Content Funnel Segmentor

This tool—Analyze QRG—assists SEO professionals by evaluating a page against Google's Helpful Content Guidelines and Search Quality Evaluator Guidelines. By assessing the textual content of a webpage, it highlights areas where the page meets or falls short of these standards, offering actionable recommendations with specific examples drawn straight from the page.

Building the "Analyze QRG" App

Step 1: Fetching Guideline Information

In the initial step, the app retrieves detailed information on both Google’s Helpful Content Guidelines and the Search Quality Evaluator Guidelines. Two Chat Model functions are deployed: one to gather insights on the Helpful Content Guidelines, and another to fetch details on the Quality Evaluator Guidelines. By using these models, the app ensures that the analysis is based on the most current and comprehensive descriptions available for each guideline.

Step 2: Extracting Page Content

The next step involves a web scraping function that extracts the main body text from the provided Full Website URL. This function is set to return just the page’s body text, allowing the subsequent review processes to focus solely on the relevant content. It ensures that only the text—the part that affects how a page is perceived according to these guidelines—is used for the analysis.

Step 3: Generating Detailed Recommendations

In the final step, the app runs two separate Chat Model functions geared toward evaluating the page. One function is designed to check the page against Quality Raters Guidelines, while the other reviews it against the Helpful Content Guidelines. Each function is instructed to scrutinize the text by providing specific and actionable recommendations with examples directly pulled from the page text.

The models are carefully configured to use markdown formatting for improved clarity, organizing the feedback into easily readable headings, subheadings, and lists. Importantly, they explicitly note that recommendations for images or links are omitted since only the textual content is being analyzed, with a disclaimer about the limitations regarding non-textual elements.

Customizing the App for Your Needs

• Enhance Recommendation Specificity: Adjust the Chat Model prompts to require additional contextual analysis, such as evaluating the tone, structure, or keyword density of the text for even more detailed feedback.

• Variable Analysis Depth: Tweak the instructions for the Chat Models to either broaden their scope of analysis or focus on particular sections of the page. For example, you can modify the prompts to place extra emphasis on unique content sections or specific paragraphs that need significant improvement.

• Custom Output Formats: The app’s configurations can be altered to include alternative output formats, such as tables or bullet-point summaries, that improve the readability of recommendations based on your workflow needs.

• Integration with Other Tools: Incorporate additional nodes in the workflow to pull in data from competitor analysis or historical performance metrics, which can further enrich the recommendations provided by the Chat Models.

Running the App at Scale

1. Create a Bulk Job: Navigate to the Bulk Runs page on Moonlit Platform and start a new job by uploading a CSV file that contains multiple website URLs.

2. Configure Column Mapping: Ensure that the CSV column containing the website URLs is accurately mapped to the Full Website URL input of the app.

3. Execute and Monitor: Launch the batch job to iterate through each URL. The app will perform an analysis on every page, providing detailed recommendations for quality and helpful content errors based on the guidelines.

If you’re looking to streamline how you assess content quality and improve your website’s compliance with Google’s standards, sign up for Moonlit Platform today. Explore and build your own custom apps with our intuitive no-code editor and run them at scale with our Bulk Runs feature to save time and enhance your SEO strategy.