Automated App Analysis & ASO Report Generator - n8n Workflow

• ASO Specialists: Users needing rapid, automated competitor analysis and reporting.


• Mobile App Developers: Teams seeking consistent, data-driven recommendations for app growth.


• Digital Marketing Agencies: Professionals who need to generate numerous, high-quality analysis documents efficiently.


• n8n Automation Enthusiasts: Users looking for advanced examples of using the n8n node ecosystem for data retrieval, code scripting, and AI integration.

Generating App Store Optimization (ASO) reports manually is time-consuming and often inconsistent. This robust n8n workflow solves this by fully automating the process from data ingestion to final document delivery. The core value of this n8n template is its ability to seamlessly integrate external app intelligence APIs (like SensorTower) with powerful LLMs (Gemini via OpenRouter) to produce highly structured and actionable ASO analysis.

When a user submits an app URL via the initial form n8n trigger, the system fetches key metrics, competitor data, and reviews. A dedicated n8n node then cleans and formats this complex JSON data before presenting it to the AI. The resulting comprehensive report is automatically saved in a new Google Doc, ensuring easy sharing and collaboration. This advanced n8n workflow drastically cuts down reporting time and ensures a consistent, professional output.

This automation sequence starts with the 'On form submission' n8n trigger:

1. Trigger and Input: The n8n workflow is initiated when a user submits a form containing the Google Play Store URL of the app to be analyzed.


2. Package Name Extraction (Code1): A Code n8n node parses the URL to extract the id= parameter (the package name), which is required for API calls.


3. Data Retrieval (HTTP Request): Using the extracted package name, an HTTP Request n8n node calls the specified app intelligence API (e.g., SensorTower) to fetch all relevant data, including installs, ratings, reviews, and competitor information.


4. Data Preparation (Code): A second Code n8n node processes the large, raw JSON output. It structures the data by extracting key app details, the top featured reviews, competitor names/ratings, and market insights (downloads/revenue) into clean, formatted text ready for the LLM.


5. AI Analysis (Basic LLM Chain): The prepared data is fed into the Basic LLM Chain n8n node, configured to use the Gemini model via OpenRouter. The prompt instructs the AI Analyst to generate a detailed ASO Report covering App Overview, Reviews, Competitor Analysis, Market Insights, and Recommendations.


6. Google Docs Creation: A Google Docs n8n node creates a new document, using the app's name as the title, within a designated Google Drive folder.


7. Report Insertion (Update a document): A subsequent Google Docs n8n node updates the newly created document, inserting the full, plain-text ASO report generated by the AI.


8. Notification (Telegram): Finally, a Telegram n8n node sends a notification to the user, providing the app's name and a direct link to the finalized Google Docs report. This completes the automated n8n workflow cycle.

To implement this n8n template, follow these steps:

1. Import: Copy the provided n8n workflow JSON and paste it into your n8n instance using the 'New' -> 'Import from JSON' option.


2. Credentials Setup:

1. Activation: Save the n8n workflow and activate it by toggling the 'Active' switch on the top right. Access the unique form link from the 'On form submission' n8n trigger to begin testing.

On form submission (n8n Trigger): This n8n trigger starts the execution. It is configured to accept a single input field: 'Play Store URL'. This makes the entire n8n workflow accessible via a simple web form.
Code1 (Code n8n node): Essential for pre-processing. Its function is to reliably extract the package name (id=...) from the potentially complex Google Play URL submitted by the user.
HTTP Request (n8n node): Performs the data fetching, targeting an external API (like SensorTower) to retrieve comprehensive app intelligence data. The URL uses an expression {{ $json.packageName }} dynamically populated by the previous n8n node.
Code (Code n8n node): Acts as a data formatter. This specific n8n node selects and organizes app details, filters the top 3 reviews, and structures competitor and market data into distinct, clean text strings for optimal input into the LLM.
OpenRouter Chat Model (n8n node): The language model provider, configured here to use a powerful model like google/gemini-2.0-flash-exp:free, enabling cost-effective AI analysis within this n8n workflow.
Basic LLM Chain (n8n node): The core AI processing step. It combines the system prompt (ASO Analyst persona, formatting instructions) and the structured app data to generate the final analytical report. The result is stored as text for downstream processing.
Create a document (Google Docs n8n node): Creates the required output container, dynamically naming the document using the app's name ({{ $('Code').item.json.appInfo.name }}) derived from earlier steps in the n8n workflow.
Update a document (Google Docs n8n node): Inserts the AI-generated report text into the previously created Google Doc.

• Send a text message (Telegram n8n node): Provides final confirmation and utility by sending a message containing the link to the generated Google Doc, completing the n8n workflow notification loop.