As you have read before when doing a report production run you can specify a grouping variable in the settings. This will cause a report to be created for each of the categories defined for the selected variable. This allows you to very quickly create reports on f.e. store level, where store is a variable in your dataset.

Now an important part of reporting is comparing data for on unit to that of another unit, in the above example of store level reports, you might for example want to show an NPS score on regional level in the report next to the NPS score for the shop.

In DataDynamic Reporter this can easily be achieved by running multi level reports.

When going to the settings screen for report production, you can ctually use the field for specifying the group variable to specify more then 1 variable separated by a comma.

When multiple variables are selected, DataDynamic will start the reporting process by looping through the categories of the first variable, applying a filter and producing a report foreach of them. Then foreach category it will loop through the categories of the second variable, using the output report of the first variable category as a template for this report.

As you might have already seen, most commands and links from charts and tables can hold an additional run level parameter. The system will check this runlevel against the current runlevel where 1 (one) is the first selected variable, 2 (two) the second etc. A chart will only be changed if it has no level specification or when it has a level specification equal to the current run.

This concept allows you to create a single NPS table in DataDynamic and then to link it to two PPT charts, the first showing the NPS on regional level, the second on shop level. To achieve this you can  add a level specifier of 1 to the first chart and 2 to the second which will give you the desired output as the chart modified on level 1 will not be changed when producting the store level report.

This is a very powerfull feature of DataDynamic reporting as it easily allows you to automatically combine all sorts of information from different cuts of the data with minimal effort.