Purpose:

The Python script tags campaigns as ‘Brand’ or ‘Non-Brand’ based on the presence of the word ‘Brand’ in the campaign name.

To Elaborate

The script is designed to categorize marketing campaigns into ‘Brand’ or ‘Non-Brand’ categories based on their names. This is particularly useful for campaigns that lack explicit labeling, allowing for a structured approach to budget allocation and performance analysis. The script examines each campaign name to check for the presence of the word ‘Brand’. If found, the campaign is tagged as ‘Brand’; otherwise, it is tagged as ‘Non-Brand’. This categorization helps in organizing and analyzing campaign data more effectively, ensuring that marketing efforts are aligned with strategic branding goals.

Walking Through the Code

1. Initial Setup:
   • The script defines constants for column names used in the DataFrame, which represent campaign and account information.
2. Brand Check:
   • It performs a check on the campaign names to see if they contain the word ‘Brand’. This check is case-sensitive and ignores any missing values.
3. DataFrame Creation:
   • Two separate DataFrames are created: one for campaigns identified as ‘Brand’ and another for those identified as ‘Non-Brand’.
   • The ‘Brand/Non-Brand’ column is updated in each DataFrame to reflect the respective category.
4. Merging DataFrames:
   • The script merges the ‘Brand’ and ‘Non-Brand’ DataFrames into a single DataFrame, ensuring all campaigns are included.
5. Output:
   • Finally, the script prints the merged DataFrame in a tabular format for easy viewing and analysis.

Vitals

• Script ID : 135
• Client ID / Customer ID: 1306924501 / 60269325
• Action Type: Bulk Upload
• Item Changed: Campaign
• Output Columns: Account, Campaign, Brand/Non-Brand
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: Byron Porter (bporter@marinsoftware.com)
• Created by Byron Porter on 2023-05-25 06:29
• Last Updated by lneels@marinsoftware.com on 2023-12-06 04:01

> See it in Action

Python Code

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#
# Bran/Non-Brand Dimension Tagging
#
# Byron Porter
# 2023-05-25
#


RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_ACCOUNT = 'Account'
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_B_NB = 'Brand/Non-Brand'

# Check if RPT_COL_CAMPAIGN column has 'Brand' in the campaign name
bcheck = (inputDf[RPT_COL_CAMPAIGN].str.contains('Brand', case=True, na=False))

# Use check to create two DataFrames. One for Brand and one for Non-Brand tagging
brandDf = inputDf.loc[bcheck, [BULK_COL_ACCOUNT, BULK_COL_CAMPAIGN, BULK_COL_B_NB]]
brandDf.loc[:, BULK_COL_B_NB] = 'Brand'

nonbrandDf = inputDf.loc[~bcheck, [BULK_COL_ACCOUNT, BULK_COL_CAMPAIGN, BULK_COL_B_NB]]
nonbrandDf.loc[:, BULK_COL_B_NB] = 'Non-Brand' 

# Merges the Brand and Non-Brand dataframes
outputDf = brandDf.merge(nonbrandDf, how='outer')

# Print the tableized version of the output DataFrame
print(tableize(outputDf))

Post generated on 2025-03-11 01:25:51 GMT