Purpose:

Automates the population of utmcampaign and utmmedium dimensions based on campaign name and campaign type values.

To Elaborate

The Python script named ‘Campaign Dimension Update’ is designed to automate the process of populating two specific dimensions, utmcampaign and utmmedium, based on the values of campaign name and campaign type. This is particularly useful in digital marketing analytics where tracking parameters are essential for understanding the performance of various campaigns. The script processes data from a primary data source, filters out rows where these dimensions are already populated, and applies specific rules to determine the values for utmcampaign and utmmedium. The utmcampaign dimension is derived by transforming the campaign name to lowercase and replacing spaces with plus signs, while the utmmedium dimension is determined based on predefined rules associated with the campaign type, such as categorizing ‘Search’ as ‘paidsearch’ and ‘Display’ as ‘display’. This automation helps streamline the data preparation process for marketing analysis.

Walking Through the Code

1. Data Preparation:
   • The script begins by defining the primary data source and relevant columns, including campaign, account, campaign type, utmcampaign, and utmmedium.
   • Functions are defined to transform the campaign name and determine the utmmedium value based on campaign type.
2. Data Filtering and Transformation:
   • The script filters the input data to exclude rows where both utmcampaign and utmmedium are already populated.
   • It initializes a new dataframe, outputDf, with the filtered rows.
   • The transformation functions are applied to populate the utmcampaign and utmmedium columns in the output dataframe.
3. Output:
   • The script concludes by printing the first few rows of the transformed output dataframe, showcasing the updated dimensions.

Vitals

• Script ID : 1291
• Client ID / Customer ID: 1306913420 / 60268008
• Action Type: Bulk Upload (Preview)
• Item Changed: Campaign
• Output Columns: Account, Campaign, utmmedium, utmcampaign
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: Byron Porter (bporter@marinsoftware.com)
• Created by Byron Porter on 2024-07-24 22:21
• Last Updated by Kyle Perkins on 2024-08-20 14:22

> See it in Action

Python Code

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##
## name: Campaign Dimension Update
## description:
##  Automates populating utmcampaign and utmmedium dimensions based on campaign name and campaign type values, respectively
## 
## author: Byron Porter
## created: 2024-07-24
## 

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_CAMPAIGN_TYPE = 'Campaign Type'
RPT_COL_UTMMEDIUM = 'utmmedium'
RPT_COL_UTMCAMPAIGN = 'utmcampaign'

# output columns and initial values
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_UTMCAMPAIGN = 'utmcampaign'
BULK_COL_UTMMEDIUM = 'utmmedium'

# define function to replace ' ' with '+' and convert to lowercase in Campaign value
def set_utm_campaign(campaign_name):
    return campaign_name.replace(' ', '+').lower()

# define function to determine utmmedium dimension value based on Campaign Type value
def set_utm_medium(campaign_type):
    if campaign_type == "Search":
        return "paidsearch"
    elif campaign_type in ["Display", "Video", "Discovery"]:
        return "display"
    elif campaign_type == "Performance Max":
        return "paidsearch"
    else:
        return None

# filter inputDf to skip rows where both utmmedium and utmcampaign are populated and copy rows to new filteredDf dataframe
filteredDf = inputDf[
    ~inputDf[RPT_COL_UTMMEDIUM].notna() | ~inputDf[RPT_COL_UTMCAMPAIGN].notna()
].copy()

# intialize outputDf so that it only includes rows that have been filtered
outputDf = filteredDf

# apply the functions to each row in the filtered dataframe and set result as the output dataframe value
outputDf[BULK_COL_UTMCAMPAIGN] = filteredDf[RPT_COL_CAMPAIGN].apply(set_utm_campaign)
outputDf[BULK_COL_UTMMEDIUM] = filteredDf[RPT_COL_CAMPAIGN_TYPE].apply(set_utm_medium)

print(tableize(outputDf.head()))

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