Script 329: Conversion Influencers

Purpose:

The Python script identifies and labels the top 10 keywords based on cost per conversion over the last 60 days, requiring at least one conversion to be considered.

To Elaborate

The script is designed to analyze keyword performance in advertising campaigns by focusing on cost efficiency in terms of conversions. It evaluates keywords based on their cost per conversion over a specified period of 60 days. To qualify for consideration, a keyword must have achieved at least one conversion. The script then selects the top 10 keywords with the highest cost efficiency, labeling them as “Top Performer” in the Conversion Influencers dimension. This helps businesses identify which keywords are driving the most cost-effective conversions, allowing them to allocate their budget more strategically.

Walking Through the Code

  1. Initialization: The script begins by defining constants for column names used in reports and bulk data operations. These constants help maintain consistency and readability throughout the code.

  2. Date Assignment: The current date is assigned to a variable using the client’s timezone. This step is crucial for ensuring that the analysis is based on the most recent data available.

  3. Data Processing: The script processes an input dataframe that is pre-sorted by cost per conversion, from highest to lowest. It extracts the first 10 rows, which represent the top-performing keywords based on cost efficiency.

  4. Labeling: The selected top 10 keywords are labeled as “Top Performer” in the Conversion Influencers column. This labeling helps in identifying keywords that are most effective in terms of cost per conversion.

  5. Output: The script prints the resulting dataframe, showcasing the top-performing keywords and their associated labels.

Vitals

  • Script ID : 329
  • Client ID / Customer ID: 1306926109 / 60269815
  • Action Type: Bulk Upload
  • Item Changed: Keyword
  • Output Columns: Account, Campaign, Group, Keyword, Match Type, Conversion Influencers
  • Linked Datasource: M1 Report
  • Reference Datasource: None
  • Owner: Byron Porter (bporter@marinsoftware.com)
  • Created by Byron Porter on 2023-10-04 22:30
  • Last Updated by Byron Porter on 2023-12-06 04:01
> 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_CONV = 'Conv.'
RPT_COL_COST_PER_CONV = 'Cost/Conv. $'
RPT_COL_CONVERSION_INFLUENCERS = 'Conversion Influencers'
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_GROUP = 'Group'
BULK_COL_KEYWORD = 'Keyword'
BULK_COL_MATCH_TYPE = 'Match Type'
BULK_COL_CONVERSION_INFLUENCERS = 'Conversion Influencers'


# 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_CONVERSION_INFLUENCERS] = "Top Performer"

print("outputDf", tableize(outputDf))

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

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