Purpose:

The Python script compares the CPA (Cost Per Acquisition) for campaigns between the current quarter and the last week, flagging those with a CPA for the last week that is 30% higher or lower than the quarterly CPA.

To Elaborate

The script is designed to analyze campaign performance by comparing the Cost Per Acquisition (CPA) metrics over two different time periods: the current quarter and the last week. It identifies campaigns where the CPA for the last week deviates significantly—specifically, by more than 30%—from the CPA of the current quarter. This deviation can be either an increase or a decrease, and such campaigns are flagged for further review. The purpose of this analysis is to quickly identify campaigns that may require attention due to significant changes in their cost efficiency, allowing for timely adjustments to budget allocations or campaign strategies.

Walking Through the Code

1. Data Preparation:
   • The script begins by assuming the presence of a DataFrame (inputDf) sourced from dataSourceDict, which contains the necessary campaign data.
   • It defines key columns for CPA metrics: Campaign CPA This Quarter and All - Complete Registration - CPA.
2. Threshold Calculation:
   • The script calculates two thresholds for each campaign: a high threshold (30% above the quarterly CPA) and a low threshold (30% below the quarterly CPA).
   • These thresholds are used to determine significant deviations in CPA.
3. Flagging Campaigns:
   • A new column, Flag, is created in the DataFrame to mark campaigns where the last week’s CPA exceeds the high threshold or falls below the low threshold.
   • Campaigns meeting these criteria are flagged with the label ‘Flag’.
4. Output:
   • The script filters the DataFrame to include only flagged campaigns and prints them in a table format for easy review.
   • The function tableize is used to convert the DataFrame into a string representation suitable for display.

Vitals

• Script ID : 1137
• Client ID / Customer ID: 1306927809 / 60270355
• Action Type: Email Report
• Item Changed: None
• Output Columns:
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: Grégory Pantaine (gpantaine@marinsoftware.com)
• Created by Grégory Pantaine on 2024-05-23 13:44
• Last Updated by Grégory Pantaine on 2024-05-23 13:52

> See it in Action

Python Code

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## name: Script - Campaign - CPA LW vs TQuarter
## description:
## Compare CPA for the current quarter with the CPA for the last week for campaigns and flag those with a CPA for LW 30% higher or lower than the quarterly CPA.
## Author: Gregory Pantaine
## created: 2024-05-23
##

# Simulated CLIENT_TIMEZONE for example
CLIENT_TIMEZONE = datetime.timezone.utc

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

# Assuming `dataSourceDict` is provided and contains a DataFrame with the necessary data
# Primary data source and columns
inputDf = dataSourceDict["1"]

# Output columns and initial values
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_CPA_QUARTER = 'Campaign CPA This Quarter'
RPT_COL_CPA_LW = 'All - Complete Registration - CPA'

# primary data source and columns
inputDf = dataSourceDict["1"]

# Calculate the threshold for flagging (30% higher or lower)
inputDf['Threshold_High'] = inputDf[RPT_COL_CPA_QUARTER] * 1.30
inputDf['Threshold_Low'] = inputDf[RPT_COL_CPA_QUARTER] * 0.70

# Create a new column to flag the campaigns that meet the criteria
inputDf['Flag'] = np.where((inputDf[RPT_COL_CPA_LW] > inputDf['Threshold_High']) | 
                           (inputDf[RPT_COL_CPA_LW] < inputDf['Threshold_Low']), 'Flag', '')

# Output the flagged campaigns
flagged_campaigns = inputDf[inputDf['Flag'] == 'Flag']

# Function to print the DataFrame as a table (for the purpose of this example)
def tableize(df):
    return df.to_string(index=False)

# Print the tableized version of the flagged campaigns DataFrame
print(tableize(flagged_campaigns))

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