Purpose:

The Python script processes keyword data to manage and optimize advertising campaigns by pausing certain keywords based on performance metrics.

To Elaborate

The script is designed to manage and optimize advertising campaigns by analyzing keyword performance data. It evaluates various performance metrics such as publication cost, conversion rate, clicks, impressions, and conversions. Based on these metrics, the script identifies keywords that may not be performing well and suggests pausing them to improve the overall efficiency of the campaign. The script uses structured budget allocation (SBA) principles to ensure that advertising budgets are spent effectively, focusing on high-performing keywords while minimizing expenditure on underperforming ones. This approach helps in maximizing return on investment (ROI) for advertising campaigns.

Walking Through the Code

1. Initialization of Constants:
   • The script begins by defining constants for column names used in reports and bulk operations. These constants represent various performance metrics and campaign attributes such as keywords, publication cost, conversion rate, clicks, impressions, and conversions.
2. Output DataFrame Configuration:
   • The script sets up an output DataFrame with placeholders for account, group, match type, and status. These placeholders are user-changeable parameters that need to be filled with actual values relevant to the user’s advertising account and campaign structure.
3. Data Processing:
   • The script processes the input DataFrame, which contains keyword performance data. It uses this data to evaluate the performance of each keyword based on the defined metrics.
4. Performance Evaluation:
   • Although not explicitly shown in the provided code, the script likely includes logic to assess which keywords should be paused based on their performance metrics. This involves comparing metrics like conversion rate and cost against predefined thresholds or business rules.
5. Output and Reporting:
   • The script outputs the processed data, potentially highlighting keywords that are recommended for pausing. This output is intended to guide users in making informed decisions about their keyword management strategies.

Vitals

• Script ID : 195
• Client ID / Customer ID: 309909744 / 14196
• Action Type: Bulk Upload (Preview)
• Item Changed: Keyword
• Output Columns: Account, Campaign, Group, Keyword, Match Type, Status
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: emerryfield@marinsoftware.com (emerryfield@marinsoftware.com)
• Created by emerryfield@marinsoftware.com on 2023-06-15 17:21
• Last Updated by emerryfield@marinsoftware.com on 2023-12-06 04:01

> See it in Action

Python Code

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RPT_COL_KEYWORD = 'Keyword'
RPT_COL_PUB_COST = 'Pub. Cost $'
RPT_COL_CONV_RATE = 'Conv. Rate %'
RPT_COL_CLICKS = 'Clicks'
RPT_COL_IMPR = 'Impr.'
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_CONV = 'Conv.'
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_GROUP = 'Group'
BULK_COL_KEYWORD = 'Keyword'
BULK_COL_MATCH_TYPE = 'Match Type'
BULK_COL_STATUS = 'Status'

outputDf[BULK_COL_ACCOUNT] = "<<YOUR VALUE>>"
outputDf[BULK_COL_GROUP] = "<<YOUR VALUE>>"
outputDf[BULK_COL_MATCH_TYPE] = "<<YOUR VALUE>>"
outputDf[BULK_COL_STATUS] = "<<YOUR VALUE>>"

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

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