Purpose:

The Python script checks and filters campaigns based on their budget spending and status, ensuring they meet specific criteria for active traffic allocation.

To Elaborate

The Python script is designed to analyze and filter advertising campaign data to ensure that campaigns are spending their allocated budget effectively. It calculates the percentage difference between the actual public cost and the daily budget for each campaign. The script then filters out campaigns that are not actively trafficking, are marked as read-only, have no spending, or are not active. Additionally, it focuses on campaigns where the spending difference is significant, specifically those with a difference percentage of 50% or more. This helps in identifying campaigns that are either overspending or underspending relative to their budget, allowing for better budget management and allocation.

Walking Through the Code

1. Data Preparation:
   • The script begins by retrieving data from a primary data source, which includes various columns related to campaign details such as campaign name, account, public cost, daily budget, and status.
2. Initial Calculation:
   • It calculates the ‘Difference_Percentage’ for each campaign, which represents how much the actual spending deviates from the daily budget.
3. Filtering Process:
   • The script filters out campaigns that are not actively trafficking by checking the ‘SBA Traffic’ column.
   • It excludes campaigns marked as read-only by filtering the ‘SBA Read Only’ column.
   • Campaigns with zero spending are removed by checking the ‘Pub. Cost $’ column.
   • Only campaigns with an ‘Active’ status are retained.
   • Finally, it filters campaigns where the absolute value of the ‘Difference_Percentage’ is less than 50%, focusing on those with significant budget discrepancies.
4. Output:
   • The filtered data is stored in outputDf, which contains campaigns that meet all the specified criteria for further analysis or reporting.

Vitals

• Script ID : 577
• Client ID / Customer ID: 1306923673 / 60269245
• Action Type: Email Report
• Item Changed: None
• Output Columns:
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: Jonathan Reichl (jreichl@marinsoftware.com)
• Created by Jonathan Reichl on 2023-12-08 16:30
• Last Updated by Jonathan Reichl on 2023-12-14 13:49

> See it in Action

Python Code

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##
## name: SBA_Check_budget_spent
## description:
##  
## 
## author: 
## created: 2023-12-08
## 

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

# primary data source and columns
inputDf = dataSourceDict["1"]
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_PUB_COST = 'Pub. Cost $'
RPT_COL_DAILY_BUDGET = 'Daily Budget'
RPT_COL_SBA_BUCKET_NAME = 'SBA Bucket Name'
RPT_COL_SBA_TRAFFIC = 'SBA Traffic'
RPT_COL_SBA_READ_ONLY = 'SBA Read Only'
RPT_COL_CAMPAIGN_STATUS = 'Campaign Status'

# output columns and initial values
 
  
 
# user code start here
print(tableize(inputDf))

inputDf['Difference_Percentage'] = ((inputDf[RPT_COL_PUB_COST] - inputDf[RPT_COL_DAILY_BUDGET]) / inputDf[RPT_COL_DAILY_BUDGET]) * 100

# filter rows which arnt trafficing
filtered_df = inputDf[inputDf[RPT_COL_SBA_TRAFFIC] != 0]
#filter rows that are readonly
filtered_df = filtered_df[filtered_df[RPT_COL_SBA_READ_ONLY] == 0]
#filter rows with no spend
filtered_df = filtered_df[filtered_df[RPT_COL_PUB_COST] != 0]
#Finter paused rows 
filtered_df = filtered_df[filtered_df[RPT_COL_CAMPAIGN_STATUS] == 'Active']
#Filter rows where the dif is <50%
filtered_df = filtered_df[abs(filtered_df['Difference_Percentage']) >= 50]

outputDf = filtered_df

print (tableize(outputDf))

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