Script 409: New Launch Updates Round 2

Purpose:

The Python script updates the status, target CPA, and daily budget for new launch campaigns based on specified ROAS criteria.

To Elaborate

The script is designed to manage and update new launch advertising campaigns by adjusting their status, target cost-per-acquisition (tCPA), and daily budget according to predefined return on ad spend (ROAS) criteria. It evaluates each campaign’s performance based on its ROAS and applies specific adjustments to campaigns that fall within certain ROAS ranges. The adjustments include pausing campaigns, activating them, or modifying their budget and tCPA. This process ensures that campaigns are optimized for better performance and resource allocation, aligning with the business’s advertising goals.

Walking Through the Code

  1. Initialization and Setup
    • The script begins by defining several constants representing column names for both reporting and bulk operations.
    • It sets up a parameter current_maturity to track the maturity of campaigns from the input data.
  2. Defining Adjustment Criteria
    • A list of tuples, newlaunch_adj_criteria, is defined to specify the conditions for adjusting campaigns. Each tuple contains minimum and maximum ROAS values, budget and tCPA adjustments, and the desired campaign status.
  3. Temporary Columns for Adjustments
    • Temporary columns are created in the input DataFrame to store new values for campaign status, daily budget, and target CPA.
  4. Applying Adjustment Criteria
    • The script iterates over each set of criteria in newlaunch_adj_criteria.
    • For each criterion, it identifies campaigns that match the specified ROAS range.
    • It calculates new budget and tCPA values for these campaigns and updates the temporary columns with these values and the new status.
  5. Identifying and Processing Changes
    • The script checks for campaigns where any of the temporary values differ from the original values, indicating a change.
    • It creates a new DataFrame, outputDf, containing only the changed campaigns, with updated column names for bulk operations.
  6. Output
    • If there are changes, the script outputs the modified campaigns; otherwise, it outputs an empty DataFrame.

Vitals

  • Script ID : 409
  • Client ID / Customer ID: 1306925431 / 60269477
  • Action Type: Bulk Upload (Preview)
  • Item Changed: Campaign
  • Output Columns: Account, Campaign, Status, Daily Budget, Publisher Target CPA
  • Linked Datasource: M1 Report
  • Reference Datasource: None
  • Owner: Byron Porter (bporter@marinsoftware.com)
  • Created by Byron Porter on 2023-10-24 21:53
  • Last Updated by simon@rainmakeradventures.com on 2023-12-06 04:01
> See it in Action

Python Code

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#
# Update, Status, tCPA, Bid Strategy, and or Campaign Maturity for New Launch Campaigns
#  
#  
#
#
# Author: Byron Porter
# Date: 2023-10-16
#


RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_CAMPAIGN_STATUS = 'Campaign Status'
RPT_COL_DAILY_BUDGET = 'Daily Budget'
RPT_COL_PUBLISHER_BIDSTRATEGY = 'Publisher Bid Strategy'
RPT_COL_PUBLISHER_TARGETCPA = 'Publisher Target CPA'
RPT_COL_PUB_COST = 'Pub. Cost $'
RPT_COL_REVENUE = 'Revenue $'
RPT_COL_ROAS = 'CLICKS ROAS'
RPT_COL_RPC = 'Rev./Click $'
RPT_COL_CLICKS = 'Clicks'
RPT_COL_MATURITY = 'Campaign Maturity'

BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_STATUS = 'Status'
BULK_COL_DAILY_BUDGET = 'Daily Budget'
BULK_COL_PUBLISHER_TARGETCPA = 'Publisher Target CPA'
BULK_COL_PUBLISHER_BIDSTRATEGY = 'Publisher Bid Strategy'
BULK_COL_MATURITY = 'Campaign Maturity'


#set maturity parameter value so that it can be used in tuple
current_maturity = inputDf[RPT_COL_MATURITY]

# Define thesholds/conditions for updating New Launch Campaign values
newlaunch_adj_criteria = [
    # format:
    # (min roas, max roas, budget adj, tCPA adj, status, maturity)
    (.70, .75, 0.0, 0.0, 'PAUSE'),
    (.75, 1, 0.0, .10, 'ACTIVE'),
    (1, 100, .25, 0.0, 'ACTIVE')
]


# temp columns to house new values and make sure all values are cleared out
TMP_STATUS = RPT_COL_CAMPAIGN_STATUS + '_'
inputDf[TMP_STATUS] = np.nan

TMP_BUDGET = RPT_COL_DAILY_BUDGET + '_'
inputDf[TMP_BUDGET] = np.nan

TMP_TARGETCPA = RPT_COL_PUBLISHER_TARGETCPA + '_'
inputDf[TMP_TARGETCPA] = np.nan


# loop through each adj criteria tuple and apply
for (min_roas, max_roas, budget_adj, tcpa_adj, status) in newlaunch_adj_criteria:

    print(f"Applying adj criteria: min roas={min_roas}, max roas={max_roas}, budget adj={budget_adj}, tcpa adj={tcpa_adj}, status ={status}")

    matched_campaigns = (inputDf[RPT_COL_ROAS] >= min_roas) & \
                        (inputDf[RPT_COL_ROAS] < max_roas)
                        

    if sum(matched_campaigns) > 0:
        print("matched campaigns: ", sum(matched_campaigns))

        new_budget = inputDf.loc[matched_campaigns, RPT_COL_DAILY_BUDGET] * (1 + budget_adj)
        inputDf.loc[ matched_campaigns, TMP_BUDGET ] = new_budget

        new_tcpa = inputDf.loc[matched_campaigns, RPT_COL_PUBLISHER_TARGETCPA] * (1 - tcpa_adj)
        inputDf.loc[ matched_campaigns, TMP_TARGETCPA ] = new_tcpa

        inputDf.loc[ matched_campaigns, TMP_STATUS] = status

        print("adj applied", tableize(inputDf.loc[matched_campaigns]))


# find changed campaigns
changed = (inputDf[TMP_BUDGET].notnull() & (inputDf[RPT_COL_DAILY_BUDGET] != inputDf[TMP_BUDGET])) | \
          (inputDf[TMP_TARGETCPA].notnull() & (inputDf[RPT_COL_PUBLISHER_TARGETCPA] != inputDf[TMP_TARGETCPA])) | \
          (inputDf[TMP_STATUS].notnull() & (inputDf[RPT_COL_CAMPAIGN_STATUS] != inputDf[TMP_STATUS])) 

if sum(changed) > 0:

    print("== Campaigns with Changed Adj ==", tableize(inputDf.loc[changed]))

    # only select changed rows
    cols = [RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN, TMP_BUDGET, TMP_TARGETCPA, TMP_STATUS]
    outputDf = inputDf.loc[ changed, cols ].copy() \
                    .rename(columns = { \
                        TMP_BUDGET: BULK_COL_DAILY_BUDGET, \
                        TMP_TARGETCPA: BULK_COL_PUBLISHER_TARGETCPA, \
                        TMP_STATUS: BULK_COL_STATUS \
                    })
else:
    
    print("Empty inputDf")
    outputDf = outputDf.iloc[0:0]

print("outputDf", tableize(outputDf))

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

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