Script 1211: MediaType Autotagging Campaign

Purpose:

The Python script automates the tagging of media types and sub-types for advertising campaigns based on specific account and publisher name criteria.

To Elaborate

The script is designed to streamline the process of categorizing advertising campaigns by automatically assigning media types and sub-types based on predefined criteria. It examines the account and publisher names within a dataset to determine the appropriate media type and sub-type tags. For instance, campaigns associated with ‘Taboola’ are tagged as ‘Native’, while those linked to ‘TikTok’ are labeled as ‘Social’. The script also handles more complex conditions, such as differentiating between Google campaigns based on specific campaign identifiers like ‘[GDN]’ or ‘[YT]’. This automation helps in maintaining consistency and accuracy in campaign categorization, which is crucial for reporting and analysis in digital marketing strategies.

Walking Through the Code

  1. Initialization:
    • Temporary columns for media type and sub-type are created and initialized with numpy.nan to store the tagging results.
    • The script uses inputDf as the primary DataFrame for processing.
  2. Tagging Logic:
    • The script applies a series of conditional checks using str.contains() to identify specific account or publisher names.
    • For each condition met, it assigns predefined media type and sub-type values to the temporary columns.
    • Examples include tagging ‘Taboola’ accounts as ‘Native’ and ‘TikTok’ accounts as ‘Social’.
  3. Complex Conditions:
    • Additional logic is applied for Google campaigns, where specific campaign identifiers like ‘[GDN]’, ‘[Demand Gen]’, and ‘[YT]’ further refine the tagging process.
    • These conditions allow for more granular categorization, such as distinguishing between ‘Display’, ‘Online video’, and ‘Demand Gen’.
  4. Output Preparation:
    • The tagged media type and sub-type values are copied from the temporary columns to the outputDf.
    • This ensures that the final output reflects the updated tagging strategy.
  5. User Changeable Parameters:
    • Users can modify the conditions and tagging values to suit different criteria or business rules, allowing flexibility in how campaigns are categorized.

Vitals

  • Script ID : 1211
  • Client ID / Customer ID: 1306925577 / 60269545
  • Action Type: Bulk Upload
  • Item Changed: Campaign
  • Output Columns: Account, Campaign, Media sub-type, Media Type
  • Linked Datasource: M1 Report
  • Reference Datasource: None
  • Owner: Anton Antonov (aantonov@marinsoftware.com)
  • Created by Anton Antonov on 2024-06-24 10:36
  • Last Updated by Anton Antonov on 2024-06-24 10:36
> See it in Action

Python Code

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RPT_COL_ACCOUNT = 'Account'
RPT_COL_CAMPAIGN = 'Campaign'
#RPT_COL_GROUP = 'Group'
RPT_COL_MEDIA_SUBTYPE = 'Media sub-type'
RPT_COL_MEDIA_TYPE = 'Media Type'
RPT_COL_ACCOUNT_PUBLISHER_NAME = 'Publisher Name'
RPT_COL_ACCOUNT_PUBLISHER = 'Publisher'
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
#BULK_COL_GROUP = 'Group'
BULK_COL_MEDIA_SUBTYPE = 'Media sub-type'
BULK_COL_MEDIA_TYPE = 'Media Type'
BULK_COL_ACCOUNT_PUBLISHER_NAME = 'Publisher Name'
BULK_COL_ACCOUNT_PUBLISHER = 'Publisher'

#outputDf[BULK_COL_MARKET] = "<<YOUR VALUE>>"

TMP_MediaType = 'Unknown Media Type'
TMP_SubType = 'Unknown Media sub-type'
# blank out tmp field
inputDf[TMP_MediaType] = numpy.nan
inputDf[TMP_SubType] = numpy.nan

today = datetime.datetime.now(CLIENT_TIMEZONE).date()
print(tableize(inputDf))


inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Taboola', na=False)) , TMP_MediaType ] = 'Native'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Taboola', na=False)) , TMP_SubType ] = 'Native'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('TikTok', na=False)) , TMP_MediaType ] = 'Social'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('TikTok', na=False)) , TMP_SubType ] = 'TikTok'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT_PUBLISHER_NAME].str.contains('Facebook', na=False)) , TMP_MediaType ] = 'Social'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT_PUBLISHER_NAME].str.contains('Facebook', na=False)) , TMP_SubType ] = 'Meta'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT_PUBLISHER_NAME].str.contains('BING', na=False)) , TMP_MediaType ] = 'SEM'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT_PUBLISHER_NAME].str.contains('BING', na=False)) , TMP_SubType ] = 'SEM-Bing'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT_PUBLISHER_NAME].str.contains('Google', na=False)) , TMP_MediaType ] = 'SEM'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT_PUBLISHER_NAME].str.contains('Google', na=False)) , TMP_SubType ] = 'SEM-Google'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT_PUBLISHER_NAME].str.contains('Google', na=False)) & (inputDf[RPT_COL_CAMPAIGN].str.contains('\[GDN\]', na=False)), TMP_MediaType ] = 'Display'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT_PUBLISHER_NAME].str.contains('Google', na=False)) & (inputDf[RPT_COL_CAMPAIGN].str.contains('\[GDN\]', na=False)), TMP_SubType ] = 'Standard display'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT_PUBLISHER_NAME].str.contains('Google', na=False)) & (inputDf[RPT_COL_CAMPAIGN].str.contains('\[Demand Gen\]', na=False)), TMP_MediaType ] = 'Display & Online video'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT_PUBLISHER_NAME].str.contains('Google', na=False)) & (inputDf[RPT_COL_CAMPAIGN].str.contains('\[Demand Gen\]', na=False)), TMP_SubType ] = 'Demand Gen'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT_PUBLISHER_NAME].str.contains('Google', na=False)) & (inputDf[RPT_COL_CAMPAIGN].str.contains('\[YT\]', na=False)), TMP_MediaType ] = 'Online video'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT_PUBLISHER_NAME].str.contains('Google', na=False)) & (inputDf[RPT_COL_CAMPAIGN].str.contains('\[YT\]', na=False)), TMP_SubType ] = 'VIDEO-Instream'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT_PUBLISHER_NAME].str.contains('Google', na=False)) & (inputDf[RPT_COL_CAMPAIGN].str.contains('\[YT\]', na=False)) & (inputDf[RPT_COL_CAMPAIGN].str.contains('Bumper', na=False)), TMP_SubType ] = 'VIDEO-Bumper'

print(tableize(inputDf))

#print(inputDf.index.duplicated())

# copy new strategy to output
outputDf.loc[:,BULK_COL_MEDIA_TYPE] = inputDf.loc[:, TMP_MediaType]
outputDf.loc[:,BULK_COL_MEDIA_SUBTYPE] = inputDf.loc[:, TMP_SubType]

#outputDf = outputDf[inputDf[TMP_MediaType].notnull() & inputDf[TMP_SubType].notnull() & ~inputDf[BULK_COL_CAMPAIGN].str.contains('"')]
print(tableize(outputDf))

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

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