Saturday, 4 May 2013

What Do Google Trends Scores Mean?

What Google Trends Tells Us:

Sample Data:

Google Trends tells us that its scores are based on an analysis of a portion of search volume. This means the scores are only based on a sample of available data, rather than all available data.

How are Interest Over Time Scores Calculated?:

Google Trends  tells us that it scores are relative. An “interest over time” score for any particular day / week / month is awarded on the basis of its actual search volume relative to the actual search volume of the other days / weeks / months. A score of 100 is always awarded to the day / week / month with the highest relative search volume, with the other days/ weeks / months scaled between 0 and 100 accordingly.

When a term is analysed over time within global parameters the scores are calculated using an average score (based on the scores returned from each country). This prevents search terms with high popularity in countries with heavy internet usage constantly achieving high scores.

How are Regional Interest Scores Calculated?: 

In the case of “regional interest” scores, Google Trends uses relativity in order to prevent countries with high internet usage automatically receiving the highest scores. The scores are calculated by comparing the relative popularity of the search term within each region. This data is then scaled between 0 and 100, with the country with the highest relative interest receiving a score of 100.

How Broad is the Search Volume Data Used to Calculate Scores?:

Google Trends offers users the ability to search for an exact phrase by enclosing it in quotation marks. According to the Help Centre when this function is used, scores generated are based on "specific order" search volume. This means that data from searches that include the inputted search terms in the specific order they have been entered will be used. No synonyms or variations are considered.

Google Trends also states that where no "quotation marks" are used, results include searches for the inputted terms in any order and may also include extra terms. No synonyms or variations are considered.

Wednesday, 1 May 2013

Cross-Analysis: Keyword Tool and Google Trends

The steps below can be used to help estimate the value of Google Trends scores in terms of actual search numbers.


Please read the caveat at the end of this article, which discusses the impact of normalisation / scaling and the accuracy of Keyword Tool.

Step 1:

Keyword Tool should be used to ascertain the monthly search volume for the term being analysed.

This figure should then be multiplied by 12 and divided by 365, to give the average daily search volume.

For example, if the search term hummingbird is reported as having 1 million searches a month, the average daily search volume is 32,877:

(1,000,000 x 12)

Step 2:

The average Google Trends score for the  search term over the previous twelve months should be calculated.

For example,  if between May 2012 and April 2013 hummingbird achieved scores totalling 384, its monthly average was 32:


Step 3:

The average daily search volume should be divided by the average Google Trends score. This provides the value of 1 Google Trends score point for one day.

For example, in the case of hummingbird, the value of 1 Google Trends score point for one day would be 1027:


Step 4:

In order to now ascertain the actual search volume within any month, the value of 1 score point should be multiplied by that month’s score and then multiplied by the number of days in the month.

For example, if the Google Trends score for hummingbird was 62 in June 2011, this would equate to 1,910,220 actual searches:

1027 x 62 x 30


I have good reason to believe that this calculation provides a reasonably reliable result. Read about why I think this calculation works here: Working With Google Trends and Keyword Tool Together.

However, the calculation does make one pretty big assumption.  It assumes that Keyword Tool data is updated monthly, which seems unlikely.

If this assumptions is wrong, the calculation above can at least be used as an estimator of search interest, where Google Trends reports a year-on-year increase in popularity.

The calculation also ignores the fact that Google Trends scores are both normalised and scaled. This means that scores over time do not have a direct quantitive correlation. Instead, they are determined by two steps:

(1) the absolute search volume relative to the total search queries received by Google

(2) the relative popularity on each day / week / month compared to the relative popularity of other days / weeks / months and then scaled between 0 and 100.

This has a significant impact where internet use rises or falls. In theory, a score of 10 on a peak internet  day could represent more actual searches than a score of 100 on a slow internet day. However, as my calculation uses a Google Trend's one-year average as it's starting point, the impact of such anomalies should be diminished.

The calculation will naturally work best in territories that have stable internet usage and with terms that are not radically more popular on certain days (ie Ebay on sundays) or at certain times of the year.