How to Automate Tracking DAUs and MAUs Automatically with n8n for Data & Analytics

Tracking daily active users (DAUs) and monthly active users (MAUs) is crucial for product growth and user engagement analysis 📊. However, manually compiling and analyzing this data can be time-consuming and error-prone, especially for fast-growing startups and data teams. In this guide, you will learn how to automate tracking DAUs and MAUs automatically with n8n, a powerful open-source automation tool ideal for the Data & Analytics department.

We’ll walk you through a practical, step-by-step automation workflow integrating popular services like Google Sheets, Slack, and HubSpot. This automation not only saves time but enhances accuracy, enabling you to monitor user engagement in real-time and share actionable insights effortlessly. Whether you are a startup CTO, automation engineer, or operations specialist, this article will equip you with the methods and best practices to streamline your DAU and MAU tracking.

Why Automate Tracking DAUs and MAUs?

DAUs and MAUs metrics provide a pulse on your product’s health, engagement, and growth. Manual tracking often leads to delays, inconsistent data, and missed opportunities. Automating this process benefits:

• Data & Analytics teams by reducing manual data handling and errors.
• Startup CTOs through real-time insights for fast decision-making.
• Operations specialists who monitor product performance and user behavior trends.

Automated workflows help aggregate user data daily and monthly, send alerts on anomalies, and maintain historical records for reporting, all without human intervention.

Tools and Services Involved in the Automation Workflow

This automation will integrate the following tools:

• n8n: The automation platform orchestrating workflow nodes.
• Google Sheets: For storing and visualizing the DAU and MAU data.
• Slack: To send daily summary notifications to your team.
• Gmail: Optional for sending detailed email reports.
• HubSpot: To enrich user data or trigger CRM-related workflows if needed.

n8n offers flexibility to build highly customizable flows, bridging data from various sources and allowing you to implement robust error handling and scalability features easily.

The Complete n8n Workflow to Track DAUs and MAUs Automatically

Overview of the Workflow

The workflow runs once daily on a schedule and performs these main steps:

1. Trigger: A scheduled Cron node runs every day at midnight UTC.
2. Fetching Data: Query your product’s user activity database or API to get user IDs active in the last day and 30 days.
3. Calculations: Calculate DAUs and MAUs counts.
4. Data Storage: Append results to Google Sheets for historical tracking.
5. Notifications: Send summary reports to Slack channels and optionally via Gmail.
6. Enrich Data (Optional): Use HubSpot to pull customer info based on active users.

Step-by-step Node Breakdown

1. Cron Node: Trigger Automation Daily

Configure the Cron node with these exact settings:

• Mode: Every Day
• Time (UTC): 00:00
• Timezone: Your preference (UTC recommended for consistency)

This sets the automation to start each day at midnight, ensuring fresh data.

2. HTTP Request Node or Database Node: Fetch User Activity Data

You can use either an HTTP Request node to call your internal API or a database node (MySQL, PostgreSQL, etc.) to fetch user IDs active in the last 1 day and the last 30 days.

• For API requests, set method to GET, add necessary headers like Authorization: Bearer YOUR_API_KEY.
• For DB, write SQL queries such as:

-- DAUs
SELECT DISTINCT user_id FROM user_activity WHERE activity_date = CURRENT_DATE - INTERVAL '1 DAY';

-- MAUs
SELECT DISTINCT user_id FROM user_activity WHERE activity_date >= CURRENT_DATE - INTERVAL '30 DAYS';

Extract the unique user_id values from these queries.

3. Function Node: Calculate Counts

Use the Function node to process the fetched data by counting the unique users from the DAU and MAU queries:

const dauCount = items[0].json.user_ids.length;
const mauCount = items[1].json.user_ids.length;
return [{ json: { dau: dauCount, mau: mauCount, date: new Date().toISOString().slice(0,10) } }];

This aggregates your data into a single structured JSON object.

4. Google Sheets Node: Append Results

Connect the Google Sheets node to append this data to your tracking spreadsheet. Set these fields:

• Operation: Append
• Spreadsheet ID: Your Google Sheets ID
• Sheet Name: “DAU_MAU_Tracking”
• Fields: Map dau, mau, and date columns accordingly.

This creates a historical log of your daily aggregation.

5. Slack Node: Send Daily Summary Notification

Use Slack’s Post message node with a webhook or app token. Example message text:

Daily User Activity Summary for {{ $json.date }}:
- DAUs: {{ $json.dau }}
- MAUs: {{ $json.mau }}

Configure the target channel and enable error handling to retry on failure.

6. (Optional) Gmail Node: Email Detailed Report

If email reports are required, use the Gmail node to send automated messages with detailed stats and charts attached or linked.

7. (Optional) HubSpot Node: Enrich User Data

Use HubSpot’s APIs in n8n to supplement user activity data with CRM attributes to get deeper insights or trigger lead nurturing workflows.

Handling Errors, Rate Limits, and Robustness

Reliable Error Handling

• Enable retry policies with exponential backoff on API or DB nodes.
• Use IF nodes to branch on error states and send alerts to Slack or email.
• Log failures in a dedicated Google Sheet tab or external monitoring service.

Idempotency and Data Accuracy

Ensure your queries or API calls are idempotent to avoid duplicates. When appending to Sheets, verify if the date row exists and update instead of blindly appending.

Rate Limits & Throttling

Be aware of API call limits for Google Sheets, Slack, or HubSpot. Implement delays or queued parallelism in n8n settings to avoid crossing thresholds.

Security and Compliance Considerations

• Store API keys securely in n8n credentials with limited scope access.
• Mask sensitive data — do not log PII in raw form.
• Ensure OAuth tokens are refreshed correctly in workflows connecting to HubSpot or Gmail.
• Review Google Sheet sharing permissions to restrict data to your team only.

Scaling and Adaptation Strategies

Handling Large Data Volumes

• Use pagination or batch processing in API calls when querying user data.
• Parallelize requests but limit concurrency to prevent rate limits.
• Modularize complex workflows by separating data extraction, transformation, and notification flows.

Using Webhooks vs. Polling for Real-Time Tracking 🔄

Approach	Pros	Cons
Webhook Trigger	Real-time updates; efficient resource use; no polling delays.	Requires product support for webhooks; setup complexity.
Scheduled Polling (Cron)	Simple setup; works with any data source allowing queries.	Potential delays; higher API usage; less real-time.

Google Sheets vs. Database for Storing User Activity Data 💾

Storage Option	Advantages	Limitations
Google Sheets	Easy access; built-in visualization; collaborative editing.	Limited scalability; slower with large datasets; fewer query capabilities.
Database (SQL/NoSQL)	High scalability; complex queries; robust data integrity.	Requires database management skills; access control needed.

By choosing the right storage based on team skills and data volume, you can optimize performance and data accessibility.

If you want to save time building these workflows, consider checking out pre-built automations. Explore the Automation Template Marketplace for starter workflows integrating n8n with Google Sheets, Slack, and more.

Testing and Monitoring Your Automation

• Use sandbox or test data when first developing your workflow to prevent data pollution.
• Leverage n8n’s run history and debug console for troubleshooting node failures.
• Set up alerts via Slack or email if node failures or unexpected data values occur.
• Periodically review logs and data trends to ensure accuracy and reliability over time.

Comparing Leading Automation Platforms for Tracking DAUs and MAUs

Platform	Cost	Pros	Cons
n8n (self-hosted)	Free (Open-source); Paid cloud plans	Highly customizable; strong community; no vendor lock-in.	Needs hosting setup; UI learning curve.
Make (Integromat)	From $9/month, usage-based	Visual builder; many integrations; robust error handling.	Costs rise with volume; less open-source.
Zapier	Starts $19.99/month	Easy to use; extensive app integrations; strong support.	Limited complex logic; expensive at scale.

Choosing the right platform depends on your technical expertise, budget, and complexity of required workflows.

Ready to build your automated DAU and MAU tracking system? Create Your Free RestFlow Account and accelerate your automation projects with ease.

Conclusion

Automating the tracking of DAUs and MAUs with n8n empowers your Data & Analytics teams to deliver accurate, timely insights with minimal manual effort. By leveraging integrations like Google Sheets and Slack, you create a seamless flow from data extraction to actionable notifications.

Adopting best practices for error handling, security, and scalability ensures your automation remains robust as your startup grows. Start with a basic workflow and evolve it to suit your complex needs—automation is a journey that pays dividends in operational efficiency and data-driven decision-making.

Take the next step now: automate your user metrics tracking and unlock actionable growth insights!