Script 369: Adjust for KW Headroom

Purpose:

The Python script adjusts keyword headroom based on historical performance data to optimize advertising campaigns.

To Elaborate

The script processes advertising performance data to adjust keyword headroom, which is a measure used to optimize budget allocation in advertising campaigns. It filters data to exclude recent entries, aggregates performance metrics such as cost, revenue, and impressions, and calculates the Return on Advertising Spend (ROAS) for each keyword. The script then determines the keyword headroom adjustment based on the ROAS, ensuring that adjustments are only made for keywords with an impression share below a certain threshold. This helps in reallocating budget more effectively to improve campaign performance.

Walking Through the Code

  1. Data Preparation:
    • The script begins by converting the date column in the input DataFrame to a datetime format.
    • It calculates a date two days prior to the current date to filter out recent data.
  2. Data Filtering:
    • It filters the input data to include only records with dates older than two days ago, ensuring that only historical data is used for analysis.
  3. Data Aggregation:
    • The filtered data is grouped by keyword, match type, account, campaign, and group.
    • It aggregates the sum of publisher cost, revenue, impressions, and impression opportunities.
  4. ROAS Calculation:
    • The script calculates the ROAS by dividing the total revenue by the total publisher cost for each group.
  5. Headroom Adjustment:
    • It calculates the keyword headroom adjustment based on the ROAS, setting it to zero if the calculated adjustment is less than or equal to zero.
  6. Final Filtering:
    • The script filters the grouped data to include only those entries with an impression share below 0.95, preparing the final output DataFrame.

Vitals

  • Script ID : 369
  • Client ID / Customer ID: 247648668 / 13095968
  • Action Type: Bulk Upload
  • Item Changed: Keyword
  • Output Columns: Account, Campaign, Group, Keyword, Match Type, KW Headroom Adj
  • Linked Datasource: M1 Report
  • Reference Datasource: None
  • Owner: Jeremy Brown (jbrown@marinsoftware.com)
  • Created by Jeremy Brown on 2023-10-17 14:30
  • Last Updated by marin-scripts@marinsoftware.com on 2024-11-22 05:47
> See it in Action

Python Code

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RPT_COL_CLIENT = 'Client'
RPT_COL_CURRENCY = 'Currency'
RPT_COL_KEYWORD = 'Keyword'
RPT_COL_DATE = 'Date'
RPT_COL_STATUS = 'Status'
RPT_COL_MATCH_TYPE = 'Match Type'
RPT_COL_PUBLISHER = 'Publisher'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_GROUP = 'Group'
RPT_COL_CAMPAIGN_STATUS = 'Campaign Status'
RPT_COL_GROUP_STATUS = 'Group Status'
RPT_COL_REVENUE_CAMPAIGNTYPE = 'Revenue Campaign Type'
RPT_COL_KW_HEADROOMADJ = 'KW Headroom Adj'
RPT_COL_PUB_COST = 'Pub. Cost US$'
RPT_COL_REVENUE = 'Revenue US$'
RPT_COL_ROAS = 'ROAS'
RPT_COL_CONV = 'Conv.'
RPT_COL_IMPR = 'Impr.'
RPT_COL_IMPR_OPS = 'Impression Ops'
RPT_COL_KEYWORD_ID = 'Keyword ID'
RPT_COL_COST_PER_CONV = 'Cost/Conv.'
RPT_COL_CONV_RATE = 'Conv. Rate %'
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_GROUP = 'Group'
BULK_COL_KEYWORD = 'Keyword'
BULK_COL_MATCH_TYPE = 'Match Type'
BULK_COL_KW_HEADROOMADJ = 'KW Headroom Adj'

outputDf[BULK_COL_KW_HEADROOMADJ] = "<<YOUR VALUE>>"




today = datetime.datetime.now(CLIENT_TIMEZONE).date()

# print(tableize(inputDf))
inputDf[RPT_COL_DATE] = pd.to_datetime(inputDf[RPT_COL_DATE], format='%d/%m/%Y')

two_days_ago =  pd.Timestamp(today - datetime.timedelta(days=2))
print(two_days_ago)


filtered_df = inputDf[inputDf[RPT_COL_DATE] < two_days_ago]

print(tableize(filtered_df))

grouped_df = filtered_df.groupby([
    RPT_COL_KEYWORD,  
    RPT_COL_MATCH_TYPE, 
    RPT_COL_ACCOUNT,
     RPT_COL_CAMPAIGN, 
     RPT_COL_GROUP
     ]).agg({RPT_COL_PUB_COST: 'sum', RPT_COL_REVENUE: 'sum'
    , RPT_COL_IMPR: 'sum',  RPT_COL_IMPR_OPS: 'sum' })

# Reset the index to make the grouping columns standalone columns
grouped_df = grouped_df.reset_index()


grouped_df[RPT_COL_ROAS] = grouped_df[RPT_COL_REVENUE] / grouped_df[RPT_COL_PUB_COST]

grouped_df['Impr_Share'] = grouped_df[RPT_COL_IMPR] / grouped_df[RPT_COL_IMPR_OPS]


#grouped_df[RPT_COL_KW_HEADROOMADJ] = int(round(((grouped_df[RPT_COL_ROAS] / 1)-1)*100,0))
grouped_df[RPT_COL_KW_HEADROOMADJ] = grouped_df[RPT_COL_ROAS].apply(lambda x: int(round(((x / 1)-1)*100,0)))

grouped_df.loc[grouped_df[RPT_COL_KW_HEADROOMADJ] <= 0, RPT_COL_KW_HEADROOMADJ] = 0

# new_names = {RPT_COL_ROAS: 'Promotion', RPT_COL_REVENUE: 'Plan Type', RPT_COL_PUB_COST:'Temp for New Keywords'}
# grouped_df.rename(columns=new_names, inplace=True)  # The inplace=True modifies the DataFrame in place

outputDf = grouped_df[grouped_df['Impr_Share'] < 0.95]

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

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