Purpose:

The Python script identifies campaigns where the previous day’s spending is at least 90% of the daily budget and flags them as “Budget Capped” if certain conditions are met.

To Elaborate

The script is designed to monitor advertising campaigns by analyzing their spending relative to their allocated daily budgets. Specifically, it identifies campaigns where the publisher’s cost for the previous day reaches or exceeds 90% of the daily budget. If this condition is met, and additional criteria such as the ‘iDNE’ value being less than 1.5 and the ‘Lost Impression Share’ being greater than 10% are satisfied, the campaign is flagged with a “Budget Capped” alert. This helps in identifying campaigns that are at risk of exceeding their budget, allowing for timely adjustments to prevent overspending and optimize budget allocation.

Walking Through the Code

1. Data Preparation
   • The script begins by converting the ‘iDNE’ column in the input DataFrame to numeric values, handling any errors by coercing them into NaN. This ensures that the data can be used in numerical calculations.
2. Calculation of Ratios
   • It calculates the ratio of the publisher’s cost to the daily budget for each campaign and stores it in a new column ‘Pub Cost / Budget’. This ratio helps in determining how close the spending is to the budget limit.
3. Budget Cap Alert Logic
   • A threshold is set at 0.90, which can be adjusted by the user. The script checks if the ‘Pub Cost / Budget’ ratio is below this threshold and clears any existing alerts.
   • If the ratio is equal to or exceeds the threshold, and the ‘iDNE’ value is less than 1.5, and the ‘Lost Impression Share’ is greater than 10%, the campaign is flagged with a “Budget Capped” alert.
4. Output Preparation
   • The script selects relevant columns, including ‘Account’, ‘Campaign’, and ‘Budget Alert’, to create a new DataFrame for output, which is then printed. This output provides a concise view of campaigns that require attention due to budget constraints.

Vitals

• Script ID : 859
• Client ID / Customer ID: 197178269 / 13095968
• Action Type: Bulk Upload
• Item Changed: Campaign
• Output Columns: Account, Campaign, Budget Alert
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: smalina@marinsoftware.com (smalina@marinsoftware.com)
• Created by Stephen Malina on 2024-03-27 21:36
• Last Updated by Stephen Malina on 2024-04-23 20:46

> See it in Action

Python Code

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#
# Tag campaign if Pub Cost for prior day is 90% of Daily Budget
#
#
# Author: Stephen Malina
# Date: 2024-03-27

RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_PUBLISHER = 'Publisher'
RPT_COL_PUB_COST = 'Pub. Cost $'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_DAILY_BUDGET = 'Daily Budget'
RPT_COL_CAMPAIGN_STATUS = 'Campaign Status'
RPT_COL_BUDGET_ALERT = 'Budget Alert'
RPT_COL_iDNE = 'iDNE'
RPT_COL_LOST_IMP_SHARE = 'Lost Impr. Share (Budget) %'
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_BUDGET_ALERT = 'Budget Alert'

# Convert the 'iDNE' column to numeric values
inputDf[RPT_COL_iDNE] = pd.to_numeric(inputDf[RPT_COL_iDNE], errors='coerce')

# Calculate the difference between daily budget and pub cost, include iDNE for consideration
inputDf['Pub Cost / Budget'] = inputDf[RPT_COL_PUB_COST] / inputDf[RPT_COL_DAILY_BUDGET]
inputDf['iDNE'] = inputDf[RPT_COL_iDNE]
inputDf['Lost Imp Share'] = inputDf[RPT_COL_LOST_IMP_SHARE]

# Output a "Budget Cap Alert" where daily spend is greater than 90% of daily budget and iDNE is less than 1.5
threshold = 0.90  # Adjust this threshold as needed
inputDf.loc[inputDf['Pub Cost / Budget'] < threshold, BULK_COL_BUDGET_ALERT] = ""
inputDf.loc[(inputDf['Pub Cost / Budget'] >= threshold) & (inputDf['iDNE'] < 1.5) & (inputDf['Lost Imp Share'] > 10), BULK_COL_BUDGET_ALERT] = "Budget Capped"

# Print the modified inputDf
print(inputDf)
cols = [RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN, BULK_COL_BUDGET_ALERT]
outputDf = inputDf[cols]

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