In the book The Search author John Battelle describes Google as “a database of intentions.” If you prescribe to that classification, the Google tools I’m going to employ will provide some interesting insight into shopper intentions and how that information can be leveraged.  The first tool I’m going to use is Google Insights for Search.  Insights for Search is another Google Experiment, this time one that allows one to peer inside the volume of keyword (or phrase) searches over time and geographical regions.  Let’s see what we can learn.

What can Google tell us about Valentine’s Day shopping?

Our hypothesis will be that people are still shopping online, but they’re likely looking to get a bargain.  The hypothesis ins’t a stroke of genius, but the devil is in the details.  If we’re going to launch a campaign or put some copy on our homepage regarding our holiday deals, let’s use words that resonate with online shoppers.  Further, since we’re theoretically doing this exercise the week before the holiday, Pay-per-Click is a definite medium we would target.  We’d want to know not only what keywords we might want to focus on for our ads to display, but also what keywords to place in generalized ads to get Internet searcher’s attention.

Let’s take a look at the frequency of terms being searched upon in the United States over the past couple of years using Insights for Search.  The search phrases I’m going to take a look at are:

• Valentine’s Day
• Valentine’s Day roses
• Cheap Valentine’s Day gift
• Valentine’s Day specials
• Valentine’s deals
• Valentine’s discount

Search volume for “Valentine’s Day”

Let’s start by looking up a generalized term about the holiday to use as a baseline for interest overall around the topic over time.  The graph below shows search volume for “Valentine’s Day” from January of 2007 until February of 2009 in the United States.  To be sure we’re focused on shopping, we’ve restricted our inquiry to the “Shopping” category.   The February ‘09 data for this year, term and region is reported to the current week, but we’ll focus on the week before the holiday for all of our analysis.

• 2007: 60
• 2008: 48
• 2009: 53
  

Before we move forward, let’s be clear about what these numbers mean. The numbers on the graph do not allow us to understand the total number of searches for this term in the United States. I’ll let Google tell you what the numbers mean as opposed to paraphrasing:

The numbers on the graph reflect how many searches have been done for a particular term, relative to the total number of searches done on Google over time. They don’t represent absolute search volume numbers, because the data is normalized and presented on a scale from 0-100; each point on the graph is divided by the highest point, or 100. The numbers next to the search terms above the graph are summaries, or totals.

If I check the points on the graph, last week’s number (the week before Valentine’s Day) registers 60. The week before Valentine’s Day in ‘08 is lower, at 48, and that week in ‘07 clocked in at 53.  This shows an increase in the volume of shopping related searches for “Valentine’s Day” for the week before Valentine’s Day this year in the United States as compare to the previous two years.

For the purpose of this exercise, I’m also going to scale data to a 100 point scale when our results fail to do so to make comparisons easier.  Here’s what that will look like for the aforementioned data:

• 2007: 60 (100)
• 2008: 48 (80)
• 2009: 53 (88)

As above, when data is extrapolated to a 100 point scale I’ll include it in parenthesis.  Graph data will not be changed.

Not following the trend

So, we’ve established that the volume of searches for Valentine’s Day gifts the week before the holiday has increased considerably in comparison to the two previous years.  To help illustrate our point regarding the discount terms, I’m going to run an example non-discounted term: “Valentine’s Day roses.”

Search volume for “Valentine’s Day roses”

Below is our result.  Search volume is fairly close to 2008 levels, both of which years are lower than 2007.

• 2007: 62 (100)
• 2008: 43 (69)
• 2009: 42 (68)

So, what about the thrifty shopper?

Moving on to bargain hunting, let’s run the same time period but try some phrases that would show people searching with the intent of finding discounted Valentine’s Day gifts. After trying a few phrases and seeing volume increases that were generally aligned with the increase in searches for “Valentine’s Day,” I found several that also showed strong upticks, specifically around the idea of getting a bargain.

Search volume for “Cheap Valentine’s Day gift”

This first graph is for “Cheap Valentine’s Day gift.” For the weeks preceding Valentine’s day, 2007’s interest level is at 40, 2008’s is 36, and 2009 holds the top end (100). So, while shopping-related searches have increased for the term “Valentine’s Day,” searches for those looking for a cheap Valentine’s Day gift have also increased, but to a much more significant degree.

• 2007: 40
• 2008: 36
• 2009: 100

Let’s look for more terms and see if we can find higher variances while still reflecting intent.

Search volume: “Valentine’s Day specials”

Another term I thought that might indicate an intention to find a deal was the phrase “Valentine’s Day specials.” Again, the result shows the same trend.

• 2007: 31 (62)
• 2008: 28 (56)
• 2009: 50 (100)

Search volume: “Valentine’s deals”

Bingo. “Deals” seems to be a word that people are using far more when shopping for Valentine’s Day this year and looking for a bargain.  Do note that I dropped “Days” from the phrase as I wasn’t getting results, but felt “Valentine’s” would be enough of a qualifier.  Take a look:

• 2007: 19
• 2008: 29
• 2009: 100

Search volume: “Valentine’s discount”

“Valentine’s discount,” the last phrase I’m including, follows suit with a considerable uptick.

• 2007: 41
• 2008: 42
• 2009: 100

Do we have any actionable data yet?

Not really.  We still need more information to paint the full picture.  If we were going to use this information to understand linguistics and what words were being used more frequently when shopping online for Valentine’s Day compared to previous years, this data is helpful.  But, we really need to get a better idea of how much search volume we’re talking about.

If a phrase was searched on three times during the week before Valentine’s Day in 2008 and 10 times the week before Valentine’s Day this year, we’d see a huge volume increase year-over-year, but creating a marketing campaign based upon that information wouldn’t be the wisest move.

In my next post I’ll take what I’ve learned so far and leverage another of Google’s tools to show how this information could be used to make online marketing decisions.

Related posts:

1. Google AdWords keyword tool now providing search volume
2. Have you been getting Google keyword search volume right?
3. Android and SEO/SEM? Dream on.

From the Google Inside AdWords blog:

Based on advertiser feedback, and our commitment to provide useful tools and information for our advertisers, we’ve now added search volume data to the Keyword Tool.

…

These approximate numbers are intended to provide better insight into keywords’ monthly and average search volumes than previously provided by the tool.

If you’re new to Google AdWords, their Keyword Tool previously provided a visual cue which indicated search volume.

If you want to check it out and your not currently an AdWords user you can use Google’s AdWords external keyword tool.

Related posts:

1. Have you been getting Google keyword search volume right?
2. Google Labs provides popularity of keywords, sites
3. What Google tells me about Valentine’s Day shopping – Part 1

My gooruze page: smcandrew.gooruze.com
My Sphinn profile: smcandrew on Sphinn

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3. Math and… better math on the State of the Union

Time’s visualization went with a view which scaled the size of the words in proportion to their frequency.  Think tag cloud.  The basic visualization technique was posted under the heading “State of the Union by the Numbers”.  Yawn.

With a simple explanation of how the technique works, a teenager could whip this out with a steno pad and a pencil.  The hardest part would be making it through the whole speech.  Reporting.  Not analysis.

Slate Magazine took a very different approach.  And they knew their results were in a different neighborhood than what Time and others were up to.  Slate’s work-up relied on the slick natural-language processing algorithms baked into Crawdad Technologies’ software.  A bit different than a simple counting exercise.  An excerpt from the Slate article (A Computer Reads the State of the Union):

By natural-language-processing standards, simply counting the frequency of words is unthinkably primitive. As computers get better at understanding the structure of sentences—think about that corrugated green line under a sentence in Microsoft Word reminding you to avoid the passive voice—they can do a much more thorough job reading political speeches and offering their insights.

Needless to say, Slate’s experiment is just a bit more interesting.  Looking at not only last night’s State of the Union, but all of Bush’s Presidential addresses, the software identified “four distinct voices” during his time in the White House:

1. “Domestic Bush” – 2001
2. “Security Bush” – 2002, 2003
3. “Visionary Bush” – 2004, 2005, 2006, 2007
4. “Legacy Bush” – 2008

The table below provides further insight.

For another example of their technology in action, visit the Wonkosphere, currently tracking buzz online surrounding the 2008 Presidential race.  Learn more about Crawdad Technologies and their software at their web site, crawdadtech.com.

What does this have to do with online marketing?  Good natural-language processing technology is a Swiss Army knife for measuring buzz—and its tone—online.  Before stumbling across Crawdad I had planned to post about a few companies I’ve been interested in whose offerings cater to online marketers.  As with Crawdad’s Wonkosphere, these solutions focus on monitoring social media outlets.  A few of those companies: Nielsen (BuzzMetrics), Radian6 and Relevant Noise.

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