This alone saves us hours of spreadsheet work. We don't need to write crazy formulas, VLOOKUPS, or pivot tables…the data comes out of BigQuery completely ready to review.

Organized and Formatted. Everything we need to make an informed SEO decision about every page on your website.

The models are trained to read pages to determine “category” (blog post, product page, etc). This is so impactful because it allows us to analyze sections of your website (i.e. blog posts only) so we’re comparing “apples to apples” (as opposed to comparing performance of a blog post vs contact page).

We use this especially for large, enterprise sites who have a ton of data to sift through.

The main output from the WAM is knowing what to do with every page on a site from an SEO point of view. This serves as the perfect roadmap for any SEO campaign, as we know exactly how we’re going to optimize the entire site.

Before pushing to BigQuery, we used to run the WAM manually. We’d inspect each URL and assign an “Action” to take (i.e. 301 redirect, merge content, target with links, etc). This took too long.

So we trained BigQuery to understand “if” statements to automatically generate Actions.

For example…

• If a page is 404 but has external links; Action = 301 redirect to appropriate page
• If a page is 404 but has external links and internal links;Action = 301 redirect to appropriate page, remove internal links

We literally wrote hundreds of these and we continuously add more every time we run a WAM for clients.

We break these Actions down into 4 categories for simple analysis:

• Technical Actions
• On page Actions
• Off page Actions
• Site architecture Actions

We then review these in Sheets, or neatly organize on the Data Studio report.

The WAM can be refreshed daily, weekly, yearly; on whatever we’d like.

For clients, we set them up to run monthly. Each month the WAM refreshes to find new issues, but also report on the progress of past ones.

It’s a simple, cost effective way to monitor SEO changes and progress over a campaign.