Post by account_disabled on Mar 5, 2024 4:34:56 GMT
The the coverage. The broader the ranges the lower the specificity and higher coverage. If we only had one range that was from zero to a billion we would have horrible specificity and perfect coverage. If we had millions of ranges we would have perfect specificity but no coverage. Given our weightings and parameters we identified the best possible arrangement. Im pretty sure theres a mathematical expression of this problem that would have done a quicker job here but I am not a clever man so I used my favorite tool of all brute force. The idea was simple.
We take the maximum and minimum boundaries of the search volume Greece Mobile Number List data provided by Google Keyword Planner lets say... between and billion. We then randomly divide it into ranges testing a reasonable number of ranges somewhere between and . Imagine randomly placing dividers between books on a shelf. We did that except the books were keyword volume numbers. We assign a weighting to the importance of specificity the distance between the average of the range min and max from the keywords actual average monthly search. that were close to the average for the year. We assign a weighting to the importance of coverage the likelihood that any given month over the last year falls within the range. For example we might say its important that were close to the average each month.
We test randomly selected keywords and their Google Keyword Planner volume against the randomly selected ranges. We use the actual average of the last months rather than the rounded average of the last months. We do this for millions of randomly selected ranges. We select the winner from among the top performers. It took a few days to run the longer we ran it the rarer new winners were discovered. Ultimately we settled on different ranges a nice whole number for.
We take the maximum and minimum boundaries of the search volume Greece Mobile Number List data provided by Google Keyword Planner lets say... between and billion. We then randomly divide it into ranges testing a reasonable number of ranges somewhere between and . Imagine randomly placing dividers between books on a shelf. We did that except the books were keyword volume numbers. We assign a weighting to the importance of specificity the distance between the average of the range min and max from the keywords actual average monthly search. that were close to the average for the year. We assign a weighting to the importance of coverage the likelihood that any given month over the last year falls within the range. For example we might say its important that were close to the average each month.
We test randomly selected keywords and their Google Keyword Planner volume against the randomly selected ranges. We use the actual average of the last months rather than the rounded average of the last months. We do this for millions of randomly selected ranges. We select the winner from among the top performers. It took a few days to run the longer we ran it the rarer new winners were discovered. Ultimately we settled on different ranges a nice whole number for.