Script 1527: Always Push Budgets

Purpose:

The Python script dynamically allocates and updates daily budgets for advertising campaigns based on recommended values.

To Elaborate

The script addresses the need for dynamically adjusting the daily budgets of advertising campaigns. It pulls data from a primary data source, processes it, and updates the daily budget for each campaign based on recommended values. This ensures that the budget allocation is optimized according to the latest recommendations, potentially improving the efficiency and effectiveness of advertising spend. The script is designed to handle structured budget allocation (SBA) by using predefined columns and data structures to manage and update campaign budgets systematically.

Walking Through the Code

  1. Data Source Initialization
    • The script begins by defining the primary data source, inputDf, which contains campaign-related data. This data includes various metrics and recommendations necessary for budget allocation.
  2. Column Definitions
    • Several column names are defined to facilitate data manipulation. These columns represent different attributes of the campaigns, such as campaign type, status, publisher, and various performance metrics.
  3. Output DataFrame Creation
    • An output DataFrame, outputDf, is created to store the updated budget information. It initially copies the ‘Account’ and ‘Campaign’ columns from the input data.
  4. Budget Update
    • The script updates the ‘Daily Budget’ column in the output DataFrame with the recommended daily budget values from the input data. This step is crucial for ensuring that the budgets are set according to the latest recommendations.
  5. Output Display
    • Finally, the script prints the first few rows of the output DataFrame to verify the updated budget allocations. This provides a quick check to ensure that the data has been processed correctly.

Vitals

  • Script ID : 1527
  • Client ID / Customer ID: 1306926629 / 60270083
  • Action Type: Bulk Upload
  • Item Changed: Campaign
  • Output Columns: Account, Campaign, Daily Budget
  • Linked Datasource: M1 Report
  • Reference Datasource: None
  • Owner: ascott@marinsoftware.com (ascott@marinsoftware.com)
  • Created by ascott@marinsoftware.com on 2024-11-14 19:52
  • Last Updated by ascott@marinsoftware.com on 2024-12-06 20:13
> See it in Action

Python Code

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##
## name: Dynamic Allocation Temp Traffic
## description:
##  
## 
## author: 
## created: 2024-11-13
## 

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

# primary data source and columns
inputDf = dataSourceDict["1"]
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_CAMPAIGN_TYPE = 'Campaign Type'
RPT_COL_CAMPAIGN_STATUS = 'Campaign Status'
RPT_COL_PUBLISHER = 'Publisher'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_STRATEGY = 'Strategy'
RPT_COL_STRATEGY_TARGET = 'Strategy Target'
RPT_COL_IMPR = 'Impr.'
RPT_COL_CLICKS = 'Clicks'
RPT_COL_PUB_COST = 'Pub. Cost $'
RPT_COL_CTR = 'CTR %'
RPT_COL_CONV = 'Conv.'
RPT_COL_COST_PER_CONV = 'Cost/Conv. $'
RPT_COL_IMPR_SHARE = 'Impr. share %'
RPT_COL_LOST_IMPR_SHARE_BUDGET = 'Lost Impr. Share (Budget) %'
RPT_COL_LOST_IMPR_SHARE_RANK = 'Lost Impr. Share (Rank) %'
RPT_COL_SHARED_BUDGET = 'Shared Budget'
RPT_COL_PUBLISHER_BID_PORTFOLIO_NAME = 'Publisher Bid Portfolio Name'
RPT_COL_PUBLISHER_BID_STRATEGY = 'Publisher Bid Strategy'
RPT_COL_PUBLISHER_TARGET_CPA = 'Publisher Target CPA'
RPT_COL_PUBLISHER_TARGET_ROAS = 'Publisher Target ROAS'
RPT_COL_UNCONSTRAINED_RECOMMENDED_CPA_PER_ROAS_TARGET = 'Unconstrained Recommended CPA/ROAS Target'
RPT_COL_RECOMMENDED_CPA_PER_ROAS_TARGET = 'Recommended CPA/ROAS Target'
RPT_COL_DAILY_BUDGET = 'Daily Budget'
RPT_COL_UNCONSTRAINED_RECOMMENDED_DAILY_BUDGET = 'Unconstrained Recommended Daily Budget'
RPT_COL_RECOMMENDED_DAILY_BUDGET = 'Recommended Daily Budget'
RPT_COL_RECOMMENDATION_CONSTRAINTS = 'Recommendation Constraints'
RPT_COL_CAMPAIGN_ID = 'Campaign ID'
RPT_COL_DAILY_BUDGET_RECOMMENDATION_DATE = 'Daily Budget Recommendation Date'
RPT_COL_CPA_PER_ROAS_TARGET_RECOMMENDATION_DATE = 'CPA/ROAS Target Recommendation Date'
RPT_COL_PUBLISHER_BID_STRATEGY_SYSTEM_STATUS = 'Publisher Bid Strategy System Status'

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


# Assuming CLIENT_TIMEZONE is defined somewhere in your code
today = datetime.datetime.now(CLIENT_TIMEZONE).date()

# primary data source and columns
inputDf = dataSourceDict["1"]
RPT_COL_RECOMMENDED_DAILY_BUDGET = 'Recommended Daily Budget'

# output columns and initial values
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_DAILY_BUDGET = 'Daily Budget'

# Create the output DataFrame
outputDf = pd.DataFrame()
outputDf[BULK_COL_ACCOUNT] = inputDf['Account']
outputDf[BULK_COL_CAMPAIGN] = inputDf['Campaign']
outputDf[BULK_COL_DAILY_BUDGET] = inputDf[RPT_COL_RECOMMENDED_DAILY_BUDGET]

# Print the first few rows of the output DataFrame
print(outputDf.head())

print(tableize(inputDf.head()))

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

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