Script 1351: Dimension Update Device and Match Type

Purpose:

Automates the process of populating device and match type dimensions in a dataset based on campaign name values.

To Elaborate

The Python script is designed to automate the task of populating device and match type dimensions for marketing campaigns based on the values found in campaign names. It processes a dataset containing campaign information and applies specific rules to determine the appropriate device and match type for each campaign. The script filters out campaigns that already have these dimensions populated, ensuring that only necessary updates are made. By automating this process, the script helps streamline the management of campaign data, reducing manual effort and minimizing errors in data entry.

Walking Through the Code

  1. Data Preparation:
    • The script begins by defining the primary data source and relevant columns, such as ‘Campaign’, ‘Account’, ‘Campaign Type’, ‘Device Target’, and ‘Match Type Target’.
    • It initializes the output columns that will store the results of the device and match type determinations.
  2. Device Type Determination:
    • A function determine_device_type is defined to analyze campaign names and assign a device type based on specific naming conventions.
    • The function checks for keywords or patterns in the campaign name, such as “m”, “_dt”, “desktop”, or “mobile”, to determine if the campaign targets mobile (M), desktop (DT), or all devices.
  3. Match Type Determination:
    • Another function, determine_match_type, is defined to determine the match type based on both the campaign name and campaign type.
    • It checks for specific keywords like “bmm”, “exact”, “phrase”, or “broad” in the campaign name and considers the campaign type to assign a match type, defaulting to “N/A” for certain campaign types.
  4. Data Filtering and Processing:
    • The script filters the input data to exclude rows where device type and match type are already populated.
    • It creates a new dataframe, outputDf, to store the filtered data.
  5. Applying Functions:
    • The script applies the determine_device_type function to each campaign name in the filtered data to populate the device type column.
    • It also applies the determine_match_type function to each row, considering both the campaign name and type, to populate the match type column.
  6. Output:
    • Finally, the script prints the first few rows of the updated dataframe to display the results of the automation process.

Vitals

  • Script ID : 1351
  • Client ID / Customer ID: 1306913420 / 60268008
  • Action Type: Bulk Upload
  • Item Changed: Campaign
  • Output Columns: Account, Campaign, Match Type Target, Device Target
  • Linked Datasource: M1 Report
  • Reference Datasource: None
  • Owner: Kyle Perkins (kyle.perkins@genesys.com)
  • Created by Kyle Perkins on 2024-08-28 18:48
  • Last Updated by Kyle Perkins on 2024-08-28 19:12
> See it in Action

Python Code

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##
## name: Dimension Update - Device & Match Type
## description:
##  Automates populating device and match type dimensions 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_CAMPAIGN_TYPE = 'Campaign Type'  # Reference to the campaign type column
RPT_COL_DEVICE_TYPE = 'Device Target'  # Reference to the device type column
RPT_COL_MATCH_TYPE = 'Match Type Target'  # Reference to the match type column

# Output columns and initial values
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_DEVICE_TYPE = 'Device Target'  # Output device type column
BULK_COL_MATCH_TYPE = 'Match Type Target'  # Output match type column

# Define the function to determine device type based on the provided rules
def determine_device_type(campaign_name):
    campaign_name = campaign_name.lower()  # Convert to lowercase to ignore case
    if campaign_name.endswith("_m"):
        return "M"
    elif "_dt_" in campaign_name:
        return "DT"
    elif "_m_" in campaign_name:
        return "M"
    elif campaign_name.endswith("_dt"):
        return "DT"
    elif "desktop" in campaign_name:
        return "DT"
    elif "mobile" in campaign_name:
        return "M"
    else:
        return "All"

# Define the function to determine match type based on the provided rules
def determine_match_type(campaign_name, 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 Match Type should be "N/A"
    if campaign_type in ["display", "audience", "video", "discovery", "performance max"]:
        return "N/A"

    if "bmm" in campaign_name:
        return "BMM"
    elif "exact" in campaign_name:
        return "Exact"
    elif "phrase" in campaign_name:
        return "Phrase"
    elif "broad" in campaign_name:
        return "BMM"
    else:
        return "All"

# Filter inputDf to skip rows where device type and match type are already populated
filteredDf = inputDf[
    ~inputDf[RPT_COL_DEVICE_TYPE].notna() | ~inputDf[RPT_COL_MATCH_TYPE].notna()
].copy()

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

# Apply the functions to each row in the filtered dataframe
outputDf[BULK_COL_DEVICE_TYPE] = filteredDf[RPT_COL_CAMPAIGN].apply(determine_device_type)
outputDf[BULK_COL_MATCH_TYPE] = filteredDf.apply(
    lambda row: determine_match_type(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

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