Script 771: Pacing Campaign Bulk Sheet

Purpose:

The Python script processes campaign data to adjust daily budgets based on pacing cycles and traffic conditions.

To Elaborate

The Python script is designed to manage and adjust daily budgets for advertising campaigns based on specific pacing cycles and traffic conditions. It filters out campaigns that have ended and focuses on those with active traffic. The script calculates a recommended daily budget and applies an adjustment factor if certain conditions are met, such as when the campaign is underpacing and has five or fewer days remaining in its pacing cycle. Additionally, it flags campaigns with daily budgets exceeding $200 for further review. The script aims to optimize budget allocation by dynamically adjusting budgets according to campaign performance metrics and pacing thresholds.

Walking Through the Code

  1. Data Filtering:
    • The script begins by filtering out campaigns that have ended, ensuring only active campaigns are considered.
    • It further narrows down the selection to campaigns with ‘Traffic’ in the SBA Traffic column.
  2. Budget Adjustment:
    • For campaigns with a recommended daily budget below $200, the script sets the daily budget to the recommended value.
    • It introduces a ‘Daily Budget Adjustment Factor’ for campaigns underpacing with five or fewer days remaining, applying a 5% increase to the daily budget.
  3. Budget Alerts:
    • The script flags campaigns with daily budgets exceeding $200, marking them for review.
    • It ensures the daily budget column is numeric for accurate calculations.
  4. Output Preparation:
    • The final filtered data is renamed and prepared for output, focusing on key columns like account, campaign, daily budget, adjustment factor, and budget alert status.
    • The processed data is printed for further use or analysis.

Vitals

  • Script ID : 771
  • Client ID / Customer ID: 1306927187 / 60270139
  • Action Type: Bulk Upload
  • Item Changed: Campaign
  • Output Columns: Account, Campaign, Daily Budget, Daily Budget Alert, Daily Budget Adjustment Factor
  • Linked Datasource: M1 Report
  • Reference Datasource: None
  • Owner: ascott@marinsoftware.com (ascott@marinsoftware.com)
  • Created by ascott@marinsoftware.com on 2024-03-06 22:10
  • Last Updated by ascott@marinsoftware.com on 2024-09-11 14:28
> See it in Action

Python Code

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## name: Pacing - Campaign Bulk Sheet
## description:
##  
## 
## author: Jesus Garza
## created: 2024-07-03
## 7/1 Updated version with Daily Budget Adjustment Factor 

today = datetime.datetime.now().date()  # Removed CLIENT_TIMEZONE for simplicity

# primary data source and columns
inputDf = dataSourceDict["1"]
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_AUTO_PACING_CYCLE_START_DATE = 'Auto. Pacing Cycle Start Date'
RPT_COL_AUTO_PACING_CYCLE_END_DATE = 'Auto. Pacing Cycle End Date'
RPT_COL_AUTO_PACING_CYCLE_DAYS_ELAPSED = 'Auto. Pacing Cycle Days Elapsed'
RPT_COL_AUTO_PACING_CYCLE_DAYS_REMAINING = 'Auto. Pacing Cycle Days Remaining'
RPT_COL_AUTO_PACING_CYCLE_PACING = 'Auto. Pacing Cycle Pacing'
RPT_COL_AUTO_PACING_CYCLE_THRESHOLD = 'Auto. Pacing Cycle Threshold'
RPT_COL_TOTAL_TARGET_SPEND_PER_IMPRVIEWS = 'Total Target (Spend/Impr./Views)'
RPT_COL_TOTAL_DAYS = 'Total Days'
RPT_COL_TOTAL_DAYS_ELAPSED = 'Total Days Elapsed'
RPT_COL_TOTAL_PACING = 'Total Pacing'
RPT_COL_DELIVERY_STATUS = 'Delivery Status'
RPT_COL_RECOMMENDED_DAILY_BUDGET = 'Recommended Daily Budget'
RPT_COL_DAILY_BUDGET = 'Daily Budget'
RPT_COL_PACING_CALCULATION_DATE = 'Pacing Calculation Date'
RPT_COL_SOCIAL_BUDGET = 'Social Budget'
RPT_COL_SOCIAL_BUDGET_UPDATE_STATUS = 'Social Budget Update Status'
RPT_COL_AUTO_PACING_CYCLE_PUB_COST = 'Auto. Pacing Cycle Pub. Cost'
RPT_COL_AUTO_PACING_CYCLE_IMPR = 'Auto. Pacing Cycle Impr.'
RPT_COL_AUTO_PACING_CYCLE_CLICKS = 'Auto. Pacing Cycle Clicks'
RPT_COL_AUTO_PACING_CYCLE_VIEWS = 'Auto. Pacing Cycle Views'
RPT_COL_SBA_TRAFFIC = 'SBA Traffic'
RPT_COL_DAILY_BUDGET_ALERT = 'Daily Budget Alert'

# output columns
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_DAILY_BUDGET = 'Daily Budget'
BULK_COL_DAILY_BUDGET_ADJUSTMENT_FACTOR = 'Daily Budget Adjustment Factor'
BULK_COL_DAILY_BUDGET_ALERT = 'Daily Budget Alert'

# First filter: Exclude campaigns with 'Campaign Ended'
campaigns_not_ended = inputDf[inputDf[RPT_COL_AUTO_PACING_CYCLE_THRESHOLD] != 'Campaign Ended'].copy()

# Second filter: From the remaining, only include those where SBA Traffic is 'Traffic'
filteredDf = campaigns_not_ended[campaigns_not_ended[RPT_COL_SBA_TRAFFIC] == 'Traffic'].copy()

# Apply necessary operations on filteredDf
filteredDf[RPT_COL_DAILY_BUDGET] = np.where(filteredDf[RPT_COL_RECOMMENDED_DAILY_BUDGET] >= 200, "", filteredDf[RPT_COL_RECOMMENDED_DAILY_BUDGET])

# Add the new logic for Daily Budget Adjustment Factor
# Ensure the column exists before setting values
if BULK_COL_DAILY_BUDGET_ADJUSTMENT_FACTOR not in filteredDf.columns:
    filteredDf[BULK_COL_DAILY_BUDGET_ADJUSTMENT_FACTOR] = np.nan

# Now you can safely set the value
filteredDf.loc[(filteredDf[RPT_COL_AUTO_PACING_CYCLE_DAYS_REMAINING] <= 5) & 
               (filteredDf[RPT_COL_AUTO_PACING_CYCLE_THRESHOLD] == 'Underpacing'), 
               BULK_COL_DAILY_BUDGET_ADJUSTMENT_FACTOR] = 0.05

# Apply the Daily Budget Adjustment Factor to the Daily Budget column
filteredDf.loc[filteredDf[BULK_COL_DAILY_BUDGET_ADJUSTMENT_FACTOR].notnull(), 
               RPT_COL_DAILY_BUDGET] = filteredDf.loc[filteredDf[BULK_COL_DAILY_BUDGET_ADJUSTMENT_FACTOR].notnull(), 
                                                       RPT_COL_DAILY_BUDGET] * (1 + filteredDf[BULK_COL_DAILY_BUDGET_ADJUSTMENT_FACTOR])
                                                       
# Convert the 'RPT_COL_DAILY_BUDGET' column to numeric
filteredDf[RPT_COL_DAILY_BUDGET] = pd.to_numeric(filteredDf[RPT_COL_DAILY_BUDGET], errors='coerce')

# Configured 'Daily Budget Alert' and set to 'Flagged' for daily budgets exceeding $200
filteredDf[BULK_COL_DAILY_BUDGET_ALERT] = filteredDf.apply(
    lambda row: 'Checked' if row[RPT_COL_RECOMMENDED_DAILY_BUDGET] >= 200 and row[RPT_COL_DAILY_BUDGET_ALERT] == 'Checked' else
               'Flagged' if row[RPT_COL_RECOMMENDED_DAILY_BUDGET] >= 200 else '', axis=1)

# Assign the final filtered DataFrame to outputDf with correct column names
outputDf = filteredDf.rename(columns={
    RPT_COL_ACCOUNT: BULK_COL_ACCOUNT,
    RPT_COL_CAMPAIGN: BULK_COL_CAMPAIGN,
    RPT_COL_DAILY_BUDGET: BULK_COL_DAILY_BUDGET,
    BULK_COL_DAILY_BUDGET_ADJUSTMENT_FACTOR: BULK_COL_DAILY_BUDGET_ADJUSTMENT_FACTOR,
    BULK_COL_DAILY_BUDGET_ALERT: BULK_COL_DAILY_BUDGET_ALERT
})

# Assuming you want to display or utilize outputDf
print(outputDf)

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

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