Script 371: Adjust for KW Headroom

Purpose:

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

To Elaborate

The script is designed to optimize advertising campaigns by adjusting the keyword headroom, which is a measure of potential budget allocation for keywords. It processes historical performance data to calculate the Return on Advertising Spend (ROAS) and impression share for each keyword. The script filters out data older than two days and groups it by keyword, match type, account, campaign, and group. It then calculates the ROAS and impression share for each group. Based on these calculations, the script adjusts the keyword headroom, ensuring that it is not negative. Finally, it filters the results to include only those with an impression share below 95%, indicating potential for increased visibility and performance.

Walking Through the Code

  1. Data Preparation:
    • The script begins by filtering the input data to exclude records older than two days. This ensures that only recent data is considered for analysis.
  2. Data Grouping and Aggregation:
    • The filtered data is grouped by keyword, match type, account, campaign, and group. For each group, the script calculates the total publisher cost, revenue, impressions, and impression opportunities.
  3. ROAS and Impression Share Calculation:
    • The script computes the ROAS by dividing the total revenue by the total publisher cost. It also calculates the impression share by dividing the total impressions by the total impression opportunities.
  4. Keyword Headroom Adjustment:
    • The keyword headroom adjustment is calculated based on the ROAS. If the calculated adjustment is less than or equal to zero, it is set to zero to avoid negative adjustments.
  5. Filtering for Potential Optimization:
    • The script filters the grouped data to include only those entries with an impression share below 95%, indicating that these keywords have room for increased budget allocation and potential performance improvement.

Vitals

  • Script ID : 371
  • Client ID / Customer ID: 1306912245 / 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 15:31
  • Last Updated by Tom Lowes on 2023-12-06 04:01
> 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 $'
RPT_COL_REVENUE = 'Revenue $'
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))


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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