Purpose:

The Python script processes marketing data to determine and populate the “NTB Uplift” dimension based on specific business conditions.

To Elaborate

The script is designed to analyze marketing data and determine the “NTB Uplift” (New To Brand Uplift) for specific advertising strategies. It evaluates data from a primary source to identify whether certain conditions are met for different advertising strategies, such as “Sponsored Brand Test - Watch,” “Sponsored Brand Test - iPad,” and “Sponsored Brand Test - Mac Brand.” The conditions involve checking if the revenue is greater than zero, the Return on Advertising Spend (ROAS) exceeds a specified threshold, and the percentage of orders new to the brand over a 14-day period is above 0.5. If these conditions are satisfied, the script calculates the NTB Uplift as a percentage and updates the data accordingly. This helps in assessing the effectiveness of different advertising strategies in attracting new customers.

Walking Through the Code

1. Initialization and Data Setup
   • The script begins by defining a dictionary MATCH_TYPE to categorize match types as ‘exact’, ‘phrase’, or ‘broad’.
   • It retrieves the primary data source into inputDf and defines several column names for use in the script.
2. Output Column Initialization
   • The script initializes the output DataFrame outputDf and sets the initial value for the ‘NTB Uplift’ column to a placeholder.
3. Data Processing Function
   • A function process is defined to handle the main logic of the script.
   • It sets up conditions for three different advertising strategies. Each condition checks if the strategy matches a specific type, revenue is greater than zero, ROAS exceeds a certain threshold, and the percentage of new orders is above 0.5.
4. Condition Application and Calculation
   • The script applies these conditions to the input DataFrame. If any condition is met, it calculates the NTB Uplift by adjusting the percentage of new orders and rounding it to the nearest integer.
5. Execution and Output
   • The process function is called with inputDf, and the resulting DataFrame is stored in outputDf.
   • The script concludes by printing the processed data, showing the updated NTB Uplift values.

Vitals

• Script ID : 475
• Client ID / Customer ID: 1306926773 / 50395
• Action Type: Bulk Upload
• Item Changed: Keyword
• Output Columns: Account, Campaign, Group, Keyword, Match Type, NTB Uplift
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: Jeremy Brown (jbrown@marinsoftware.com)
• Created by Jeremy Brown on 2023-11-03 15:00
• Last Updated by Jeremy Brown on 2024-12-10 13:34

> See it in Action

Python Code

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##
## name: SCRIPT: Populate NTB Uplift Dimension
## description:
##  
## 
## author: 
## created: 2023-11-03
## 

MATCH_TYPE = {
  'EXACT': 'exact',
  'PHRASE': 'phrase',
  'BROAD': 'broad',
}
today = datetime.datetime.now(CLIENT_TIMEZONE).date()

# primary data source and columns
inputDf = dataSourceDict["1"]
RPT_COL_KEYWORD = 'Keyword'
RPT_COL_MATCH_TYPE = 'Match Type'
RPT_COL_GROUP = 'Group'
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_CAMPAIGN_TYPE = 'Campaign Type'
 
RPT_COL_CLICKS = 'Clicks'
RPT_COL_REVENUE = 'Revenue $'
RPT_COL_ROAS = 'ROAS'
RPT_COL_ORDERS_NEW_TO_BRAND_14_DAY = '% Orders New To Brand (14 Day)'
RPT_COL_SALES_NEW_TO_BRAND_14_DAY = '% Sales New To Brand (14 Day)'
RPT_COL_NTB_UPLIFT = 'NTB Uplift'

# output columns and initial values
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_GROUP = 'Group'
BULK_COL_KEYWORD = 'Keyword'
BULK_COL_MATCH_TYPE = 'Match Type'
BULK_COL_NTB_UPLIFT = 'NTB Uplift'
outputDf[BULK_COL_NTB_UPLIFT] = "<<YOUR VALUE>>"

# user code start here
print(tableize(inputDf))

# Function to process the data
def process(inputDf):

    # Define the conditions for setting 'NTB Uplift' to 'YES'
    condition1 = (inputDf['Strategy'] == 'Sponsored Brand Test - Watch') & (inputDf['Revenue $'] > 0.00) & (inputDf['ROAS'] > 11) & (inputDf['% Orders New To Brand (14 Day)'] > 0.5)
    condition2 = (inputDf['Strategy'] == 'Sponsored Brand Test - iPad') & (inputDf['Revenue $'] > 0.00) & (inputDf['ROAS'] > 14) & (inputDf['% Orders New To Brand (14 Day)'] > 0.5)
    condition3 = (inputDf['Strategy'] == 'Sponsored Brand Test - Mac Brand') & (inputDf['Revenue $'] > 0.00) & (inputDf['ROAS'] > 15) & (inputDf['% Orders New To Brand (14 Day)'] > 0.5)

    # Apply the conditions to set 'NTB Uplift' to 'YES' where applicable
    inputDf['NTB Uplift'] = ''
    inputDf.loc[condition1 | condition2, 'NTB Uplift'] = ((inputDf['% Orders New To Brand (14 Day)'] -0.5)*100).apply(lambda x: int(round(x, 0)))

    return inputDf

# Trigger the main process
outputDf = process(inputDf)

print(tableize(outputDf))

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