On December 3rd, Google announced that the latest core update would be rolled out. Initially, most of the impact appeared to be on that date, with MozCast soaring to 112.4 ° F:
We measured the above average ranking flow in the three days prior to the update announcement and a few days after the announcement, but most of the flow appeared to occur on the roll-out day. (The dotted line represents the 30-day average prior to December 3.)
How did December 2020 compare to other core updates?
While Google's December core update was technically the third largest named core update, it was very similar in terms of the measured impact on the core update from May 2020 and the "Medic" update from August 2018.
Winners and more winners
In May, I worked pretty hard on reports from winners and losers. I don't want to discourage all major update analytics, but our rush to publish can lead to misleading results, especially with multi-day updates. In May I decided to do a 7-day update analysis and compared the whole week before the update with the full week after. This helps to better reflect multi-day roll-outs and remove the noise from naturally high-flow websites such as B. News sites (which often grow and decrease on a weekly cycle).
Below are the top 20 overall winners in our MozCast dataset by percentage profit:
Note the 1-day comparisons (December 4th vs. December 2nd) vs. 7-day comparisons and especially the orange values - five of our top 20 achieved significantly more gains after the majority of the update hit. We've seen some reversals too, but most of the sites recorded their gains and losses at the start of this update.
Another challenge in analyzing winners and losers is that it is easy for large percentage wins and losses from small websites to overshadow larger websites that may have a much greater impact on traffic and sales. Here are the top 20 winners of the top 100 websites in our tracking set:
Note that New York Magazine saw significantly more gains after December 4th. Of course, for any particular site, we cannot prove that these gains were due to the core update. While Apple's App Store was the big winner here, some large websites saw growth of over + 20%, and eBay did particularly well.
The best content / pages
We tend to focus on winners and losers at the domain level simply because grouping by domains gives us more data to work with. However, we also know that many of the changes made by Google work at the page level. So I decided to try something new and examine the winners on each page of our dataset.
I stuck to the top 100 most visible pages in our dataset, removed home pages and then only looked at the 7 day change (before versus after). Here are the top 10 winners along with their 7 day winnings (I went with a text list so you can click these pages if you want to explore):
- + 126% – https://www.cashnetusa.com/paydayloans.html
- + 65% – https://www.trulia.com/rent/
- + 58% – https://www.customink.com/products/t-shirts/4
- + 53% – https://turbotax.intuit.com/tax-tools/calculators/taxcaster/
- + 41% – https://www.whitepages.com/person
- + 40% – https: //www.goodhousekeeping.com/home/gardening/advice / …
- + 38% – https://www.nerdwallet.com/mortgages/mortgage-rates
- + 33% – https: //www.bankrate.com/calculators/mortgages / …
- + 26% – https://www.wellsfargo.com/mortgage/rates/
- + 23% – https://smartasset.com/mortgage/mortgage-calculator
It's interesting to see a number of shifts in financial services, particularly mortgage rates and calculators. Of course, we cannot speak of causality. It's entirely possible that some of these pages were moved up because competitors lost ground. For example, https://www.mortgagecalculator.org lost 23% of its visibility in a 7-day comparison over 7 days.
While it is interesting to scour these pages looking for general topics, please note that a short-term increase in rank does not necessarily mean that a particular page is doing something right or has been rewarded by the core update.
What trends do you see?
Now that the dust has largely settled, do you see any clear trends? Are certain page types better or worse than before? As an industry, core update analysis has a long way to go (and to be fair, it's an incredibly complex problem), but I think the key is that we try to put a little more pressure and a little more each time to learn. If you have any ideas on how to extend this analytics, especially at the page level, let us know in the comments.