The following fields are necessary to define a pricing rule:

The following variables can be used for the pricing rules. They can be introduced into your rule with curly double brackets like {{header}}:

An example for the "bus price by distance" could look like:

{% capture ziplong %}{{zip}}#####{% endcapture %}
{% capture zipshort %}{{ziplong|truncate:5}}{% endcapture %}
{% case zipshort %}
  {% when '81...' or '80...' %}
    0
  {% else %}
    {% if distance < 5000 %} 
      5
    {% elsif distance < 10000 %}
      10
    {% elsif distance < 15000 %}
      15
    {% elsif distance < 20000 %}
      20
    {% elsif distance < 25000 %}
      25
    {% else %}
      300
    {% endif %}
{% endcase %} 

In the above example we assign to the variable "ziplong" the value of "zip" and add some hashes to make the string longer.

Then we assign to the variable "zipshort" the value of "ziplong", but truncating it to 5 characters. This means that "80345#####" gets converted to "80...".

If a zip code starts with "80" or "81" (which means that "zipshort" must be "80..." or "81...", then the price is 0.

Otherwise the price depends on the "distance" in meters.

	Note
	Commas must be written with a point - like e.g. 10.05 would be a valid number. If the result of a pricing rule is not a number, then the system converts the value to 0 when calculating a price.

With this functionality, children and parents can be uploaded together in a mass upload and the system can link them automatically together via a “match field”. The “match field” is a column which must exist in both CSV files and which has the same value for parents and children rows. Just indicate the name of the column that will serve as the match field.

Also mass updates of entries already existing in the database can be done via this functionality, but then some rules must be respected.

The upload of the data is specific for the currently selected schoolyear as some parents and children data depends on the schoolyear (e.g. membership of parent or the class level of a child).

The system will upload the files and at first do a test run to check for any errors.

During the import, the following data are eventually generated automatically:

You can also overwrite existing data by a mass import of parents and children.

	Important
	Fields that are left blank in the import file will not overwrite any value in the database records

The following table explains in which priority and how data records are matched with values from the CSV file, so that the system can find the corresponding existing data record in the database for overwriting the parents and the children:

The CSV files must comply with the following rules:

	Tip
	If you do a CSV export of the parents and the children (e.g. in the demo client if you do not have any own data yet), you will get exactly the format that you need. This also a good way to do mass updates of data with the help of a spreadsheet program outside the system and then import them again.

The format definition for special field types is the following:

The CSV file with the parents data must consist of the following column headers and column value formats. The order is not important and only those columns must be provided that you need.

The CSV file with the children data must consist of the following column headers and column value formats. The order is not important and only those columns must be provided that you need.