Cohort Retention Table Generator
Generate cohort retention matrices from simple inputs. Track how different user groups retain over time and identify your best-performing cohorts.
Ready to Analyze Cohort Retention
Enter your cohort data to generate a retention table. Track how different user groups retain over time and identify your best-performing cohorts.
Define Cohorts
Add cohorts by signup month, campaign, or any grouping
Enter Retention Data
Input active user counts or retention percentages per period
Analyze Patterns
View retention curves, heatmaps, and identify best cohorts
Understanding Cohort Retention Analysis: Essential Calculations for Customer Analytics and Business Growth
Cohort retention analysis tracks how groups of users (cohorts) who share a common characteristic—typically their signup date—remain engaged with your product over time. Unlike aggregate metrics that can be misleading, cohort analysis reveals the true health of your user base by showing how specific groups behave as they age. Understanding cohort retention is crucial for students studying business analytics, customer retention, data science, and product management, as it explains how to measure retention by cohort, identify best-performing groups, and understand customer behavior over time. Cohort retention calculations appear in virtually every customer analytics protocol and are foundational to understanding customer lifetime value.
Key concepts include: (1) Cohort—a group of users who share a common attribute, usually signup date (e.g., "January 2024 signups"), (2) Period—the time interval for measuring retention (Day 1, Week 1, Month 1, etc.), (3) Retention rate—percentage of the original cohort still active at a given period, (4) Period 0—the starting point (100% by definition—the cohort just formed). Understanding these concepts helps you see why each is needed and how they work together.
Reading the retention table (also called a cohort triangle): (a) Rows represent each cohort (e.g., signup month), (b) Columns represent time periods since cohort start, (c) Cells show % of original cohort still active, (d) Colors indicate retention levels (green = high retention, red = low retention). Understanding this structure helps you see how cohort retention tables visualize customer behavior over time.
Why cohort analysis matters: (1) Reveals true trends—growing user counts can mask declining retention; cohort analysis shows whether your product is actually improving, (2) Identifies best cohorts—see which acquisition channels, product versions, or time periods produce the most engaged users, (3) Highlights drop-off points—identify critical periods where users churn (Day 1? Week 1? Month 3?) to focus retention efforts accordingly. Understanding this helps you see why cohort analysis is fundamental to customer analytics.
Typical retention patterns include: (a) Early drop—most products see significant churn in the first period (Day 1, Week 1); this is normal and represents users who tried but didn't find value, (b) Flattening curve—healthy products show retention curves that flatten over time; users who survive the early periods tend to stick around, (c) Improving cohorts—newer cohorts should ideally show better retention than older ones, indicating product improvement. Understanding these patterns helps you see what healthy retention looks like.
Input modes allow you to enter data in two ways: (1) Counts mode—enter the actual number of active users at each period (e.g., 850 users active in Month 1); the tool calculates retention percentages, (2) Percents mode—directly enter retention percentages (e.g., 85% retained in Month 1). Use counts if you have raw data; use percents if you've already calculated retention rates. Understanding this helps you see which mode to use for your data.
This calculator is designed for educational exploration and practice. It helps students master cohort retention by generating retention tables, calculating average retention by period, identifying best-performing cohorts, and visualizing retention patterns. The tool provides step-by-step calculations showing how cohort retention works. For students preparing for business analytics exams, customer retention courses, or data science labs, mastering cohort retention is essential—these concepts appear in virtually every customer analytics protocol and are fundamental to understanding customer lifetime value. The calculator supports comprehensive analysis (retention tables, averages, best cohorts), helping students understand all aspects of cohort retention.
Critical disclaimer: This calculator is for educational, homework, and conceptual learning purposes only. It helps you understand cohort retention theory, practice table generation, and explore how customer behavior changes over time. It does NOT provide instructions for actual business decisions, which require proper training, validated analytics platforms, statistical significance testing, and adherence to best practices. Never use this tool to determine actual business decisions, investment strategies, or customer retention programs without proper statistical review and validation. Real-world cohort retention analysis involves considerations beyond this calculator's scope: statistical significance testing, weighted averages, revenue per user, expansion/contraction, discounting, and cohort segmentation. Use this tool to learn the theory—consult trained professionals and validated platforms for practical applications.
Understanding the Basics of Cohort Retention Analysis
What Is a Cohort?
Cohort is a group of users who share a common attribute, usually signup date (e.g., "January 2024 signups"). Cohorts allow you to track how specific groups behave over time, revealing patterns that aggregate metrics can hide. Understanding cohorts helps you see why they're fundamental to customer analytics.
How Do You Calculate Retention Rate?
Retention rate is calculated as: Retention Rate = (Active Users at Period / Original Cohort Size) × 100%. For example, if your cohort started with 1,000 users and has 850 active in Month 1, retention = (850/1000) × 100% = 85%. Understanding this helps you see how retention quantifies customer retention.
What Does Period 0 Represent?
Period 0 represents the moment the cohort is formed—for example, when users sign up. By definition, retention at Period 0 is always 100% because all users in the cohort are "active" at the start. Period 1 is the first measurement after the cohort started (e.g., 1 month after signup). Understanding this helps you see why Period 0 is always 100%.
How Is Retention Calculated: Original Cohort vs Previous Period?
This tool calculates retention relative to the original cohort size, not the previous period. For example, if your cohort started with 1,000 users and has 500 active in Month 3, that's 50% retention—regardless of how many were active in Month 2. This is the standard approach for cohort retention analysis. Understanding this helps you see why retention is always relative to the original cohort.
What's the Difference Between Counts and Percents Input Mode?
In counts mode, you enter the actual number of active users at each period (e.g., 850 users active in Month 1); the tool calculates retention percentages. In percents mode, you directly enter retention percentages (e.g., 85% retained in Month 1). Use counts if you have raw data; use percents if you've already calculated retention rates. Understanding this helps you see which mode to use for your data.
How Do You Calculate Average Retention Across Cohorts?
Average retention is calculated as: Average Retention = Sum of Retention Rates / Number of Cohorts. For example, if Cohort A has 85% retention and Cohort B has 80% retention, average = (85% + 80%) / 2 = 82.5%. This tool uses a simple (unweighted) average across cohorts. Understanding this helps you see how average retention quantifies overall performance.
What Granularity Should You Use?
Choose granularity based on your business: Daily retention makes sense for social apps, Monthly for most SaaS, Quarterly for longer sales cycles, Yearly for enterprise products with annual contracts. The key is consistency—pick a granularity and stick with it for meaningful comparisons. Understanding this helps you see when to use each granularity.
How to Use the Cohort Retention Table Generator
This interactive tool helps you generate cohort retention tables by calculating retention rates, averages, and identifying best-performing cohorts. Here's a comprehensive guide to using each feature:
Step 1: Configure Granularity and Periods
Set up your analysis parameters:
Granularity
Select the time period: Monthly, Quarterly, Yearly, or Custom. This affects period labels (M0, M1, M2... or Q0, Q1, Q2...).
Unit Label
Enter the unit you're measuring (e.g., "users", "customers", "subscribers"). This is for labeling only.
Number of Periods
Enter how many periods to track (1-24, e.g., 6 for 6 months). This determines how many columns the table will have.
Step 2: Select Input Mode
Choose how you want to enter data:
Counts Mode
Enter the actual number of active users at each period (e.g., 850 users active in Month 1). The tool calculates retention percentages automatically.
Percents Mode
Enter retention percentages directly (e.g., 85% retained in Month 1). Use this if you've already calculated retention rates.
Step 3: Add Cohorts and Enter Data
Add cohorts and enter their retention data:
Add Cohort
Click "Add Cohort" to create a new cohort row. Each cohort needs a label (e.g., "January 2024") and an initial size (e.g., 1000 users).
Enter Retention Data
For each period, enter either the count of active users (counts mode) or the retention percentage (percents mode). Period 0 is always 100% (the cohort just started).
Example: January 2024 cohort with 1000 users, 850 active in Month 1, 720 in Month 2
Input: Label = "January 2024", Size = 1000, Values = [850, 720] (counts mode)
Output: Retention = 100% (M0), 85% (M1), 72% (M2)
Explanation: Calculator computes retention as (active/original) × 100% for each period.
Step 4: Generate and Review Results
Click "Calculate" to generate your retention table:
View Retention Table
The calculator shows: (a) Retention table with rows (cohorts) and columns (periods), (b) Retention percentages for each cohort at each period, (c) Average retention by period across all cohorts, (d) Best-performing cohort at the last period, (e) Visual heatmap with color coding (green = high, red = low), (f) Summary statistics and caveats.
Tips for Effective Use
- Ensure cohort sizes are positive—zero or negative sizes prevent calculation.
- Use consistent granularity—if using monthly, all cohorts should use monthly periods.
- Enter data for all periods if possible—missing data will show as empty cells.
- Remember that Period 0 is always 100%—the cohort just started.
- Use counts mode if you have raw user data—it automatically calculates retention percentages.
- Use percents mode if you've already calculated retention rates—it's faster for pre-calculated data.
- All calculations are for educational understanding, not actual business decisions.
Formulas and Mathematical Logic Behind Cohort Retention
Understanding the mathematics empowers you to calculate cohort retention on exams, verify calculator results, and build intuition about customer behavior over time.
1. Calculating Retention Rate from Counts
Retention Rate = (Active Users at Period / Original Cohort Size) × 100%
Where:
Active Users at Period = number of users still active at the given period
Original Cohort Size = number of users when the cohort started (Period 0)
Key insight: Retention is always relative to the original cohort size, not the previous period. This allows you to see how each cohort performs over time. Understanding this helps you see how retention quantifies customer retention.
2. Converting Counts to Retention Percentages
Retention % = (Active Count / Original Size) × 100
This converts raw counts to percentages
Example: Original = 1000, Active = 850 → Retention = (850/1000) × 100 = 85%
3. Converting Percents to Active Counts
Active Count = Original Size × (Retention % / 100)
This converts percentages to counts
Example: Original = 1000, Retention = 85% → Active = 1000 × (85/100) = 850
4. Calculating Average Retention Across Cohorts
Average Retention = Sum of Retention Rates / Number of Cohorts
This gives the simple (unweighted) average across cohorts
Example: Cohort A = 85%, Cohort B = 80%, Cohort C = 75% → Average = (85 + 80 + 75) / 3 = 80%
5. Period 0 Always Equals 100%
Retention at Period 0 = 100% (by definition)
Period 0 represents when the cohort just started
Example: Any cohort at Period 0 = 100% retention (all users are active at the start)
6. Worked Example: Complete Cohort Retention Calculation
Given: January 2024 cohort, Size = 1000, Active counts: M1 = 850, M2 = 720, M3 = 650
Find: Retention rates for each period
Step 1: Period 0 (M0)
Retention = 100% (by definition—cohort just started)
Step 2: Period 1 (M1)
Retention = (850 / 1000) × 100 = 85%
Step 3: Period 2 (M2)
Retention = (720 / 1000) × 100 = 72%
Step 4: Period 3 (M3)
Retention = (650 / 1000) × 100 = 65%
7. Identifying Best-Performing Cohort
Best Cohort = Cohort with Highest Retention at Last Period
Compare retention rates at the final period across all cohorts
Example: Cohort A = 65% at M6, Cohort B = 70% at M6, Cohort C = 60% at M6 → Best = Cohort B (70%)
Practical Applications and Use Cases
Understanding cohort retention is essential for students across business analytics and customer retention coursework. Here are detailed student-focused scenarios (all conceptual, not actual business decisions):
1. Homework Problem: Calculate Retention Rates
Scenario: Your business analytics homework asks: "If a cohort started with 1,000 users and has 850 active in Month 1, what is the retention rate?" Use the calculator: enter Size = 1000, M1 = 850 (counts mode). The calculator shows: Retention = 85%. You learn: how to use Retention = (Active/Original) × 100% to calculate retention rate. The calculator helps you check your work and understand each step.
2. Lab Report: Compare Multiple Cohorts
Scenario: Your customer retention lab report asks: "Compare retention across three cohorts: January (1000 users, 850 at M1), February (1200 users, 1020 at M1), March (950 users, 810 at M1)." Use the calculator: add three cohorts with their data. The calculator shows: January = 85%, February = 85%, March = 85.3%. Understanding this helps explain how to compare cohorts and identify patterns. The calculator makes this comparison concrete—you see exactly how different cohorts perform over time.
3. Exam Question: Calculate Average Retention
Scenario: An exam asks: "What is the average retention at Month 1 if Cohort A = 85%, Cohort B = 80%, Cohort C = 75%?" Use the calculator: enter three cohorts with percents mode. The calculator shows: Average = (85 + 80 + 75) / 3 = 80%. This demonstrates how to calculate average retention across cohorts.
4. Problem Set: Identify Best-Performing Cohort
Scenario: Problem: "Which cohort has the best retention at Month 6?" Use the calculator: enter multiple cohorts with data through Month 6. The calculator shows: Best cohort = highest retention at M6. This demonstrates how to identify best-performing cohorts.
5. Research Context: Understanding Why Cohort Analysis Matters
Scenario: Your business analytics homework asks: "Why is cohort analysis fundamental to customer analytics?" Use the calculator: explore different cohort scenarios. Understanding this helps explain why cohort analysis reveals true trends (growing user counts can mask declining retention), identifies best cohorts (which channels/versions produce engaged users), highlights drop-off points (when users churn), and shows product improvement (newer cohorts should have better retention). The calculator makes this relationship concrete—you see exactly how cohort analysis provides insights that aggregate metrics cannot.
Common Mistakes in Cohort Retention Calculations
Cohort retention problems involve retention calculations, average calculations, and cohort comparisons that are error-prone. Here are the most frequent mistakes and how to avoid them:
1. Calculating Retention Relative to Previous Period Instead of Original Cohort
Mistake: Using Retention = (Current Period / Previous Period) × 100% instead of (Current Period / Original Size) × 100%, leading to wrong retention rates.
Why it's wrong: Cohort retention should be relative to the original cohort size, not the previous period. Using previous period gives period-to-period retention (different metric). For example, Original = 1000, M1 = 850, M2 = 720, using (720/850) × 100 = 84.7% (wrong, should be 720/1000 = 72%).
Solution: Always use: Retention = (Active at Period / Original Size) × 100%. The calculator does this correctly—observe it to reinforce cohort retention calculation.
2. Not Setting Period 0 to 100%
Mistake: Entering a value for Period 0 or calculating it, leading to confusion about cohort start.
Why it's wrong: Period 0 represents when the cohort just started. By definition, retention at Period 0 is always 100% because all users are active at the start. Entering a different value or calculating it is incorrect.
Solution: Always remember: Period 0 = 100% (by definition). The calculator sets this automatically—observe it to reinforce that Period 0 is always 100%.
3. Mixing Counts and Percents in the Same Calculation
Mistake: Entering some values as counts and others as percents, leading to inconsistent calculations.
Why it's wrong: The calculator uses one input mode (counts or percents) for all cohorts. Mixing modes gives wrong results. For example, entering 850 (count) and 85% (percent) in the same cohort is inconsistent.
Solution: Always use consistent input mode. If using counts mode, enter all values as counts. If using percents mode, enter all values as percents. The calculator enforces this—use it to reinforce mode consistency.
4. Using Wrong Denominator for Average Retention
Mistake: Using weighted average (by cohort size) when simple average is needed, or vice versa, leading to wrong average retention.
Why it's wrong: This tool uses simple (unweighted) average: Average = Sum / Number of Cohorts. Weighted average would be: Average = Sum(Retention × Size) / Sum(Sizes). They give different results. For example, Cohort A (1000 users, 85%) and Cohort B (100 users, 80%), simple average = 82.5%, weighted = (850 + 80) / 1100 = 84.5%.
Solution: Always remember: this tool uses simple average. For weighted average, you'd need to calculate manually. The calculator shows simple average—use it to reinforce the distinction.
5. Not Accounting for Missing Data
Mistake: Treating missing data as zero or ignoring it, leading to wrong retention calculations.
Why it's wrong: Missing data (empty cells) should be treated as unknown, not zero. Zero means 0% retention (all users churned), while missing means data not available. For example, if M3 is missing, you can't calculate retention for M3—it's unknown, not 0%.
Solution: Always leave missing periods empty. The calculator treats empty cells as missing (not zero)—use it to reinforce that missing data is different from zero.
6. Using Inconsistent Granularity Across Cohorts
Mistake: Mixing monthly, quarterly, and yearly periods in the same table, leading to meaningless comparisons.
Why it's wrong: All cohorts must use the same granularity for meaningful comparison. Comparing monthly retention to quarterly retention is like comparing apples to oranges. For example, 85% monthly retention is very different from 85% quarterly retention.
Solution: Always use consistent granularity. If using monthly, all cohorts should use monthly periods. The calculator enforces this—use it to reinforce granularity consistency.
7. Not Recognizing That This Tool Doesn't Provide Statistical Significance
Mistake: Assuming the calculator provides statistical significance testing or guarantees that one cohort is "significantly" better than another.
Why it's wrong: This tool performs descriptive cohort analysis only. It doesn't provide statistical hypothesis testing, confidence intervals, or significance tests. Real cohort comparison requires formal statistical tests that account for sample sizes and multiple comparisons.
Solution: Always remember: this tool is for descriptive analysis. You must use statistical tests for significance. The calculator emphasizes this limitation—use it to reinforce that descriptive analysis and statistical testing are separate steps.
Advanced Tips for Mastering Cohort Retention
Once you've mastered basics, these advanced strategies deepen understanding and prepare you for complex cohort retention problems:
1. Understand Why Retention Is Always Relative to Original Cohort (Conceptual Insight)
Conceptual insight: Cohort retention measures how many users from the original cohort are still active, not how many users remain from the previous period. This allows you to see how each cohort performs over time and compare cohorts fairly. Understanding this provides deep insight beyond memorization: retention relative to original = cohort health, retention relative to previous = period-to-period retention (different metric).
2. Recognize Patterns: Early Drop, Flattening Curve, Improving Cohorts
Quantitative insight: Most products show: (a) Early drop—significant churn in first period (normal, represents users who didn't find value), (b) Flattening curve—retention curves flatten over time (healthy, users who survive early periods tend to stick), (c) Improving cohorts—newer cohorts show better retention (indicates product improvement). Understanding these patterns helps you predict retention: early drop = normal, flattening = healthy, improving = good sign.
3. Master the Systematic Approach: Cohorts → Periods → Retention → Averages → Best Cohort
Practical framework: Always follow this order: (1) Define cohorts (groups with common attribute), (2) Set up periods (time intervals for measurement), (3) Calculate retention for each cohort at each period (Active/Original × 100%), (4) Calculate average retention by period (Sum/Number), (5) Identify best-performing cohort (highest retention at last period). This systematic approach prevents mistakes and ensures you don't skip steps. Understanding this framework builds intuition about cohort retention.
4. Connect Cohort Retention to Customer Analytics Applications
Unifying concept: Cohort retention is fundamental to customer analytics (understanding customer behavior over time), product management (identifying best cohorts and drop-off points), marketing (evaluating acquisition channels), and business growth (revealing true trends vs. aggregate metrics). Understanding cohort retention helps you see why it reveals true trends (growing user counts can mask declining retention), identifies best cohorts (which channels/versions produce engaged users), highlights drop-off points (when users churn), and shows product improvement (newer cohorts should have better retention). This connection provides context beyond calculations: cohort retention is essential for modern customer analytics.
5. Use Mental Approximations for Quick Estimates
Exam technique: For quick estimates: If 850 of 1000 active, retention ≈ 85%. If average of 85%, 80%, 75%, average ≈ 80%. If best cohort = 70% at M6, it's the highest. These mental shortcuts help you quickly estimate on multiple-choice exams and check calculator results.
6. Understand Limitations: This Tool Uses Simple Averages
Advanced consideration: This calculator uses simple (unweighted) averages across cohorts. It doesn't account for: (a) Cohort size differences (weighted averages), (b) Statistical significance (formal tests), (c) Revenue per user (revenue retention), (d) Expansion/contraction (upgrades/downgrades), (e) Discounting (time value of money). Real systems may show these effects. Understanding these limitations shows why weighted averages, statistical tests, and revenue retention are often needed, and why advanced methods are required for accurate work in research, especially for complex businesses or non-standard customer behaviors.
7. Appreciate the Relationship Between Cohort Retention and Business Health
Advanced consideration: Cohort retention affects business outcomes: (a) Improving cohorts = product improvement = better long-term health, (b) Flattening curves = healthy retention = users who survive early periods tend to stick, (c) Early drop = normal but can be improved = focus on onboarding, (d) Best cohorts = identify what works = replicate successful strategies. Understanding this helps you design retention strategies that use cohort analysis effectively and achieve optimal business outcomes.
Limitations & Assumptions
• Simple Unweighted Averages: This calculator uses simple averages across cohorts. Cohorts of different sizes should often be weighted by size. A large cohort's retention is more important than a small cohort's, but simple averages treat them equally.
• No Statistical Significance: Retention differences between cohorts may reflect random variation rather than meaningful changes. Without confidence intervals or hypothesis tests, apparent improvements could be noise rather than signal.
• User Retention vs. Revenue Retention: This tool tracks user/logo retention. For businesses where customer value varies, revenue retention (dollar retention) often matters more than user counts. High-value customer churn is more impactful than low-value churn.
• Fixed Cohort Definition: Retention curves depend heavily on how cohorts are defined (signup date, first purchase, activation). Different definitions can yield dramatically different patterns. Ensure cohort definitions align with business questions.
Important Note: This calculator is strictly for educational and informational purposes only. It demonstrates cohort retention concepts for learning. For product decisions, use comprehensive analytics platforms with statistical testing, segmentation, and revenue retention metrics. Consult with product analysts or data scientists for strategic insights.
Sources & References
The cohort retention analysis methods used in this calculator are based on established customer analytics principles from authoritative sources:
- Fader, P. S., & Hardie, B. G. S. (2010). Customer-Base Valuation in a Contractual Setting. Marketing Science, 29(1), 85-98. — Academic foundation for cohort-based retention modeling.
- Croll, A., & Yoskovitz, B. (2013). Lean Analytics: Use Data to Build a Better Startup Faster. O'Reilly Media. — Practical guide to cohort analysis for product development.
- Amplitude Analytics — amplitude.com — Industry best practices for cohort analysis and retention measurement.
- Mixpanel Resources — mixpanel.com — Product analytics methodology and cohort visualization techniques.
Note: This calculator is designed for educational purposes to help students understand cohort retention concepts. For product decisions, use comprehensive analytics platforms with statistical significance testing.
Frequently Asked Questions
What's the difference between 'counts' and 'percents' input mode?
In 'counts' mode, you enter the actual number of active users at each period (e.g., 850 users active in Month 1). The tool calculates retention percentages for you. In 'percents' mode, you directly enter retention percentages (e.g., 85% retained in Month 1). Use counts if you have raw data; use percents if you've already calculated retention rates. Understanding this helps you see which mode to use for your data and why each mode is useful.
What does Period 0 (M0, Q0, etc.) represent?
Period 0 represents the moment the cohort is formed—for example, when users sign up. By definition, retention at Period 0 is always 100% because all users in the cohort are 'active' at the start. Period 1 is the first measurement after the cohort started (e.g., 1 month after signup). Understanding this helps you see why Period 0 is always 100% and how periods are numbered.
How is retention calculated relative to original cohort vs previous period?
This tool calculates retention relative to the original cohort size, not the previous period. For example, if your cohort started with 1,000 users and has 500 active in Month 3, that's 50% retention—regardless of how many were active in Month 2. This is the standard approach for cohort retention analysis. Understanding this helps you see why retention is always relative to the original cohort and how it differs from period-to-period retention.
Why might newer cohorts show better retention than older ones?
Several factors can explain improving cohort retention: product improvements (better onboarding, new features), better targeting in acquisition (higher-quality users), seasonal effects (certain months attract more engaged users), or market changes. However, be careful not to over-interpret short-term differences—statistical noise and small sample sizes can create apparent patterns. Understanding this helps you see why newer cohorts might perform better and how to interpret these patterns correctly.
Should I weight the average by cohort size?
This tool uses a simple (unweighted) average across cohorts. For a weighted average that accounts for cohort sizes, you'd give larger cohorts more influence. Both approaches are valid: simple averages treat each cohort equally as an 'experiment', while weighted averages reflect your overall user base better. For most purposes, the simple average is sufficient for comparing cohort performance. Understanding this helps you see when to use weighted vs unweighted averages and why each approach is useful.
How do I know if my retention is 'good'?
Retention benchmarks vary dramatically by product type. Mobile apps might be happy with 25% Day 30 retention, while B2B SaaS expects 90%+ annual retention. Compare against: (1) your historical trends, (2) industry-specific benchmarks, and (3) your business model requirements (e.g., what retention do you need to be profitable?). This tool is for analysis, not for declaring 'good' or 'bad'. Understanding this helps you see why retention benchmarks are context-dependent and how to evaluate your retention correctly.
Can I use this for daily, weekly, or yearly cohorts?
Yes! Use the granularity dropdown to select monthly, quarterly, yearly, or custom periods. Choose based on your business: daily retention makes sense for social apps, monthly for most SaaS, yearly for enterprise products with annual contracts. The key is consistency—pick a granularity and stick with it for meaningful comparisons. Understanding this helps you see when to use each granularity and why consistency matters.
What if my cohort sizes vary significantly?
Varying cohort sizes are normal (some months have more signups than others). However, very small cohorts (e.g., 10 users) will have noisy retention data—a single user churning represents 10%! For small cohorts, focus on trends across multiple cohorts rather than individual cohort performance. Understanding this helps you see how cohort size affects data quality and why small cohorts need careful interpretation.
Why isn't there a statistical significance test?
This tool is for descriptive cohort analysis, not statistical hypothesis testing. To determine if one cohort is 'significantly' better than another, you'd need formal statistical tests that account for sample sizes and multiple comparisons. Such tests are more complex and require additional assumptions. Use this tool to explore patterns, then validate important findings with proper statistical analysis. Understanding this limitation helps you use the tool correctly and recognize when statistical tests are needed.
Is this tool suitable for financial planning?
No. This is an educational tool for understanding cohort retention patterns. For financial planning, you'd need more sophisticated models that account for revenue per user, expansion/contraction, discounting, and other factors. Additionally, past retention does not guarantee future retention. Always consult with financial professionals for business planning. Understanding this limitation helps you use the tool for learning while recognizing that financial planning requires validated procedures and professional judgment.
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