐢𝐧𝐠 𝐘𝐨𝐮𝐫 𝐂𝐨𝐡𝐨𝐫𝐭 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 ⤷ Track User Behavior: Measure how cohorts engage with your product over time. ⤷ Retention Metrics: Track how many users return after 1 day, 7 days, and 30 days. 𝟑. 𝐓𝐫𝐚𝐜𝐤 𝐑𝐞𝐭𝐞𝐧𝐭𝐢𝐨𝐧 𝐎𝐯𝐞𝐫 𝐓𝐢𝐦𝐞 ⤷ Tracking Rates: Compare retention rates across different time windows. ⤷ Analyze Trends: Identify which cohorts are more engaged and when they drop off. 𝟒. 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐢𝐧𝐠 𝐘𝐨𝐮𝐫 𝐃𝐚𝐭𝐚 ⤷ Visual Tools: Use heatmaps and line charts to display retention. ⤷ Quick Insights: Visuals help spot trends and communicate findings easily. 𝟓. 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 ⤷ Segment by Behavior: Create cohorts based on feature adoption or user demographics. ⤷ Example: Compare retention for users who adopted a key feature versus those who didn’t. Cohort analysis helps you understand user behavior and retention patterns. Use this data to make data-driven decisions and improve your product. #product #productanalyst

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

📌 Power BI Breakdown # 1: Cohort Analysis Last week, I shared a breakdown on how to analyze customer retention using a cohort analysis. Many people asked—how can we actually implement this in Power BI? So, I built this demo template to show you its potential. A lot of tech companies struggle with customer retention because they: ⤷ Can’t pinpoint when and why customers churn ⤷ Have difficulty tracking cohort performance across different time periods ⤷ Lack clear visualizations to present retention data to investors But realistically, what data do you need to build something like this? Setting up cohort analysis in Power BI doesn’t require complex data engineering. In fact, it’s easier than you think if you already have a data warehouse or an analytical database. All you need to do is bring together key datasets from different sources: 🔹 Your payment system (Stripe, PayPal, etc.) → To track customer transactions and lifetime value 🔹 Your CRM or database → To link transaction data with customer profiles 🔹 Marketing sources (Organic, Referrals, etc.) → To analyze how different acquisition channels impact retention Once you merge these datasets, you can segment customers into cohorts based on their signup month, first purchase, or other key milestones. The ultimate goal of this analysis is to help you move beyond vanity metrics like total revenue and look at real retention trends over time. ✅ Identify when and why customers drop off ✅ Justify investor decisions with clear retention metrics Live Demo: https://lnkd.in/ej4a5wKm If you want the PBIX template file ⤷ Leave a comment, and I’ll send it over! (Must be connected so I can DM you.) #PowerBI #DataAnalytics #BusinessIntelligence