SEO workflows enable you to break down SEO tasks into smaller steps to ensure you’re completing each SEO task efficiently and effectively. Content optimization is the process of enhancing old (or new) content to improve its visibility on search engines. Screaming Frog allows you to find advanced technical issues with your website. This includes identifying and fixing broken links and finding and resolving duplicate content issues. (headers, meta, etc.). 

By following the workflow we’ve laid out, you can potentially leverage the insights provided by Screaming Frog's word count, sentence count, average words per sentence, and readability metrics to optimize your website's content for improved SEO performance and content optimization.

To enhance the applicability of this workflow, we'll demonstrate its steps using Slicedbread's blog pages.

Step 1. Get the Pages

Start by getting a list of your site’s pages.

We will use Search Console data, specifically its Performance report from the last 3 months. Our site has various types of pages, including blog articles, service pages, case studies, etc. The workflow is best applied to pages of the same type, as different page types may have distinct requirements to rank in Google’s top 3 search results. For instance, the primary goal of product categories is to list products; product pages, though, must provide detailed product information. Consequently, their content may vary significantly. As such, let’s solely analyze our site’s blog article pages.

To simplify things further, let’s use a single Clicks metric to assess how well a page does. Here is what I have so far:

Data for the Clicks column comes from the Google Search Console.

Step 2. Get the Content Metrics

Use Screaming Frog to crawl the pages and collect data on their word count, sentence count, average words per sentence, and readability.

Be sure to enable word count and readability settings in the crawl configuration settings:

The result should look something like this:

For each of our blog articles, we have their content metrics listed in the adjacent columns.

Step 3. Merge Data to Identify Patterns and Trends

Analyze the data collected from Screaming Frog to identify patterns and trends among high-performing pages and look for correlations between word count, sentence count, average words per sentence, and SEO performance metrics.

I merged Screaming Frog and Search Console data into the same table. The following tables show respective metrics for best- and worst-performing pages:

Best-performing pages

Worst-performing pages

Results

Let’s see if there are any evident correlations between content metrics and the performance of two groups of pages. 

Word Count and Performance

The word count for both the best-performing and worst-performing pages varies, with some pages having higher word counts and others lower. There doesn't seem to be a clear correlation between word count and performance.

Sentence Count and Performance

Similar to the word count, the sentence count varies among the top-performing and worst-performing pages. There is no evident correlation between sentence count and performance.

Average Words Per Sentence and Performance

The average words per sentence are relatively consistent across both sets of pages, with most falling within the range of 4.7 to 6.3. Again, there is no apparent correlation between average words per sentence and performance.

Readability and Performance

The readability of the content, as categorized by "Fairly Easy" and "Easy," appears consistent across both sets of pages. And yet again, there is no clear correlation between readability and performance.

Conclusion

The content optimization workflow outlined above provides a structured approach to leveraging Screaming Frog's metrics for improving SEO performance. By following these steps, you can gain valuable insights into your website's content and identify areas for optimization.

Through the practical application of this workflow on Slicedbread's blog pages, we demonstrated how to gather data from Search Console and analyze content metrics with Screaming Frog’s text ratio, and identify patterns and trends among high-performing and low-performing pages.

While our analysis did not yield immediate insights into correlations between content metrics and SEO performance, it highlights the complexity of SEO and the multitude of factors that can influence page rankings. 

Ultimately, this workflow could be used as a tool for website owners and SEO professionals to uncover insights, experiment with optimization strategies, and continually improve their content for better SEO with related search queries strategies. 

We encourage you to apply this workflow to your own website and embark on the journey of content optimization for enhanced search visibility.

Happy analyzing and optimizing!