Script 915: Bidding Strategy Assignment

Purpose

Python script that automatically assigns a campaign to an Impression Share Strategy called ‘Data Gathering’ for the first 7 days after being created, and then moves it to a CPA strategy called ‘Performance Bidding’ after 7 days.

To Elaborate

The problem being solved is the automatic assignment of campaigns to different bidding strategies based on their creation date. The script assigns campaigns to the ‘Data Gathering’ strategy if they were created within the last 7 days, and to the ‘Performance Bidding’ strategy if they are older than 7 days. The script also updates the ‘Folder Check’ column accordingly.

Walking Through the Code

  1. The script starts by getting today’s date in the specified timezone.
  2. It 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 over each row in the DataFrame.
  5. For each row, it checks if the campaign creation date is within 7 days of today’s date.
  6. If the campaign is within 7 days, it assigns the ‘Data Gathering’ strategy and leaves the ‘Folder Check’ column blank.
  7. If the campaign is older than 7 days, it assigns the ‘Performance Bidding’ strategy and sets the ‘Folder Check’ column to “YES”.
  8. The script prints the changes made to the DataFrame (for debugging purposes).
  9. The updated DataFrame is returned as the output.

Vitals

  • Script ID : 915
  • Client ID / Customer ID: 1306926015 / 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 09:01
  • Last Updated by Jeremy Brown on 2024-04-11 09:01
> 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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