Script 893: campaign 30 day cost conv assignment

Purpose:

The Python script updates the “Campaign 30 Day Cost per Conversion” dimension daily to support keyword-level anomaly detection for cost per conversion.

To Elaborate

The script is designed to automate the daily update of the “Campaign 30 Day Cost per Conversion” dimension within a dataset. This update is crucial for facilitating a keyword-level anomaly detection process that focuses on cost per conversion metrics. By ensuring that this dimension is regularly updated, the script helps maintain accurate and timely data for analysis, which is essential for identifying any unusual patterns or discrepancies in campaign performance. The script extracts relevant data from a primary data source, processes it, and updates the output dataset with the latest cost per conversion values, thereby supporting ongoing monitoring and optimization efforts.

Walking Through the Code

  1. Data Source Initialization:
    • The script begins by defining the primary data source and specifying the columns that will be used for processing. This includes columns related to campaign details and cost per conversion metrics.
  2. Data Extraction and Preparation:
    • A subset of columns is selected from the input data frame (inputDf) to create a new data frame (df). This subset includes the account, campaign, and cost per conversion columns.
    • The script copies these columns to ensure that the data is ready for further processing.
  3. Output Data Frame Update:
    • The output data frame (outputDf) is updated with values from the input data frame. Specifically, the account, campaign, and campaign 30-day cost per conversion columns are populated with the corresponding values from the input data.
    • The script sets an initial placeholder value for the “Campaign 30 Day Cost per Conversion” column, which can be customized by the user.
  4. User Changeable Parameters:
    • The placeholder value for the “Campaign 30 Day Cost per Conversion” in the output data frame is user-defined and can be adjusted to meet specific requirements or conditions.

Vitals

  • Script ID : 893
  • Client ID / Customer ID: 1306913420 / 60268008
  • Action Type: Bulk Upload
  • Item Changed: Campaign
  • Output Columns: Account, Campaign, Campaign 30 Day Cost per Conversion
  • Linked Datasource: M1 Report
  • Reference Datasource: None
  • Owner: Chris Jetton (cjetton@marinsoftware.com)
  • Created by Chris Jetton on 2024-04-02 16:36
  • Last Updated by Chris Jetton on 2024-04-02 16:57
> See it in Action

Python Code

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##
## name: campaign 30 day cost/conv assignment
## description:
##  daily job that will update the value of the "Campaign 30 day cost per conversion" dimension to facilitate the keyword-level anomaly detection script for cost/conv
## 
## author: 
## created: 2024-04-02
## 

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

# primary data source and columns
inputDf = dataSourceDict["1"]
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_CUSTOM_PARAMETERS = 'Custom Parameters'
RPT_COL_CAMPAIGN_30_DAY_COST_PER_CONVERSION = 'Campaign 30 Day Cost per Conversion'
RPT_COL_CAMPAIGN_STATUS = 'Campaign Status'
RPT_COL_ACTIVE_GROUPS = 'Active Groups'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_HISTORICAL_CONVERSIONS = 'Historical Conversions'
RPT_COL_IMPR = 'Impr.'
RPT_COL_CLICKS = 'Clicks'
RPT_COL_UTMCAMPAIGN = 'utmcampaign'
RPT_COL_PUB_COST = 'Pub. Cost $'
RPT_COL_CONV = 'Conv.'
RPT_COL_COST_PER_CONV = 'Cost/Conv. $'
RPT_COL_CTR = 'CTR %'
RPT_COL_DAILY_BUDGET = 'Daily Budget'
RPT_COL_SUBREGION = 'Subregion'
RPT_COL_BRAND = 'Brand'
RPT_COL_INQUIRY_OFFLINE_CONV_DATE_OF_CONV = 'Inquiry (Offline) Conv. (Date Of Conv.)'
RPT_COL_INQUIRY_OFFLINE_REVENUE_DATE_OF_CONV = 'Inquiry (Offline) Revenue (Date Of Conv.)'
RPT_COL_MCL_OFFLINE_CONV_DATE_OF_CONV = 'MCL (Offline) Conv. (Date Of Conv.)'
RPT_COL_MCL_OFFLINE_REVENUE_DATE_OF_CONV = 'MCL (Offline) Revenue (Date Of Conv.)'
RPT_COL_MQL_OFFLINE_CONV_DATE_OF_CONV = 'MQL (Offline) Conv. (Date Of Conv.)'
RPT_COL_MQL_OFFLINE_REVENUE_DATE_OF_CONV = 'MQL (Offline) Revenue (Date Of Conv.)'
RPT_COL_OPPORTUNITY_OFFLINE_CONV_DATE_OF_CONV = 'Opportunity (Offline) Conv. (Date Of Conv.)'
RPT_COL_OPPORTUNITY_OFFLINE_REVENUE_DATE_OF_CONV = 'Opportunity (Offline) Revenue (Date Of Conv.)'
RPT_COL_PIPELINE_OPPORTUNITY_OFFLINE_CONV_DATE_OF_CONV = 'Pipeline Opportunity (Offline) Conv. (Date Of Conv.)'
RPT_COL_PIPELINE_OPPORTUNITY_OFFLINE_REVENUE_DATE_OF_CONV = 'Pipeline Opportunity (Offline) Revenue (Date Of Conv.)'
RPT_COL_SAL_OFFLINE_CONV_DATE_OF_CONV = 'SAL (Offline) Conv. (Date Of Conv.)'
RPT_COL_SAL_OFFLINE_REVENUE_DATE_OF_CONV = 'SAL (Offline) Revenue (Date Of Conv.)'
RPT_COL_SQL_OFFLINE_CONV_DATE_OF_CONV = 'SQL (Offline) Conv. (Date Of Conv.)'
RPT_COL_SQL_OFFLINE_REVENUE_DATE_OF_CONV = 'SQL (Offline) Revenue (Date Of Conv.)'
RPT_COL_WON_OPPORTUNITY_OFFLINE_CONV_DATE_OF_CONV = 'Won Opportunity (Offline) Conv. (Date Of Conv.)'
RPT_COL_WON_OPPORTUNITY_OFFLINE_REVENUE_DATE_OF_CONV = 'Won Opportunity (Offline) Revenue (Date Of Conv.)'

# output columns and initial values
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_CAMPAIGN_30_DAY_COST_PER_CONVERSION = 'Campaign 30 Day Cost per Conversion'
outputDf[BULK_COL_CAMPAIGN_30_DAY_COST_PER_CONVERSION] = "<<YOUR VALUE>>"

# user code start here
cols = [RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN, RPT_COL_COST_PER_CONV]
df = inputDf[cols].copy()
#df = inputDf[cols].copy().head(1)  # Select only the first row
outputDf[BULK_COL_ACCOUNT] = inputDf[RPT_COL_ACCOUNT]
outputDf[BULK_COL_CAMPAIGN] = inputDf[RPT_COL_CAMPAIGN]
outputDf[BULK_COL_CAMPAIGN_30_DAY_COST_PER_CONVERSION] = inputDf[RPT_COL_COST_PER_CONV]

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

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