Purpose:

Automates the process of updating a remarketing dimension in a dataset based on specific campaign name values.

To Elaborate

The Python script is designed to automate the task of populating a remarketing dimension within a dataset by analyzing campaign name values. It processes a dataset to identify campaigns that should be marked for remarketing based on predefined rules. These rules involve checking if certain keywords or specific campaign names are present in the campaign name field. If a campaign name matches any of the specified criteria, it is labeled as “Remarketing”; otherwise, it is marked as “No”. This automation helps streamline the process of categorizing campaigns for remarketing efforts, ensuring consistency and efficiency in handling large datasets.

Walking Through the Code

1. Data Preparation:
   • The script begins by defining the primary data source and relevant columns, such as ‘Campaign’, ‘Account’, and ‘Remarketing’.
   • It filters the input dataset to exclude rows where the ‘Remarketing’ column is already populated, focusing only on rows that need processing.
2. Remarketing Determination:
   • A function determine_remarketing is defined to evaluate each campaign name. It converts the campaign name to lowercase to ensure case-insensitive matching.
   • The function checks for specific keywords like “rlsa”, “remarketing”, and “retargeting”, as well as exact matches for certain campaign names. If any condition is met, it returns “Remarketing”; otherwise, it returns “No”.
3. Applying the Function:
   • The script applies the determine_remarketing function to each row in the filtered dataset, updating the ‘Remarketing’ column based on the function’s output.
   • The processed data is stored in outputDf, which contains only the rows that were filtered and updated.
4. Output:
   • Finally, the script prints the first few rows of the updated dataset to verify the changes.

Vitals

• Script ID : 1357
• Client ID / Customer ID: 1306913420 / 60268008
• Action Type: Bulk Upload
• Item Changed: Campaign
• Output Columns: Account, Campaign, Remarketing
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: Kyle Perkins (kyle.perkins@genesys.com)
• Created by Kyle Perkins on 2024-08-28 21:16
• Last Updated by Kyle Perkins on 2024-08-28 21:17

> See it in Action

Python Code

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##
## name: Dimension Update - Remarketing
## description:
##  Automates populating remarketing dimension based on campaign name values.
## 
## author: 
## created: 2024-08-28
## 

today = datetime.datetime.now(CLIENT_TIMEZONE).date()

# Primary data source and columns
inputDf = dataSourceDict["1"]
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_REMARKETING = 'Remarketing'  # Reference to the remarketing column

# Output columns and initial values
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_REMARKETING = 'Remarketing'  # Output remarketing column

# Define the function to determine remarketing based on the provided rules
def determine_remarketing(campaign_name):
    campaign_name = campaign_name.lower()  # Convert to lowercase to ignore case
    
    if "rlsa" in campaign_name:
        return "Remarketing"
    elif "remarketing" in campaign_name:
        return "Remarketing"
    elif "retargeting" in campaign_name:
        return "Remarketing"
    elif campaign_name == "apac_en_abmlite_insurance_001".lower():
        return "Remarketing"
    elif campaign_name == "apac-in_lg-en_mofu_indiacloudlaunch_cl-lp".lower():
        return "Remarketing"
    elif campaign_name == "na_na_video_fy24q4_test_003".lower():
        return "Remarketing"
    elif campaign_name == "na_na_videoaction_001".lower():
        return "Remarketing"
    elif campaign_name == "na_na_youtubeavaya_001".lower():
        return "Remarketing"
    else:
        return "No"

# Filter inputDf to skip rows where Remarketing is already populated
filteredDf = inputDf[inputDf[RPT_COL_REMARKETING].isna()].copy()

# Initialize outputDf so that it only includes rows that have been filtered
outputDf = filteredDf

# Apply the function to each row in the filtered dataframe and set result as the output dataframe value
outputDf[BULK_COL_REMARKETING] = filteredDf[RPT_COL_CAMPAIGN].apply(determine_remarketing)

# Printing the result
print(tableize(outputDf.head()))

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