Script 1353: Dimension Update Category

Purpose:

Automates the categorization of campaigns by analyzing campaign names and assigning them to predefined categories.

To Elaborate

The Python script is designed to automate the process of categorizing campaigns based on their names. It takes a dataset containing campaign information and applies a set of predefined rules to determine the category for each campaign. The script specifically targets campaigns that do not already have a category assigned. By analyzing the campaign names, it assigns categories such as “DSA,” “Chat Software,” “PMAX,” and others based on specific keywords found within the campaign names. This automation helps streamline the categorization process, ensuring consistency and reducing manual effort in managing campaign data.

Walking Through the Code

  1. Data Preparation:
    • The script begins by loading the primary data source into a DataFrame named inputDf.
    • It identifies key columns such as ‘Campaign’, ‘Account’, and ‘Category’ for processing.
  2. Category Determination Function:
    • A function determine_category is defined to categorize campaigns based on their names.
    • The function converts campaign names to lowercase and checks for specific keywords to assign categories.
  3. Filtering Data:
    • The script filters inputDf to create filteredDf, which includes only rows where the category is not already populated.
  4. Applying Categorization:
    • The determine_category function is applied to each campaign name in filteredDf.
    • The results are stored in outputDf, updating the ‘Category’ column with the determined categories.
  5. Output:
    • The script prints the first few rows of the categorized data for verification.

Vitals

  • Script ID : 1353
  • Client ID / Customer ID: 1306913420 / 60268008
  • Action Type: Bulk Upload
  • Item Changed: Campaign
  • Output Columns: Account, Campaign, Category
  • Linked Datasource: M1 Report
  • Reference Datasource: None
  • Owner: Kyle Perkins (kyle.perkins@genesys.com)
  • Created by Kyle Perkins on 2024-08-28 19:23
  • Last Updated by Kyle Perkins on 2024-08-28 19:51
> See it in Action

Python Code

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##
## name: Dimension Update - Category
## description:
##  Automates populating category 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_CATEGORY = 'Category'  # Reference to the category column

# Output columns and initial values
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_CATEGORY = 'Category'  # Output category column

# Define the function to determine the category based on the provided rules
def determine_category(campaign_name):
    campaign_name = campaign_name.lower()  # Convert to lowercase to ignore case
    if "dsa" in campaign_name:
        return "DSA"
    elif "bng_non" in campaign_name:
        return "Chat Software"
    elif "ggl_non" in campaign_name:
        return "Chat Software"
    elif "performance-max" in campaign_name or "performance max" in campaign_name:
        return "PMAX"
    elif "rlsa-compet" in campaign_name or "rlsa_compet" in campaign_name:
        return "RLSA Competitors"
    elif "compet" in campaign_name:
        return "Competitors"
    elif "rlsa-brand" in campaign_name or "rlsa_brand" in campaign_name:
        return "RLSA Brand"
    elif "rlsa-nonbrand" in campaign_name or "rlsa_nonbrand" in campaign_name:
        return "RLSA Non-Brand"
    elif "display_brand" in campaign_name:
        return "Display Prospecting"
    elif "brand" in campaign_name:
        return "Brand"
    elif "videoremarketing" in campaign_name or "youtube-remarketing" in campaign_name or "youtuberemarketing" in campaign_name:
        return "YouTube Remarketing"
    elif "youtube-custom" in campaign_name or "youtube-inmarket" in campaign_name:
        return "YouTube Prospecting"
    elif "callmanagement" in campaign_name:
        return "Call Management"
    elif "rlsa" in campaign_name:
        return "RLSA"
    elif "chat" in campaign_name:
        return "Chat Software"
    elif "contact" in campaign_name:
        return "Contact Center"
    elif "dialer" in campaign_name:
        return "Dialers"
    elif "customerservice" in campaign_name:
        return "Customer Service"
    elif "wfm" in campaign_name or "wem" in campaign_name:
        return "WFM"
    elif "ivr" in campaign_name:
        return "IVR"
    elif "remarketing" in campaign_name or "retargeting" in campaign_name:
        return "Display Remarketing"
    elif "display_" in campaign_name:
        return "Display Prospecting"
    elif "callcenter" in campaign_name:
        return "Call Center"
    elif "cx" in campaign_name or "customerexperience" in campaign_name:
        return "CX"
    elif "pointillist" in campaign_name or "journey" in campaign_name:
        return "Pointillist"
    elif "na_pt-remarketing" in campaign_name:
        return "Display Remarketing"
    elif "na_pt" in campaign_name:
        return "Pointillist"
    elif "summit" in campaign_name or "gitex" in campaign_name or "xperience" in campaign_name or "event" in campaign_name:
        return "Event"
    elif "intent" in campaign_name or "inmarket" in campaign_name or "discovery-prospecting" in campaign_name or "affinity" in campaign_name or "gdn" in campaign_name:
        return "Display Prospecting"
    elif "mk_" in campaign_name or "bng_non" in campaign_name:
        return "Chat Software"
    elif "generic" in campaign_name or "feature" in campaign_name or "product" in campaign_name:
        return "Other"
    else:
        return "Other"

# Filter inputDf to skip rows where category is already populated and copy rows to new filteredDf dataframe
filteredDf = inputDf[~inputDf[RPT_COL_CATEGORY].notna()].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_CATEGORY] = filteredDf[RPT_COL_CAMPAIGN].apply(determine_category)

# Printing the result
print(tableize(outputDf.head()))

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

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