Using SQL like conditions, this tool allows you to mine for very specific numbers. The best way to explain this tool is by example.
To begin with, you need to select a data point of interest. To select a data point you can either choose one from the drop down list, or start typing a data point’s name in the input field to filter the list.
Now, let’s say we select the data point ‘main.buyer.location’ in our NDA example model, which is the same question we used in the example of the previous chapter, Trend Finder. If we add no conditions, the tool will mine all unique answers, in the same way as the Trend Finder does.
But what if we only wanted to collate answers within cases that meet certain criteria. Start by adding a condition, and let’s set that condition to only count the cases where the data point ‘main.enterprise.location’ is equal to ‘The Hague’. Now the engine will only count the ‘main.buyer.location’ unique answers in cases where the ‘main.enterprise.location’ is equal to ‘The Hague’:
You could add as many conditions as you like, and they can track any data point. All conditions except the first also require a conjunction key, either ‘AND’ or ‘OR’, which will determine how the engine decides whether or not to count a value per case.
For example, we may only be interested in cases where the buyer location was not London:
Be careful when using the ‘OR’ conjunction - it will negate the other conditions.
For example, if we added another condition to our example above, and made it:
'OR' 'main.enterprise.location' '=' 'London'
You might expect to now see all buyer locations except London, where the enterprise location is either The Hague or London You’d be wrong.
The final condition is joined with an ‘OR’ conjunction, which means that the engine will add all cases where the enterprise location is London regardless of the first two conditions:
As you can see, buyer location ‘London’ is present in the output despite our condition:
This is because there was a case where the buyer location was London, and the enterprise location was also London. Because there was an ‘OR’ conjunction, that case was counted.