Purpose:

The Python script identifies and labels the top 10 performing keywords based on cost per conversion from a sorted report.

To Elaborate

The Python script processes a report containing keyword performance data, specifically focusing on cost per conversion metrics. It aims to identify the top 10 keywords that are performing best in terms of cost efficiency. The script assumes that the input data is already sorted from the highest to the lowest cost per conversion. By selecting the top 10 entries, it labels these keywords as “Top Performer” in a new column, providing a quick reference for marketing teams to focus on the most cost-effective keywords in their campaigns.

Walking Through the Code

1. Data Preparation:
   • The script begins by defining several constants that represent column names used in the report and bulk data. These constants help in maintaining consistency and readability throughout the script.
2. Data Processing:
   • 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 data is pre-sorted by cost per conversion, from highest to lowest.
3. Labeling:
   • A new column, Top Keywords, is added to the outputDf DataFrame, where each of the top 10 keywords is labeled as “Top Performer”. This step highlights the best-performing keywords for easy identification.
4. Output:
   • Finally, the script prints the outputDf DataFrame in a tabular format, showcasing the top-performing keywords and their associated data.

Vitals

• Script ID : 1369
• 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 22:07
• Last Updated by Autumn Archibald on 2024-09-04 22:18

> 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