Script 1355: Dimension Update Brand
Purpose:
Automates the process of populating the brand dimension in a dataset based on specific rules applied to campaign name and type values.
To Elaborate
The Python script is designed to automate the classification of brand dimensions within a dataset by analyzing campaign names and types. It applies a set of predefined rules to determine the brand category, such as “Brand,” “Non-Brand,” “N/A,” or “Other,” based on keywords found in the campaign name and the type of campaign. This helps streamline the process of categorizing campaigns for reporting and analysis purposes, ensuring consistency and accuracy in brand dimension allocation. The script filters out rows where the brand is already populated, focusing only on those that need classification, and updates the dataset accordingly.
Walking Through the Code
- Data Preparation
- The script begins by defining the primary data source and relevant columns, including campaign, account, and brand columns.
- It filters the input data to exclude rows where the brand is already populated, ensuring only necessary rows are processed.
- Brand Determination Function
- A function named
determine_brandis defined to classify the brand based on campaign name and type. - The function converts campaign name and type to lowercase for case-insensitive comparison.
- It checks for specific keywords in the campaign name and type to assign the appropriate brand category, following a set of predefined rules.
- A function named
- Applying the Function
- The script applies the
determine_brandfunction to each row in the filtered dataset using theapplymethod. - The results are stored in the output dataframe, updating the brand column with the determined values.
- The script applies the
- Output
- Finally, the script prints the first few rows of the updated dataset to verify the changes.
Vitals
- Script ID : 1355
- Client ID / Customer ID: 1306913420 / 60268008
- Action Type: Bulk Upload
- Item Changed: Campaign
- Output Columns: Account, Campaign, Brand
- Linked Datasource: M1 Report
- Reference Datasource: None
- Owner: Kyle Perkins (kyle.perkins@genesys.com)
- Created by Kyle Perkins on 2024-08-28 20:14
- Last Updated by Kent Pearce on 2024-09-26 16:41
> See it in Action
Python Code
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##
## name: Dimension Update - Brand
## description:
## Automates populating brand dimension based on campaign name values.
##
## author:
## created: 2024-08-28
##
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_BRAND = 'Brand' # Reference to the brand column
RPT_COL_CAMPAIGN_TYPE = 'Campaign Type' # Reference to the campaign type column
# Output columns and initial values
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_BRAND = 'Brand' # Output brand column
# Define the function to determine the brand based on the provided rules
def determine_brand(campaign_name, campaign_type):
# Ensure campaign_name and campaign_type are strings
if not isinstance(campaign_name, str):
campaign_name = str(campaign_name)
if not isinstance(campaign_type, str):
campaign_type = str(campaign_type)
campaign_name = campaign_name.lower() # Convert to lowercase to ignore case
campaign_type = campaign_type.lower() # Convert campaign type to lowercase
# Check if the campaign type is one of the specified types where Brand should be "N/A"
if campaign_type in ["display", "audience", "video", "discovery", "performance max"]:
return "N/A"
if "ivr" in campaign_name:
return "Non-Brand"
elif "compet" in campaign_name:
return "Non-Brand"
elif "nonbrand" in campaign_name:
return "Non-Brand"
elif "dsa" in campaign_name:
return "N/A"
elif "na_pt" in campaign_name:
return "Non-Brand"
elif "chat" in campaign_name:
return "Non-Brand"
elif "callcenter" in campaign_name:
return "Non-Brand"
elif "rlsa-brand" in campaign_name or "rlsa_brand" in campaign_name:
return "Brand"
elif "rlsa-nonbrand" in campaign_name or "rlsa_nonbrand" in campaign_name:
return "Non-Brand"
elif "brand" in campaign_name:
return "Brand"
elif "rlsa-competitor" in campaign_name or "rlsa_competitor" in campaign_name:
return "Non-Brand"
elif "generic" in campaign_name:
return "Non-Brand"
elif "feature" in campaign_name:
return "Non-Brand"
elif "callmanagement" in campaign_name:
return "Non-Brand"
elif "contact" in campaign_name:
return "Non-Brand"
elif "dialer" in campaign_name:
return "Non-Brand"
elif "pointillist" in campaign_name:
return "Non-Brand"
elif "customerjourney" in campaign_name:
return "Non-Brand"
elif "rlsa" in campaign_name:
return "RLSA (Both)"
elif "customerservice" in campaign_name:
return "Non-Brand"
elif "wfm" in campaign_name or "wem" in campaign_name:
return "Non-Brand"
elif "affinity" in campaign_name or "customintent" in campaign_name or "inmarket" in campaign_name:
return "N/A"
elif "video" in campaign_name or "youtube" in campaign_name or "videoremarketing" in campaign_name or "remarketing" in campaign_name or "retargeting" in campaign_name:
return "N/A"
elif "max" in campaign_name:
return "N/A"
elif "cx" in campaign_name:
return "Non-Brand"
else:
return "Other"
# Filter inputDf to skip rows where Brand is already populated regardless of campaign type
filteredDf = inputDf[inputDf[RPT_COL_BRAND].isna()].copy()
# Initialize outputDf so that it only includes rows that have been filtered
outputDf = filteredDf
# Apply the function to each row in the filtered dataframe and set result as the output dataframe value
outputDf[BULK_COL_BRAND] = filteredDf.apply(
lambda row: determine_brand(row[RPT_COL_CAMPAIGN], row[RPT_COL_CAMPAIGN_TYPE]), axis=1
)
# Printing the result
print(tableize(outputDf.head()))
Post generated on 2025-03-11 01:25:51 GMT