Understanding Byte Limitations in Adobe Analytics Variables

In digital data tracking and analytics, variables are essential for capturing and transmitting information about user interactions. However, each variable has specific limitations on the number of bytes it can hold. These limitations ensure that the data remains structured and manageable.

What is a Byte?

A byte typically represents a single character, but depending on the complexity of the character (e.g., multi-byte characters like emojis or certain non-Latin scripts), a single character may take up to 6 bytes. Any variable exceeding the byte limit will be truncated, leading to potential data loss or inaccuracies.

Byte Limitations for Common Variables

Here is a breakdown of the byte limitations for frequently used variables:

• PageName and Custom Traffic Variables (props):
  Maximum: 100 bytes

• Tracking Codes and Custom Conversion Variables (eVars):
  Maximum: 255 bytes

• URL and Referrer:
  Maximum: 255 bytes

• Hierarchy Variables:
  Total across all levels: 255 bytes

• TransactionID:

  • Can contain any character except commas.

  • Maximum: 100 bytes

  • Multi-byte character support must be enabled if such characters are used to avoid unexpected truncation or issues.

• Purchase ID:
  Maximum: 20 bytes

• Products Variable:

  • 100 bytes for each product category.

  • 100 bytes for each product name.

  • No overall limit within the entire variable.

• Events Variable:

  • No overall limit.

  • Each serialized value: Maximum 20 bytes.

Key Considerations

1. Character Complexity:
   Multi-byte characters (e.g., special characters, emojis, or certain scripts) require more bytes per character. Ensure your variable limits account for these.

2. TransactionID Handling:
   Enable multi-byte character support to avoid issues with non-standard characters. This is particularly important in global implementations where different character sets may be used.

3. Variable Design:
   Be mindful of byte limits when naming products, pages, or creating hierarchical structures to ensure no data is truncated.

4. Testing:
   Test variable outputs in your analytics tool to ensure the data fits within byte limitations, especially for dynamic inputs like URLs or product categories.

By understanding these limitations and planning accordingly, you can ensure your tracking implementation captures complete and accurate data, avoiding truncation issues. If you have any questions, leave them in the comments below!