Purpose:

The script identifies and tags the top-performing keywords in a Google Ads report based on cost per conversion.

To Elaborate

The Python script is designed to process a Google Ads report and identify the top-performing keywords based on their cost per conversion. The report is sorted in descending order of cost per conversion, and the script selects the top 10 keywords from this sorted list. These selected keywords are then tagged as “Top Performer” in a new dataframe. This process helps in quickly identifying which keywords are performing best in terms of cost efficiency, allowing for more informed decision-making in managing advertising budgets and optimizing campaigns.

Walking Through the Code

1. Initialization of Constants:
   • The script begins by defining several constants that represent column names used in the input and output dataframes. These constants are used to ensure consistency and avoid hardcoding column names throughout the script.
2. Data Preparation:
   • The script copies the first 10 rows of the input dataframe (inputDf) into a new dataframe (outputDf). This selection is based on the assumption that the input dataframe is already sorted by cost per conversion in descending order, meaning the top 10 rows represent the keywords with the highest cost efficiency.
3. Tagging Top Performers:
   • A new column, Top Keywords, is added to the outputDf dataframe, and the value “Top Performer” is assigned to each of the top 10 keywords. This tagging helps in easily identifying these keywords in subsequent analyses or reports.
4. Output:
   • The script prints the outputDf dataframe, which now contains the top 10 keywords tagged as “Top Performer”, allowing users to review the results directly.

Vitals

• Script ID : 1367
• Client ID / Customer ID: 1306923239 / 60269197
• Action Type: Bulk Upload
• Item Changed: Keyword
• Output Columns: Account, Campaign, Group, Keyword, Match Type, Top Keywords
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: Autumn Archibald (aarchibald@marinsoftware.com)
• Created by Autumn Archibald on 2024-09-04 21:55
• Last Updated by Autumn Archibald on 2024-09-04 21:59

> See it in Action

Python Code

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RPT_COL_KEYWORD = 'Keyword'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_GROUP = 'Group'
RPT_COL_MATCH_TYPE = 'Match Type'
RPT_COL_STATUS = 'Status'
RPT_COL_TOTAL_DONATIONS = 'Total Donations (Conversions)'
RPT_COL_COST_PER_CONV = 'Cost/Conv. $'
RPT_COL_TOP_KEYWORDS = 'Top Keywords'

BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_GROUP = 'Group'
BULK_COL_KEYWORD = 'Keyword'
BULK_COL_MATCH_TYPE = 'Match Type'
BULK_COL_TOP_KEYWORDS = 'Top Keywords'

# Assign current date to a parameter
today = datetime.datetime.now(CLIENT_TIMEZONE).date()

# Copy the first 10 rows of input dataframe to a new dataframe
# **incoming report is sorted by highest cost/conv to the lowest cost/conv**
outputDf = inputDf.head(10)
outputDf[BULK_COL_TOP_KEYWORDS] = "Top Performer"

print("outputDf", tableize(outputDf))

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