Purpose:

The Python script assigns geographic values to the ‘Geo’ dimension at the campaign level based on specific keywords found in campaign names.

To Elaborate

The script is designed to automate the process of assigning geographic locations to marketing campaigns based on the campaign names. This is particularly useful for organizations that run multiple campaigns across different regions and need to categorize them accordingly for reporting or analysis purposes. The script examines each campaign name for specific keywords or patterns that correspond to different geographic locations, such as states or regions. When a match is found, the script assigns the appropriate geographic value to the ‘Geo’ column. This helps in maintaining a structured and organized dataset, which is crucial for effective budget allocation and performance tracking across different regions.

Walking Through the Code

1. Initialization:
   • The script begins by defining constants for various column names used in the data processing. These constants are used to reference specific columns in the input and output data frames.
2. Function Definition:
   • A function named update_geo is defined to determine the geographic location based on the campaign name. It converts the campaign name to lowercase and checks for specific keywords or patterns that indicate a particular geographic region.
3. Keyword Matching:
   • The function uses a series of conditional statements to match keywords in the campaign name with geographic locations. For example, if the campaign name contains ‘indiana’ or ‘in_’, it assigns ‘Indiana’ to the ‘Geo’ column.
4. Data Processing:
   • The script retrieves the primary data source from a dictionary named dataSourceDict and assigns it to inputDf.
5. Applying the Function:
   • The update_geo function is applied to each row of the output data frame outputDf to update the ‘Geo’ column based on the campaign name.
6. User Changeable Parameters:
   • Users can modify the keywords and corresponding geographic values in the update_geo function to suit their specific campaign naming conventions and geographic regions.

Vitals

• Script ID : 605
• Client ID / Customer ID: 1306926715 / 60270103
• Action Type: Bulk Upload
• Item Changed: Campaign
• Output Columns: Account, Campaign, Geo
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: Chris Jetton (cjetton@marinsoftware.com)
• Created by Chris Jetton on 2023-12-18 21:47
• Last Updated by Chris Jetton on 2024-01-18 20:18

> See it in Action

Python Code

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##
## name: Campaign Geo Assignment
## description: assigns values to Geo dimension at campaign level when appropriate
##  
## 
## author: Chris Jetton
## created: 2023-12-18
## 

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

# primary data source and columns
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_DAILY_BUDGET = 'Daily Budget'
RPT_COL_GEO = 'Geo'
RPT_COL_META_ABO_BUDGET = 'Meta ABO Budget'
RPT_COL_BUDGET_LEVEL = 'Budget Level'
RPT_COL_PUBLISHER_NAME = 'Publisher Name'
RPT_COL_PUB_ID = 'Pub. ID'
RPT_COL_PUBLISHER = 'Publisher'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_IMPR = 'Impr.'
RPT_COL_CLICKS = 'Clicks'
RPT_COL_PUB_COST = 'Pub. Cost $'
RPT_COL_CTR = 'CTR %'
RPT_COL_ENROLLMENTUPLOAD_CONV = 'enrollment-upload Conv.'
RPT_COL_LEADUPLOAD_CONV = 'Lead-upload Conv.'
RPT_COL_LEADTRACKER_CONV = 'Lead-tracker Conv.'
RPT_COL_OF_LEAD_CONV = '# of Lead Conv.'
RPT_COL_INSTANTFORM_LEADS_CONV = 'InstantForm Leads Conv.'
RPT_COL_WEBSITE_LEADS_CONV = 'Website Leads Conv.'

# output columns and initial values
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_GEO = 'Geo'
outputDf[BULK_COL_GEO] = "<<YOUR VALUE>>"

# Function to update Geo column based on campaign name
def update_geo(row):
    campaign = row['Campaign'].lower()

    if 'indiana' in campaign or 'in_' in campaign or 'in22' in campaign:
        return 'Indiana'
    elif 'kansas' in campaign or 'ks20' in campaign:
        return 'Kansas'
    elif 'sc22' in campaign:
        return 'South Carolina'
    elif 'oh22' in campaign or 'ohio' in campaign or 'engage oh' in campaign or 'engage_oh' in campaign:
        return 'Ohio'
    elif 'colorado' in campaign:
        return 'Colorado'
    elif 'az21' in campaign or 'engage az' in campaign or 'ar21' in campaign:
        return 'Arizona'
    elif 'wi_' in campaign:
        return 'Wisconsin'
    elif 'nh_' in campaign:
        return 'New Hampshire'
    elif 'mi adult' in campaign or 'mi_' in campaign:
        return 'Michigan'
    elif 'mo21' in campaign:
        return 'Missouri'
    else:
        return ''

# primary data source and columns
inputDf = dataSourceDict["1"]

# Apply the function to update Geo column
outputDf['Geo'] = outputDf.apply(update_geo, axis=1)

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