---
title: "Cohort Analysis for Startups - Six Summary Reports to Understand Your Customer Base (With Code)"
description: "Learn 6 essential cohort analysis reports to track SaaS growth, revenue trends, and customer churn. Data-driven insights with code examples for startup founders."
categories: ["best practices","startups","data analysis"]
keywords: ["cohort analysis","customer churn","SaaS growth","data analysis","R programming","revenue trends","Tomasz Tunguz","startup best practices"]
ai_summary: "Explore six essential cohort analysis reports to gain insights into customer behavior and revenue trends for startups using R."
date: 2013-08-12
lastmod: 2026-07-17
canonical_url: https://www.tomtunguz.com/cohort-analysis-with-r/
author: "Tomasz Tunguz"
---

<p>Cohort analysis provides deep insight into customer bases because cohorts expose how customer accounts grow, evolve and churn. Plus, cohort analysis provides a framework to evaluate product releases, marketing pushes and advertising campaign performance.</p>

<h2>The Six Cohort Reports I Use to Understand Trends</h2>

<p>Below is a list of the six reports I create when analyzing cohorts. The combination of these six reports provides a robust summary of the state of a customer base. By no means exhaustive, this collection is a solid starting point and of course these are framed in the context of revenue but could easily be changed to activity or another metric. </p>

<ol>
<li>Average Revenue Per Customer Over Time - Chart monthly revenue over time to contrast with cohort data</li>
<li>Individual Account Growth Over Time - Chart all accounts to visualize trends.</li>
<li>Typical Account Growth Over Time - Chart how the average account grows with time</li>
<li>Number of Customers in Each Cohort - Chart number of customers in each cohort to see how sensitive cohort data is to sample size and also see the size of the new customer pipeline over time. </li>
<li>Average Monthly Revenue By Cohort - Chart the revenue by cohort to see if newer customers generate more or less revenue than older customers. Really good for marketing spend evaluation. </li>
<li>Cohort Comparison - Chart the different cohorts over time to see how their revenue characteristics compare.</li>
</ol>
<h2>Implementation in R</h2>

<p>Cohort analysis is difficult to perform in a database or in Excel so I turned to <a href="http://cran.r-project.org/">R</a>. Below is a generic cohort analysis script for R, the open source statistical language. </p>

<p>I use two libraries by <a href="https://github.com/hadley">Hadley Wickham</a>, <a href="https://github.com/hadley/plyr">plyr</a> for data manipulation and <a href="https://github.com/hadley/ggplot2">ggplot2</a> for plotting the data. Hadley is at the forefront of R and an extraordinary contributor to the community. I can’t overstate his impact on the way I use R. I seem to use only standard libraries and Hadley’s. </p>

<p>To use this data analysis, start with a file with 3 columns: “date” column (YYYY-MM-DD), “company_name” column, monthly revenue “revenue”. If your file isn’t in this format, use Hadley’s <a href="https://github.com/hadley/reshape">reshape</a> library to “melt” the data. Note: I use tab-separated files but you could easily use a CSV.</p>

<p>If you have ideas for improving the script or if you’ve found a bug, send me a note using the email link on the left. </p>

<pre><code>   ### IMPORT LIBRARIES ###
library(plyr)
library(ggplot2)
library(lubridate)

### IMPORT DATA ###
account_data <- read.delim("data.txt")
account_data $date = as.Date(account_data$date)

### COMPUTE AVERAGES ACROSS CUSTOMER BASE ###
monthly_average = ddply(account_data,~date,summarise, avg_revenue=mean(revenue), customers = length(unique(company_name)))

ggplot(monthly_average, aes(x=date, y=avg_revenue)) + geom_line() + labs(title = "Average revenue by Calendar Month for Customers in $", x="Year", y="Average revenue ($)")

### REVENUE BY COHORT ###
cohort = ddply(account_data,~company_name,summarise,cohort=min(date), revenue=sum(revenue), num_months_as_customer = as.numeric(round((as.Date(format(Sys.time(), "%Y-%m-%d")) - min(date))/(365.25/12)) + 1))
cohort_summary = ddply(cohort, ~cohort, summarise, mean_revenue = sum(revenue)/sum(num_months_as_customer) )
ggplot(cohort_summary) + geom_bar(aes(cohort, mean_revenue), stat="identity") + labs(title="Average Monthly Revenue by Cohort", x="Cohort", y="Monthly Revenue")

### COHORT ANALYSIS -- PLOT INDIVIDUAL ACCOUNT GROWTH OVER TIME, USE ALPHA TO EXTRACT PATTERNS ###
accounts= ddply(account_data, .(company_name), transform, cmonth = as.numeric(round((date - min(date))/(365.25/12)) + 1, cohort = min(date)))
ggplot(accounts, aes(x=cmonth, y=revenue, group=company_name)) + geom_line(alpha=0.3) + labs(title="Individual Account Growth Over Time", x = "Months Since Becoming a Paid Customer", y = "Monthly Recurring Revenue")

### COHORT ANALYSIS - PLOT THE AVERAGE ACCOUNT GROWTH OVER TIME ###
average_growth = ddply(accounts, .(cmonth), summarise, avg_revenue = mean(revenue), num = length(company_name))
ggplot(average_growth) + geom_line(aes(cmonth,avg_revenue))  + labs(title="Account Growth Over Time", x = "Months Since Becoming a Paid Account", y = "Monthly Recurring Revenue ($)")

### PLOT THE NUMBER OF CUSTOMERS IN EACH COHORT ###
ggplot(average_growth) + geom_bar(aes(x=cmonth, y=num), stat="identity") + labs(title = "Number of Customers in Each Cohort", x="Months Since Becoming a Paid Customer", y = "Customers")

### PLOT THE AVERAGE revenue OF CUSTOMERS BY DATE JOINED ###
account_data = ddply(account_data, .(company_name), transform, cohort = min(date))
agg = ddply(account_data, .(cohort), summarise, avg_revenue = mean(revenue))
ggplot(agg, aes(x = cohort, y=avg_revenue)) + geom_bar(stat="identity") + labs(title = "Average revenue of Customers by Date Joined", x = "Date Became a Paid Customer", y = "Average revenue ($)")

### COMPARE COHORT REVENUE OVER TIME ###
ggplot(account_data) + geom_line(aes(x=date, y=revenue, group=cohort, colour=cohort))
</code></pre>