Online Marketing Weekly Report

This workflow automates the weekly aggregation and analysis of online marketing data, integrating data from Google Analytics, Google Ads, and Meta Ads. It automatically compares key metrics from the current period with the same period last year. An AI model generates structured analysis reports, which are automatically sent via email and Telegram messages, enhancing the efficiency and accuracy of data analysis. This helps the marketing team quickly gain insights into performance changes, optimize decision-making, and significantly reduces manual operations and information transmission delays.

Tags

Marketing AutomationAI Analytics

Workflow Name

Online Marketing Weekly Report

Key Features and Highlights

This workflow automates the weekly aggregation and analysis of online marketing data by integrating data from Google Analytics, Google Ads, and Meta Ads. It automatically compares key marketing metrics between the current period and the same period last year, leverages AI models to generate structured analytical reports, and sends these reports via email and Telegram messages. The highlights include unified processing of multiple data sources, intelligent data summarization and comparison, as well as automated report generation and distribution, significantly enhancing the efficiency and accuracy of marketing data analysis.

Core Problems Addressed

  • Marketing data is scattered across multiple platforms, making manual aggregation time-consuming and error-prone
  • Lack of automated historical comparison analysis, hindering quick insights into marketing performance changes
  • Report creation is cumbersome, making it difficult to obtain concise and clear weekly reports in real time
  • Delayed information delivery, preventing fast sharing with team members

Use Cases

  • Digital marketing teams receive comprehensive weekly data on ad campaigns and website performance
  • Marketing managers perform comparative analyses across different time periods to optimize budget allocation
  • Automated marketing report generation reduces manual effort and improves team collaboration efficiency
  • AI-driven analysis provides rapid, multi-dimensional data insights to support decision-making

Main Workflow Steps

  1. Scheduled Trigger: Workflow automatically starts every Monday at 7 AM
  2. Data Collection: Invokes sub-workflows to fetch data from Google Analytics, Google Ads, and Meta Ads for the past 7 days and the same period last year
  3. Data Processing: Aggregates collected data by metrics and calculates key indicators such as impressions, click-through rate, conversion rate, cost, ROAS, etc.
  4. AI Analysis: Utilizes OpenAI GPT model to perform tabular comparative analysis and generate concise textual summaries and tables
  5. Report Generation: Combines data summaries from all channels to create a structured HTML email report
  6. Report Distribution: Automatically sends the email report to designated recipients and pushes a brief version via Telegram message
  7. Supporting Tools: Includes multiple calculation and formatting nodes to ensure data accuracy and standardized formatting

Systems and Services Involved

  • Google Analytics API: Website traffic and user behavior data
  • Google Ads API: Google advertising campaign data
  • Facebook Graph API (Meta Ads): Facebook advertising campaign data
  • OpenAI GPT-4: Intelligent data analysis and natural language report generation
  • SMTP Email Service: Email delivery
  • Telegram API: Instant message notifications
  • n8n Sub-workflow Calls: Modular invocation of various data collection and processing sub-processes

Target Users and Value

  • Online marketing teams and digital advertising operators
  • Market analysts and marketing managers
  • Enterprises and organizations requiring regular automated marketing reports
  • Users seeking to leverage AI to enhance marketing data insights and reporting efficiency

This workflow enables users to achieve automated marketing data aggregation, intelligent analysis, and efficient multi-channel distribution, significantly saving time and improving the timeliness and accuracy of data-driven decision-making.

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