Keyword Cannibalization Analysis

Understanding Keyword Cannibalization

Keyword cannibalization occurs when multiple pages on your website compete against one another for the same search query. This analysis is crucial because it can result in decreased rankings and an unclear content strategy. The KW Cannibalization Analysis app uses your Google Search Console data to identify when multiple pages are ranking for a query and helps you determine which pages need attention.

Building the KW Cannibalization Analysis App

This tool is built using Moonlit Platform’s flexible no-code editor, combining data from Google Search Console with custom Python analysis to identify potential cannibalization issues. Here’s how the app works:

Step 1: Fetching Data from Google Search Console

Users start by providing their GSC Property name along with a list of keywords to exclude, typically branded queries that might distort the analysis. The Google Search Console function retrieves fundamental SEO metrics, particularly focusing on the query and page dimensions over the past year. This ensures the app has enough context on how your pages are performing in search.

Step 2: Analyzing Cannibalization with a Python Function

The Python function takes the data from Google Search Console and applies several filters: it first removes queries containing the excluded (typically branded) keywords. Then, it identifies queries ranking across more than one page, which is a common indicator of cannibalization. Next, the function calculates a percentage of clicks for each page versus the overall query—to better understand if certain pages are underperforming compared to others. Finally, it flags records as either “Potential Opportunity” or noting a “Risk” based on the share of clicks, making it easier to decide which pages might need consolidation or optimization.

Running the App at Scale

1. Navigate to the Bulk Runs section on Moonlit Platform.

2. Create a new job by uploading a CSV file that contains your GSC property names and additional parameters, such as the list of excluded keywords for each property.

3. Map the columns correctly so that the app can retrieve the GSC property name and excluded keywords from your file.

4. Run the job to iterate through your properties and generate tailored cannibalization reports for each one.

Customizing the App for Your Specific Needs

• Refine Your Filters: Adjust the Python code to refine the filters based on your industry or specific SEO challenges. For instance, you might tweak the thresholds for the click percentages to better align with your conversion goals.

• Adjust the Exclusion List: Customize the list of excluded keywords to ensure that branded queries or other irrelevant terms are not skewing your analysis. This makes the report more focused on genuine cannibalization instances.

• Enhance Reporting: Modify the output format to include additional context such as the overall search volume or ranking trends over time. This can help in making more informed decisions about which pages to merge, update, or remove.

• Integrate with Other Data Sources: Consider incorporating additional data points, like conversion rates or user engagement metrics, to deepen your analysis and further align with your business goals.

Get Started

Moonlit Platform provides a versatile environment to build and customize SEO tools like the KW Cannibalization Analysis app. Explore the platform and start building your own apps to streamline your SEO processes. Sign up today and begin tailoring your strategies with our flexible, no-code tools.