Maintain or increase website traffic.
Using the URL might make it easier to pull in metrics in later steps. Use a content triage template, or create your own.
For each page of content, list its last modified date. Calculate the number of days since it was updated.
Calculate this automatically in a spreadsheet using this formula:
List the amount of organic traffic that each page brought to the website, and the same metric for the same period in the previous year.
For example, 2019: 5,000 organic views. 2020: 3,500 organic views. You can use the Google Analytics Add-on to automatically add both of these metrics to your spreadsheet.
Record the number of impressions generated over the past year, how many referring domains it has, and the URL rating for each page.
Use Google Search Console to see how many impressions the post has generated in the past year. We want a sense of how many users the post could bring to the site, regardless of how many it brings now. For referring domains, anything that’s going to rank highly and drive lots of traffic, will almost certainly require a decent number of links.
The URL rating, pulled from Ahrefs, is a hedge against the potentially misleading number of referring domains for example, one link from The New York Times beats 20 from scraper sites.
On a scale of 1-4, roughly estimate the risk of each page including outdated information, with 1 being low-risk and 4 being high-risk.
An example of a high-risk post is our Google Analytics implementation guide. Every time a menu item or UI design element changes in Google Analytics, we have to update the post.
A low-risk post might be one on crafting a value proposition. Some examples and screenshots might start to look dated after a few years, but the core advice and process stays the same.
If you need to scale for thousands of pages, use the category or tags as a rough guide, for example, all Analytics posts get scored a 4 and Copywriting posts are scored a 2.
You’ll see which quartile each metric falls into. For example, a URL rating in bucket #4 means it’s in the highest quartile and should be prioritized for a content update over one in bucket #1.
For example, CXL’s standard weightings for content updates are:
- Age: 3.
- Organic Users 90 days: 1.
- YOY Organic Users 90 days: 2.
- Impressions: 1.
- Referring domains: 0.5.
- URL rating: 0.5.
- Outdated risk: 1.5.
Add up the weighted, quartiled metrics for each page. Turn it into a percentage of the potential risk, and add it to the spreadsheet.
Calculate this automatically by specifying the weightings in the spreadsheet and then using a formula like:
= (metric1 * weighting1 + metric2 * weighting2 + ... metricn * weightingn) / (4 * [sum of all weightings]) * 100
Sort the spreadsheet by the calculated risk column, from highest to lowest. Prioritize updating those at the top.
Last edited by @hesh_fekry 2023-11-14T15:55:14Z