Script 911: Bidding Strategy Assignment

Purpose

Automatic assignment of a campaign to an Impression Share Strategy called ‘Data Gathering’ for the first 7 days after creation, and then moving it to a CPA strategy called ‘Performance Bidding’ after 7 days.

To Elaborate

The Python script solves the problem of automatically assigning campaigns to different bidding strategies based on their creation date. The script checks the creation date of each campaign and assigns it to the appropriate strategy. If a campaign is created within 7 days of today’s date, it is assigned to the “Data Gathering” strategy. Otherwise, it is assigned to the “Performance Bidding” strategy.

Walking Through the Code

  1. The script starts by getting today’s date in the specified timezone.
  2. It then makes a copy of the input DataFrame.
  3. The ‘Campaign Creation Date’ column in the DataFrame is converted to datetime objects.
  4. The script iterates through each row in the DataFrame.
  5. For each row, it checks the campaign creation date and compares it with today’s date.
  6. If the campaign is within 7 days of today’s date, it assigns the “Data Gathering” strategy and leaves the “Folder Check” column blank.
  7. If the campaign is more than 7 days old, it assigns the “Performance Bidding” strategy and sets the “Folder Check” column to “YES”.
  8. The changes made to the DataFrame are printed for debugging purposes.
  9. The modified DataFrame is returned as the output.

Vitals

  • Script ID : 911
  • Client ID / Customer ID: 1306912139 / 69058
  • Action Type: Bulk Upload
  • Item Changed: Campaign
  • Output Columns: Account, Campaign, Strategy, Folder Check
  • Linked Datasource: M1 Report
  • Reference Datasource: None
  • Owner: Jeremy Brown (jbrown@marinsoftware.com)
  • Created by Jeremy Brown on 2024-04-11 08:45
  • Last Updated by Jeremy Brown on 2024-04-11 08:54
> See it in Action

Python Code

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##
## name: Bidding Strategy Assignment
## description:
##
## The Script automatically assigns a campaign to an Impression Share Strategy called 'Data Gathering' for the first 7 days after being created. Then after the campaign has been live for 7 days it is moved to a CPA strategy called 'Performance Bidding'. The logic is as follows;
## When a campaign's creation date is within 7 days of today's date, it is assigned to "Data Gathering" with a blank Folder Check column (""). Otherwise, it's assigned to "Performance Bidding" with a "YES" in the Folder Check column.
## 
## author: Jeremy Brown
## created: 2024-04-11
## 

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_PUBLISHER = 'Publisher'
RPT_COL_STRATEGY = 'Strategy'
RPT_COL_CAMPAIGN_CREATION_DATE = 'Campaign Creation Date'
RPT_COL_FOLDER_CHECK = 'Folder Check'
RPT_COL_IMPR = 'Impr.'

def process(inputDf):
    # Make a copy of the input DataFrame
    outputDf = inputDf.copy()
    
    # Get today's date in the specified timezone
    today_date = datetime.datetime.now(CLIENT_TIMEZONE).date()
    
    # Convert 'Campaign Creation Date' column to datetime objects (assuming format dd/mm/yyyy)
    outputDf['Campaign Creation Date'] = pd.to_datetime(outputDf['Campaign Creation Date'], format='%d/%m/%Y')
    
    # Determine the strategy and folder check based on the campaign creation date
    for index, row in outputDf.iterrows():
        creation_date = row['Campaign Creation Date'].date()
        if (today_date - creation_date).days <= 7:
            # Within 7 days of today's date
            outputDf.at[index, 'Strategy'] = "Data Gathering"
            outputDf.at[index, 'Folder Check'] = ""  # Leave Folder Check blank
        else:
            # More than 7 days old
            outputDf.at[index, 'Strategy'] = "Performance Bidding"
            outputDf.at[index, 'Folder Check'] = "YES"
    
    # Debugging: Print changes made to outputDf
    print("Data Changed:")
    print(outputDf[['Account', 'Campaign', 'Strategy', 'Folder Check']])
    
    return outputDf

# Example usage:
# Assuming inputDf is your actual DataFrame with the appropriate columns
# inputDf = ...

# Process the DataFrame
outputDf = process(inputDf)

Post generated on 2024-05-15 07:44:05 GMT

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