Script 1487: Pacing Campaign Bulk Sheet

Purpose:

The Python script adjusts daily budgets for advertising campaigns based on pacing cycles and specific criteria to optimize spending.

To Elaborate

The script is designed to manage and optimize the daily budgets of advertising campaigns by applying specific business rules and criteria. It filters out campaigns that have ended and focuses on those with active traffic. The script adjusts the daily budget based on recommendations and applies a daily budget adjustment factor for campaigns that are underpacing and have less than five days remaining in their pacing cycle. Additionally, it ensures a minimum daily budget for campaigns with a CPM goal and flags budgets exceeding a certain threshold. This process helps in maintaining efficient budget allocation and pacing for ongoing campaigns, ensuring they meet their goals without overspending.

Walking Through the Code

  1. Data Filtering:
    • The script begins by filtering out campaigns that have ended and focuses on those with active traffic.
    • It uses the inputDf DataFrame to exclude campaigns marked as ‘Campaign Ended’ and further filters for those with ‘Traffic’ in the SBA Traffic column.
  2. Budget Adjustment:
    • The script sets the daily budget to an empty string if the recommended daily budget is greater than or equal to $200.
    • It introduces a ‘Daily Budget Adjustment Factor’ for campaigns underpacing with less than five days remaining, setting this factor to 0.05.
  3. Budget Calculation:
    • The daily budget is converted to a numeric format to ensure calculations can be performed.
    • The script applies the adjustment factor to the daily budget where applicable, increasing the budget by 5% for qualifying campaigns.
  4. Minimum Budget Enforcement:
    • A function is defined to ensure a minimum daily budget of $0.75 for campaigns with a CPM goal, adjusting budgets accordingly.
  5. Budget Alerts:
    • The script flags campaigns with recommended daily budgets exceeding $200, marking them as ‘Flagged’ or ‘Checked’ based on existing alerts.
  6. Output Preparation:
    • Finally, the script renames columns for output consistency and displays the adjusted DataFrame, ready for further analysis or export.

Vitals

  • Script ID : 1487
  • Client ID / Customer ID: 1306927171 / 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: Jesus Garza (jgarza@marinsoftware.com)
  • Created by Jesus Garza on 2024-11-06 18:17
  • Last Updated by ascott@marinsoftware.com on 2024-11-22 19:05
> 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'
RPT_COL_GOAL = 'Goal'

# 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
if BULK_COL_DAILY_BUDGET_ADJUSTMENT_FACTOR not in filteredDf.columns:
    filteredDf[BULK_COL_DAILY_BUDGET_ADJUSTMENT_FACTOR] = np.nan

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])

# Function to ensure minimum daily budget for CPM goal
def apply_minimum_budget_for_cpm(df, goal_col, budget_col, minimum_budget=0.75):
    df.loc[df[goal_col].str.lower() == 'cpm', budget_col] = df[budget_col].apply(lambda x: max(x, minimum_budget) if pd.notnull(x) else minimum_budget)

# Apply the minimum budget function
apply_minimum_budget_for_cpm(filteredDf, RPT_COL_GOAL, RPT_COL_DAILY_BUDGET)

# 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
})

# Display the final output
print(outputDf)

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

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