**Note:**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)__

365

__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**:

__382__

12

__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**:

__32,877__

32

__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

__Caveat:__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.

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