Introduction to Google Trends (Part Two)

July 23rd, 2008 at 11:53 am | by Michael Giuffrida |

Here’s part two of my introduction to Google Trends. Last post we only dealt with the main line graph of search volume — this time we’re going to look at the Regions, Cities and Languages charts. We’ll also compare multiple search terms and unleash the true Power of the Trends. Or something equally awe-inspiring.

Regions, Cities and Languages

When you look at the results for facebook over the past twelve months, you’ll see three bar graphs at the bottom of the page. These charts are very minimalist:

South Africa? Yes, back in October 2007, Facebook rose above Google as the number one most trafficked site in South Africa, according to Alexa, although it’s back to number 2 now (with Google.co.za at the top and Google.com as #3). And if you look at the past month graph instead, it’s Columbia and Croatia that are at the top. Intuitively, it makes sense that countries experiencing a Facebook boom would show up higher than countries like the US, even though you might expect that countries like the US use Facebook more (especially since it originated here in the States). The reason boom-countries show up higher, I speculate, is that if you’re going to Facebook, you go to facebook.com — you don’t type “facebook” into Google. But if you’re hearing that your friends, or your children, or your neighbors are all on this “facebook” you keep hearing about, you’re gonna type it into Google (or whatever search engine). With a big enough boom, that’ll generate enough search volume for that region and beat out regions like the US and the UK.

It’s not quite that simple, though. Google normalizes and scales these rankings, too, and for good reason. If the top cities were simply those with the highest number of searches originating there, the most populated cities would always be at the top, even if the search isn’t particularly relevant to the city itself. After all, if only 5% of New York City residents search for “facebook”, that’s still going to be almost half a million searches, which will trump 50% of Virginia Beach residents making the same search, amounting to a mere quarter million searches. But surely if half of a city searches for a certain term, that term’s going to be more relevant for that city than for a city in which only five percent makes that search. So Google normalizes as follows.

First, they get a list of the top cities, simply using the number of searches. So New York might be at the top. But then they take that list and re-order the cities by the percentage of all searches from that city that used that term. So even if New York City is at the top of the first list, and Virginia Beach is at the bottom, the list will be re-ordered such that Virginia Beach is above New York City because a higher proportion of all searches from VA Beach are this specific search.

But VA Beach can only make it into the list in the first place if it’s got enough searches to be returned in the first result set, ordered by the raw number of searches. Otherwise, we’d have the reverse of the problem we discussed above. If, in some rural town with twenty residents, only three have ever used Google, and that was only to search for “facebook”, then, granted, 100% of the searches from that town will have been for “facebook”. But putting those three searches at the top of the list, above NYC, would be ludicrous. Risible. Absurd. Et cetera. “Facebook” may only take up 5% of NYC’s Google searches, but that’s still way more relevant than 100% of a town with only three technologically adept citizens.

The regions are all links — clicking them simply refines the search by region, which can also be done with the dropdown menu at the top-right like we did last time. Once you search within a particular region, you then see a chart of popularity by subregion or state in place of the region chart. And once you drill down to a particular subregion or state, that chart goes bye-bye. But you can still look at cities, if you want to drill down even further.

The city and language charts are similar to the region chart shown above. A user’s city and region are determined by his IP address, which really just allows Google to make educated guesses about the location of a search. Language, on the other hand, is directly determined by which version of the site is used.

Comparing Search Terms

Want to compare the popularity of Facebook and MySpace as Google Search keywords?

It’s pretty straightforward. You just get two lines instead of one, and they’re scaled by the same amount. See how facebook is scaled to be 1.00, according to the top bar? Since there are multiple terms, Google only scales the first one so that its average for the time period equals 1.00. So the number you see for myspace, 2.25, means that myspace had an average search volume of 2.25 times the search volume of facebook. In other words, all other terms are scaled relative to the scaling of the first term to equal 1.00. If you look at the graph of just myspace, the scale on the y-axis is different, because now the “myspace” values are the ones scaled to an average of 1.00.

This enables us to look at around September 2007 and say that the term myspace was about twice as popular as facebook. In general, as time increased until about March of 2007, the myspace:facebook ratio grew and grew. Then Facebook really took off worldwide — MySpace’s curve is concave down while Facebook’s is concave up — and the ratio shrunk until that fateful day in March 2008, when searches for Facebook became more popular. Although if you look at the March graph for yourself, you’ll see that Facebook doesn’t usurp MySpace quite as dramatically.

The regions, cities and languages charts also let you compare search terms. Notice how in the graph below, the cities are all ranked by the blue bar — facebook — and the red bars lack any order. Since the data is scaled by facebook, the first term, Google uses that to determine the city rankings, just as it would if only one term were entered. The bars, or search volume, for the cities for the term myspace is shown just for comparison, scaled relative to the first term.

What if we want to see the cities ranked by myspace search popularity? Just use that as your first term. Alternatively, there’s a dropdown box that says “Rank by facebook” — you can quickly change that to “Rank by myspace”, and it would recalculate and redisplay all the graphs, scaled to myspace. Such a graph shows you that, with myspace scaled to an average Search Volume Index of 1.00, facebook’s average SVI is 0.44. This ratio, as you would expect, is approximately equivalent to the old ratio of 2.25:1.00, when we scaled by facebook.

Note that right now, you can only compare up to five terms at the same time.

So there you have it. Now we can have fun comparing different search terms. Here are some examples to get you started:

• windows, apple, linux
• microwave oven, toaster oven, convection oven
• laptop, desktop, tablet
• blueberries, pears, oranges, apricots
• wordpress, movable type

Go back to Part 1 to figure out how the crazy normalizing and scaling stuff works. Part 3, about exporting data, is on the way.

Tags: google, google trends, making comparisons, normalizing, regions, scaling, search volume, time, tool

Related posts

• Introduction to Google Trends (Part One)
• Introduction to Google Trends (Part Three)
• Finding the Most Popular Regions for Search Categories