## Introduction
In the contemporary data-driven landscape, startups and data & analytics teams face the challenge of consolidating data from multiple analytics platforms—Google Analytics, Facebook Ads, LinkedIn Ads, and others—into a unified dataset for reporting and decision-making. Manually collecting this data is time-consuming, error-prone, and inefficient. Automating this aggregation streamlines analytics workflows, ensures timely insights, and allows teams to focus on strategic analysis rather than data wrangling.
This tutorial walks through how to build an automated analytics aggregation workflow using n8n, an open-source workflow automation tool. By the end, you will have a robust, scheduled workflow that fetches key metrics from different analytics platforms, consolidates them into a Google Sheet, and sends a Slack summary notification.
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## Tools and Services Integrated
– **n8n:** Workflow automation platform.
– **Google Analytics (GA):** Web traffic analytics data.
– **Facebook Ads API:** Advertising metrics.
– **LinkedIn Ads API:** Advertising campaign data.
– **Google Sheets:** Storage of aggregated data.
– **Slack:** Notification channel.
## Workflow Overview
1. **Trigger:** Scheduled trigger, runs the workflow every morning.
2. **Google Analytics Node:** Fetch pageviews, sessions, user metrics.
3. **Facebook Ads Node:** Pull ad spend, impressions, clicks.
4. **LinkedIn Ads Node:** Retrieve campaign performance metrics.
5. **Data Transformation:** Normalize and format all data.
6. **Google Sheets Append:** Insert aggregated data into a spreadsheet.
7. **Slack Notification:** Post a summary report with key metrics.
## Step-By-Step Tutorial
### Prerequisites
– n8n instance set up (self-hosted or n8n.cloud).
– API access and credentials for Google Analytics, Facebook Ads, LinkedIn Ads.
– Google Sheets API enabled with credentials.
– Slack webhook or bot token.
### Step 1: Create a New Workflow with a Scheduled Trigger
– In n8n, create a new workflow.
– Add a **Schedule Trigger** node.
– Configure it to run daily at 7:00 AM (adjust as needed).
### Step 2: Connect to Google Analytics API
– Add a **Google Analytics** node.
– Authenticate using OAuth2 credentials (setup in Credentials section).
– Set the report type:
– Metrics: `ga:sessions, ga:users, ga:pageviews`
– Dimensions: `ga:date`
– Date Range: `yesterday`
– This node will return your website traffic metrics from the previous day.
### Step 3: Fetch Facebook Ads Data
– Add an **HTTP Request** node (since n8n may not have a dedicated Facebook Ads node).
– Configure it to call the Facebook Graph API endpoints for ads insights:
– Endpoint: `/v14.0/
– Parameters: `fields=impressions,clicks,spend,ad_name,date_preset=yesterday`
– Authenticate using an OAuth2 or permanent Access Token.
### Step 4: Fetch LinkedIn Ads Data
– Similarly, add an **HTTP Request** node for LinkedIn Ads API.
– Use the Ads Reporting API:
– Endpoint: `https://api.linkedin.com/v2/adAnalyticsV2`
– Use query params to specify date range (yesterday), metrics (impressions, clicks, spend), and campaigns.
– Authenticate using LinkedIn OAuth2 credentials.
### Step 5: Transform and Normalize Data
– Add a **Function** node to standardize all data into a common format:
– Date
– Platform
– Campaign/Ad Name (if applicable)
– Impressions
– Clicks
– Spend
– Sessions, Users, Pageviews (from GA)
– Write JavaScript code to parse and map the JSON responses from each API to the standardized format.
Example snippet inside Function node:
“`javascript
const results = [];
// Google Analytics data
items[0].json.forEach(record => {
results.push({
date: record.date,
platform: ‘Google Analytics’,
impressions: null,
clicks: null,
spend: null,
sessions: record.sessions,
users: record.users,
pageviews: record.pageviews
});
});
// Facebook Ads data
items[1].json.data.forEach(ad => {
results.push({
date: ad.date_start,
platform: ‘Facebook Ads’,
impressions: ad.impressions,
clicks: ad.clicks,
spend: ad.spend,
sessions: null,
users: null,
pageviews: null
});
});
// LinkedIn Ads data
items[2].json.elements.forEach(ad => {
results.push({
date: ad.dateRange.start, // or relevant field
platform: ‘LinkedIn Ads’,
impressions: ad.impressions,
clicks: ad.clicks,
spend: ad.spend,
sessions: null,
users: null,
pageviews: null
});
});
return results.map(item => ({ json: item }));
“`
Update the `items` indices according to node order.
### Step 6: Append Aggregated Data to Google Sheets
– Add a **Google Sheets** node.
– Select ‘Append’ operation.
– Configure target spreadsheet and worksheet for your analytics data.
– Map the standardized fields (date, platform, impressions, clicks, spend, sessions, users, pageviews) to columns.
– Enable ‘Raw Data’ to avoid unexpected formatting errors.
### Step 7: Send Summary Notification to Slack
– Add a **Slack** node.
– Authenticate with Bot Token or Webhook.
– Compose a message summarizing key metrics, e.g.:
– Total sessions yesterday: X
– Total ad spend: $Y
– Click-through rate per platform
– Optionally, include a link to the Google Sheet.
## Common Pitfalls and Tips
– **API Rate Limits:** Be mindful of call quotas on Facebook, LinkedIn, and Google Analytics APIs. Implement error handling or retries in n8n.
– **Authentication Expiry:** OAuth tokens may expire. Use n8n credentials to manage refresh tokens automatically.
– **Data Schema Changes:** Analytics API endpoints may evolve; periodically validate response structures.
– **Timezone alignment:** Ensure all date fields are consistent (UTC or local timezone) for accurate daily reporting.
– **Error Handling:** Use n8n’s Error Trigger node or workflow-level error catchers to alert on failed API calls.
– **Data Deduplication:** Confirm that each run appends only new data, or implement logic to prevent duplicates.
## Scaling and Adaptation
– **Add More Platforms:** You can extend this workflow with additional API sources like Twitter Analytics, Google Ads, or Mixpanel.
– **Output Destinations:** Besides Google Sheets, you can write data to databases (Postgres, MySQL) or data warehouses for advanced BI.
– **Visualization Integration:** Connect downstream to data visualization tools such as Google Data Studio or Tableau.
– **Dynamic Date Ranges:** Modify schedule and date ranges dynamically to support historical backfills or incremental updates.
– **Multi-team Reporting:** Push customized summaries to different Slack channels based on team.
## Summary
Automating analytics aggregation across platforms using n8n minimizes manual effort and accelerates insight generation for data-driven teams. By integrating Google Analytics, Facebook Ads, and LinkedIn Ads APIs, normalizing data, storing it in Google Sheets, and sending Slack notifications, teams can access daily consolidated reports effortlessly.
The modularity and flexibility of n8n empower teams to expand this workflow with additional data sources and tailor reporting for organizational needs. Robust error handling, token management, and data consistency checks are key to maintaining a resilient automation.
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## Bonus Tip: Monitoring and Alerting
Set up a separate n8n workflow to monitor this analytics aggregation pipeline’s health. Configure it to:
– Trigger on errors or failed executions.
– Check data freshness and completeness.
– Send alerts to Slack or email if anomalies occur.
This proactive monitoring ensures that critical analytics data flows remain uninterrupted and reliable.