Script 759: Script Campaign Bulk Sheet

Purpose:

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

To Elaborate

The script is designed to manage and adjust the daily budgets of advertising campaigns based on their pacing cycles and specific conditions. It filters out campaigns that have ended and focuses on those with active traffic. The script then applies a daily budget adjustment factor to campaigns that are underpacing and have five or fewer days remaining in their pacing cycle. Additionally, it flags campaigns with recommended daily budgets exceeding $200. The final output is a DataFrame that includes adjusted daily budgets and alerts for campaigns that meet certain criteria, ensuring efficient budget allocation and management.

Walking Through the Code

  1. Data Preparation:
    • The script begins by loading the primary data source into a DataFrame named inputDf.
    • It defines several column names used throughout the script for clarity and consistency.
  2. Filtering Campaigns:
    • Campaigns with a status of ‘Campaign Ended’ are excluded from further processing.
    • From the remaining campaigns, only those with ‘Traffic’ in the ‘SBA Traffic’ column are retained.
  3. Daily Budget Adjustment:
    • The script checks if the ‘Daily Budget Adjustment Factor’ column exists; if not, it initializes it with NaN values.
    • For campaigns underpacing with five or fewer days remaining, a 5% adjustment factor is applied to their daily budget.
  4. Budget Alert Configuration:
    • The script flags campaigns with recommended daily budgets of $200 or more, marking them as ‘Flagged’ or ‘Checked’ based on existing conditions.
  5. Output Preparation:
    • The filtered and processed DataFrame is renamed to match the desired output column names.
    • The final DataFrame, outputDf, is printed, showcasing the adjusted budgets and alerts for the campaigns.

Vitals

  • Script ID : 759
  • Client ID / Customer ID: 1306927181 / 60270139
  • Action Type: Bulk Upload
  • Item Changed: Campaign
  • Output Columns: Account, Campaign, Daily Budget, Daily Budget Adjustment Factor, Daily Budget Alert
  • Linked Datasource: M1 Report
  • Reference Datasource: None
  • Owner: ascott@marinsoftware.com (ascott@marinsoftware.com)
  • Created by ascott@marinsoftware.com on 2024-03-06 21:39
  • Last Updated by ascott@marinsoftware.com on 2024-09-11 17:37
> 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].apply(lambda x: round(x, 2))
)

# 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

 # Convert the 'RPT_COL_DAILY_BUDGET' column to numeric
filteredDf[RPT_COL_DAILY_BUDGET] = pd.to_numeric(filteredDf[RPT_COL_DAILY_BUDGET], errors='coerce')

# 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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