## Introduction
Cohort analysis is a powerful technique used by data and analytics teams to understand user behavior over time, segmenting users based on shared characteristics or actions. Manually preparing cohort analysis reports can be time-consuming, error-prone, and non-scalable. Automating these reports ensures timely, consistent, and actionable insights, freeing data professionals to focus on analysis rather than data wrangling.
In this article, we’ll walk through how to automate cohort analysis report generation using n8n, an open-source workflow automation tool. We will integrate it with a database (e.g., PostgreSQL), Google Sheets for report visualization, and Slack for report delivery. This setup benefits data analysts, product managers, and growth teams by providing automated, accurate cohort insights regularly.
—
## Tools and Services Integrated
– **n8n:** Automation workflow engine to orchestrate the entire process
– **PostgreSQL:** Source database storing user event data
– **Google Sheets:** Destination to create and store cohort analysis reports
– **Slack:** Channel to notify teams when a new report is ready
—
## Use Case Overview
This workflow automatically extracts user event data from PostgreSQL, processes cohorts by user sign-up date and engagement over subsequent weeks, generates a report in Google Sheets with retention metrics, and sends a Slack notification with a link to the report. The workflow is triggered weekly, ensuring stakeholders receive fresh insights without manual intervention.
—
## Step-by-Step Technical Tutorial
### Prerequisites
– n8n instance running (cloud or self-hosted)
– Access credentials for PostgreSQL, Google Sheets, and Slack
– A table with user events or sign-ups in the PostgreSQL database
### Step 1: Define the Workflow Trigger
– Use the **Cron** node in n8n to schedule the workflow.
– Set it to run every Monday at 7 AM to send weekly cohort reports.
### Step 2: Query PostgreSQL for User Cohort Data
– Add the **PostgreSQL** node.
– Configure connection credentials.
– Write a parameterized SQL query to extract relevant data.
Example SQL query (simplified):
“`sql
WITH cohorts AS (
SELECT
user_id,
MIN(DATE_TRUNC(‘week’, signup_date)) AS cohort_week
FROM users
GROUP BY user_id
),
user_events AS (
SELECT
user_id,
DATE_TRUNC(‘week’, event_date) AS event_week
FROM events
)
SELECT
c.cohort_week,
ue.event_week,
COUNT(DISTINCT ue.user_id) AS active_users
FROM cohorts c
JOIN user_events ue ON c.user_id = ue.user_id
GROUP BY c.cohort_week, ue.event_week
ORDER BY c.cohort_week, ue.event_week;
“`
– This query produces weekly cohorts and their retention across event weeks.
### Step 3: Transform Data for Cohort Table
– Insert a **Function** node to pivot SQL data into a cohort matrix format.
– The input is an array of records with `cohort_week`, `event_week`, and `active_users`.
Example transformation logic:
– Identify the difference in weeks between `event_week` and `cohort_week`.
– Build an array or object where rows are cohort weeks and columns are week offsets (Week 0, Week 1, etc.) with retention numbers.
“`javascript
const data = items.map(item => item.json);
const cohorts = {};
// Helper function to calculate week difference
function weekDiff(start, end) {
return Math.round((new Date(end) – new Date(start)) / (7 * 24 * 60 * 60 * 1000));
}
data.forEach(({cohort_week, event_week, active_users}) => {
const diff = weekDiff(cohort_week, event_week);
if (!cohorts[cohort_week]) cohorts[cohort_week] = {};
cohorts[cohort_week][`Week_${diff}`] = active_users;
});
// Convert cohorts object to an array structured for Google Sheets
const result = [];
const header = [‘Cohort Week’];
// Assume max 8 weeks of retention
for(let i=0; i<=7; i++) header.push(`Week ${i}`);
result.push(header);
for (const [cohortWeek, weeks] of Object.entries(cohorts)) {
const row = [cohortWeek];
for(let i=0; i<=7; i++) {
row.push(weeks[`Week_${i}`] || 0);
}
result.push(row);
}
return result.map(row => ({ json: { row } }));
“`
– Adjust the function if your retention window is longer or shorter.
### Step 4: Write Data to Google Sheets
– Add a **Google Sheets** node.
– Authenticate and select the appropriate spreadsheet.
– Choose the ‘Append’ or ‘Clear and Append’ action on the target worksheet.
– To overwrite previous reports, use the ‘Clear’ method on the sheet first, then add rows with transformed cohort data.
– Map the rows generated by the previous Function node.
### Step 5: Send Slack Notification
– Insert a **Slack** node.
– Authenticate with your workspace.
– Configure the message payload:
– Channel ID (e.g., #data-analytics)
– Text like: “Weekly Cohort Report generated! View it [here](https://docs.google.com/spreadsheets/d/your-spreadsheet-id)”
– Optionally add message formatting or attachments.
### Step 6: Connecting the Nodes
– Connect the Cron node to PostgreSQL.
– PostgreSQL to Function (data pivot)
– Function to Google Sheets (write report)
– Google Sheets to Slack (notify team)
This ensures linear, automated flow.
—
## Tips for Robustness and Common Errors
– **Database connectivity issues:** Ensure credentials are securely stored, and add error handling or retries in n8n if your data source is unreliable.
– **Timezones:** Consistently use UTC in SQL and n8n nodes to avoid time mismatch in cohort grouping.
– **Google Sheets API limits:** Batch updates and clear the sheet prior to inserting new data to avoid duplication.
– **Data volume:** For large datasets, consider filtering or incremental cohort reporting to avoid latency.
– **Slack notifications:** Add conditional logic to send notifications only when new data is present.
—
## Scaling and Adaptation
– To scale this workflow, consider:
– Adding parameters to adjust cohort duration or retention windows dynamically.
– Use n8n environment variables or credentials manager for easier maintenance.
– Integrate additional channels e.g., email or dashboards.
– Include error notification nodes for monitoring failures.
– Connect to other databases or analytical tools like BigQuery.
– To adapt for monthly cohorts, modify SQL logic and the transformation function accordingly.
—
## Summary
Automating cohort analysis reports with n8n empowers data and analytics teams to deliver timely, consistent insights with minimal manual effort. By integrating PostgreSQL, Google Sheets, and Slack in a scheduled workflow, you save valuable time, reduce errors, and provide actionable engagement metrics. The approach demonstrated here can be customized and scaled as your data landscape evolves.
—
**Bonus Tip:**
To enhance usability, consider using Google Sheets’ built-in charts to visualize cohorts dynamically. You can have n8n refresh the sheet weekly, and stakeholders can see retention heatmaps or line charts without logging into additional BI tools.
Happy automating your data workflows with n8n!