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Convert value data processor

How to use the convert value data processor in BlueConic CDP connections for your use casesIn select BlueConic connections, you can use the Convert value data processor to replace values in your data import or export with ones that are clearer for segmentation. For instance, if you use single letters like "M" and "F" to indicate gender in your data set, you can convert those values to the full words "Male" and "Female."

To configure values for this data processor, you select a specific column from your data set and then input an original value and a new value to replace it; the system automatically changes that original value to the new one in your import or export.

Adding this data processor to your tenant

To use the Convert value data processor, you first need to add it to your tenant through the Plugins page (BlueConic Settings > Plugins). Click the Add plugin button at the top of this page and look for "Convert value" in the gallery. Then, click the Add plugin button underneath its description to install it.

Adding this data processor to a connection

The Convert value data processor is available in all BlueConic connections that use data processors. You can review the complete list here.

Each of these connections has a specific step on its import and/or export goal page to add data processors (e.g., "Correct your files before the import," "Modify your data before the export"). In that step, use the search box to add "Convert value"; once added, it appears in a numbered list of data processors activated for that connection.


For more information, review the full article Using data processors with BlueConic connections.

Configuring this data processor

The Convert value data processor requires some configuration to ensure that BlueConic imports or exports the data as specified. To make these configurations, click the cogwheel icon next to "Convert value" in the numbered list of data processors. This opens a window with the following settings:


  • Column: Select the field that contains the value(s) you want to convert.
  • Multi-value separator: Add a character such as a semicolon (;), asterisk (*), or comma (,) to split one value into multiple values. For instance, using a comma as the separator, the single value “L, XL, XXL” would be split into three values. Conversions would then apply to each of those values (e.g., "L" converts to "Large").
  • Target column: Enable the checkbox to move any converted values to a new column, with the original values kept in the original column.
  • Target column name: Enter the name of the new column for your converted values (if the "Target column" box is checked above).
  • Unknown/empty value: Apply one of the following actions whenever there is a blank value or a value that does not have a conversion in the Mappings section of this window:
    • Keep original value: Use whatever value is presently included.
    • Use empty string: Clear the value entirely, making it blank.
    • Use default value: Use a default value specified under Mappings below.
  • Mappings: Input an original value in the field to the left and the new value it should convert to in the field to the right. Click the Add mapping button for each new row of values you want to add.
    • Tips:
      • Make sure to select your preferred option under "Unknown/empty value" above so that any values not mapped here display as desired in the import or export.
      • To remove a row, hover your cursor over that row and click the X that appears at the far right.
    • Note: When the "Use default value" option is selected above, the top row of this Mappings section will always be used to input your default value, as shown here:

How to use data processors for BlueConic CDP imports and exports to clean up data for your CDP use cases

Sample scenarios for using this data processor

Review the following scenarios to see how you would set up the Convert value data processor to clean up your data for optimal segmentation:

Scenario Settings Mappings (original and new values)
For a retail chain, customers are linked to their closest store; however, each store is identified by a numeric value. The company wants to segment by the store's full name instead of a number.

Column: Store

Multi-value separator: ,

Target column: Checked

Target column name: Store Name & Location

Unknown/empty value: Use default value

Default value = Taylor Northeast, USA

1a = Taylor Store Flagship, Manhattan

1b = Taylor Store, Brooklyn

2 = Taylor Store, Boston

3 = Taylor Furniture, Providence

4 = Taylor Furniture, Philadelphia

A university uses four-letter abbreviations for each academic department but wants to segment by full department names instead.

Column: Department

Multi-value separator: ,

Target column: Unchecked

Unknown/empty value: Keep original value

BIOL = Biology

CHEM = Chemistry

COMP = Computer Science and Software Engineering

MATH = Mathematics and Statistics

PHYS = Physics


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