[Build Backlinks Online] Google's "Multi-Week" Algorithm Update

Build Backlinks Online has posted a new item, 'Google's Multi-Week Algorithm
Update'


Posted by Dr-Pete

Back on June 21st, Matt Cutts replied to a tweet about payday loan spam with
an unusual bit of information (reported on Search Engine Roundtable):




The exact timeline was a bit unclear, but Matt seemed to suggest a prolonged
algorithm update covering as many as three weeks. Four days later, we tracked
our highest temperature ever on MozCast, followed by more record highs:




Seven days during the "multi-week" timeline showed temperature spikes near or
above 90, with six of those days exceeding the severity of the original Penguin
update.

Was It A MozCast Glitch?

Let me perfectly honest â Google rankings are a moving target, and
tracking day-to-day flux has proven difficult at best. Any given temperature on
any given day is prone to error. However, this was a sustained pattern of very
high numbers, and we have no evidence to suggest a glitch in the data.


There were some reports that other tools were not showing similar spikes, but
some of these reports were based on apples-to-oranges comparisons. For example,
if you look at SERPmetrics flux data and isolate just page 1 of Google (which is
what MozCast tracks), you'll see this:




Sorry, it's a bit hard to see the dates on the reduced image, but the two
spikes equate to roughly June 28th and July 4th, with a smaller bump on June
25th. While they're not an exact match, these two data sets are certainly
telling a similar story.




Was It A Large-scale Test?

This is a much harder question to answer. Our beta 10K data set showed similar
patterns across multiple C-blocks of IPs, so we have no reason to believe this
was specific to one or a very few data centers.


What if Google made a massive change one day, though, and then reverted it?
Theoretically, we would see two days of high MozCast temperatures, but if we
looked at the two-day flux (instead of two one-day numbers), the temperature
would be very low. While this multi-day flux is theoretically interesting, it
can be very hard to interpret in practice. Some rankings naturally change, and
Google can roll out multiple small updates in any given week.


If we look at the overall flux between the start and end of recorded spikes
(June 25 - July 4), we get a MozCast temperature of 120.3, not much higher than
the one-day temperature on June 27th. The average daily temperature for this
period was 92.5. Now, let's look at a similar time period (May 28 - June 6)
â the average temperature for that period was 66.8, and the multi-day
temperature across the entire period was 114.7.


Comparing the two time periods, the overall flux for the period of record
temperatures was roughly the same as the peak and about 30% higher than the
multi-day average, whereas the overall flux for the quieter period was 72%
higher than the average. This is an inexact science at best, and we don't have a
good historical sense of multi-day patterns, but my gut feeling is that some of
the multi-week update involved changes that Google tested and later rolled back.

What About PMDs & EMDs?

In my post on the June 25th temperature spike, I reported a noticeable
single-day drop in partial-match domain (PMD) influence. That post happened very
early in the multi-week update, so let's look at the PMD influence data across a
30-day time period that includes all of the high-temperature days:




While there was a lot of movement during this period, you can see that PMDs
recovered some of their initial losses around July 4th. The overall trend is
downward, but the June 25th drop doesn't appear to have been permanent.


It's interesting to note, even if not directly relevant to this analysis, that
the long-term trend for PMD influence in our data is still decidedly downward.
Here's a graph back to the beginning of 2013:




So, how have EMDs fared? They seem to show a similar pattern, but in a much
tighter range. Scaled to the same Y-axis as the PMD chart above, we get this:




The EMD data is fairly consistent with Dr. Matt Peters' early report on our
2013 Ranking Factors study. Keep in mind that we are measuring two different
things â the correlations show how well PMDs/EMDs ranked compared to other
domains, whereas MozCast tracks how many PMDs/EMDs ranked across the data set.
If the number of total PMDs drops, but they rank roughly as well, the
correlations will remain stable, but the "PMD Influence" metric will drop. In
other words, the correlations measure how well PMDs rank, whereas MozCast
measures how many PMDs rank.

Which PMDs Lost Long-term?

There's one more question we can ask about the drop and subsequent recovery in
PMD influence. Did the PMDs that fell out eventually come back, or were they
replaced by different PMDs? The metric itself doesn't tell us, but we can dig
deeper and see who lost out long-term.


On the initial drop (between June 25-26), 62 PMDs fell out of our public 1K
MozCast query set. New PMDs always enter the mix, so the net drop is smaller,
but 62 PMDs that were ranking on June 25th weren't ranking on June 26th. So,
let's compare that list of 62 to the data on July 5th â after the apparent
recovery. On July 5th, 37 of those PMDs (60%) had returned to our data set. This
certainly suggests some amount of legitimate recovery.


So, which losing PMDs failed to recover? Here's the complete list (query
keywords in parentheses):


californiacarshows.org (car shows)
digital-voice-recorder-review.toptenreviews.com (voice recorder)
fullyramblomatic-yahtzee.blogspot.com (yahtzee)
virginiamommymakeover.com (mommy makeover)
www.appliancepartscenter.us (appliance parts)
www.appliancepartssuppliers.com (appliance parts)
www.campagnolorestaurant.ca (campagnolo)
www.campagnolorestaurant.com (campagnolo)
www.capitalcarshows.com (car shows)
www.chicagoweddingcandybuffet.com (candy buffet)
www.dollardrivingschool.com (driving school)
www.elitedrivingschool.biz (driving school)
www.etanzanite.com (tanzanite)
www.firstchoicedrivingschool.net (driving school)
www.fitzgeraldsdrivingschool.com (driving school)
www.monogrammedgiftshop.com (monogrammed gifts)
www.moscatorestaurant.com (moscato)
www.newjerseyluxuryrealestate.com (luxury real estate)
www.ocsportscards.com (sports cards)
www.phoenixbassboats.com (bass boats)
www.rvsalesofbroward.com (rv sales)
www.sri-onlineauctions.com (online auctions)
www.stoltzfusrvs.com (rvs)
www.vibramdiscgolf.com (vibram)


It's not my goal to pass judgment on the quality of these domains, but simply
to provide data for further analysis if anyone is interested. You can see that
there are a few examples of multiple PMDs falling out of a single query,
suggesting some kind of targeted action.

How Did The Big 10 Do?

In MozCast, we track a metric called the "Big 10" (I did my grad work at U.
Iowa, so I should probably have thought twice about that name) â it's just
a count of the total percentage of top 10 ranking positions held by the 10 most
prominent sites on any given day. Those sites may change day-to-day, but tend to
be fairly stable. Looking back to the beginning of 2013, we see a clear upward
trend (this graph starts on January 8th, due to a counting issue we had with
YouTube results at the beginning of the year):






The "Big 10" gained almost 2-1/2 percentage points in the first half of the
year. Some of the gain across the year represents a shuffling of sites in the
mix (Twitter falls in and out of the "Big 10", for example, and the root eBay
domain struggled earlier this year), and some of this is a symptom of other
changes. As Google gets more aggressive about spam, the sites that already
dominate naturally tend to take more spots.


I thought it would be interesting to look at these numbers alongside the
year-to-date PMD and EMD numbers, but the "Big 10" doesn't seem to tell us much
about the multi-week update. As a group, they moved only a fairly small amount
between June 25th and July 5th (from 14.97% to 15.17%). Whatever Google tested
and rolled out over this period, it didn't dramatically advantage big brands in
our data set.

What Happened, Then?


Unfortunately, the patterns just aren't clear, and digging into individual
queries that showed the most movement during the multi-week update didn't reveal
any general insights. The volatility during this time period seems to have been
real, and my best guess is that while some changes stuck, others were made and
rolled back. Google may have been doing large-scale testing of algorithm tweaks
and refining as they went, but at this point the exact nature of those changes
is unclear. Between the multi-week update and Google's announcement of 10-day
Panda roll-outs, it appears that we're going to see more prolonged updates.
Whether this is to mitigate the impact of one-day updates or make the update
process more opaque is anyone's guess.

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