Script 361: New Launch Updates Round 3

Purpose:

The Python script updates the status, target cost-per-acquisition (tCPA), bid strategy, and maturity of new launch campaigns based on specified return on ad spend (ROAS) criteria.

To Elaborate

The Python script is designed to manage and update new launch advertising campaigns by adjusting their status, daily budget, target cost-per-acquisition (tCPA), and maturity level based on predefined criteria. The script evaluates each campaign’s performance using the return on ad spend (ROAS) metric. Depending on the ROAS value, the script applies specific adjustments to the campaign’s parameters. For instance, campaigns with a ROAS below a certain threshold may be paused, while those with higher ROAS may have their budgets increased or their tCPA adjusted. The script ensures that campaigns are updated only if there are changes in their parameters, and it outputs the modified campaigns for further processing.

Walking Through the Code

  1. Initialization and Setup
    • The script begins by defining constants for column names used in the input and output dataframes.
    • It sets up a list of tuples, newlaunch_adj_criteria, which contains the criteria for adjusting campaigns. Each tuple specifies minimum and maximum ROAS values, budget and tCPA adjustments, status, and maturity level.
  2. Temporary Columns Creation
    • Temporary columns are created in the input dataframe to store new values for status, budget, tCPA, and maturity. These columns are initialized with NaN to ensure they are cleared before processing.
  3. Campaign Adjustment Loop
    • The script iterates over each set of criteria in newlaunch_adj_criteria.
    • For each criterion, it identifies campaigns that match the ROAS range and applies the specified adjustments to the temporary columns.
    • Adjustments include calculating new budgets and tCPA values, updating the status, and setting the maturity level.
  4. Identifying and Outputting Changes
    • After applying adjustments, the script checks for campaigns where any of the temporary values differ from the original values.
    • It creates an output dataframe containing only the campaigns with changes, renaming the temporary columns to match the output format.
    • If no campaigns have changed, an empty dataframe is prepared for output.

Vitals

  • Script ID : 361
  • Client ID / Customer ID: 1306925431 / 60269477
  • Action Type: Bulk Upload (Preview)
  • Item Changed: Campaign
  • Output Columns: Account, Campaign, Daily Budget, Publisher Target CPA, Status, Campaign Maturity
  • Linked Datasource: M1 Report
  • Reference Datasource: None
  • Owner: Byron Porter (bporter@marinsoftware.com)
  • Created by Byron Porter on 2023-10-16 22:16
  • 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', current_maturity),
    (.75, 1, 0.0, .10, 'ACTIVE', 'Mature'),
    (1, 100, .25, 0.0, 'ACTIVE', 'Mature')
]


# 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_PUB_STRATEGY = RPT_COL_PUBLISHER_BIDSTRATEGY + '_'
#inputDf[TMP_PUB_STRATEGY] = np.nan

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

TMP_MATURITY = RPT_COL_MATURITY + '_'
inputDf[TMP_MATURITY] = np.nan

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


# Convert the 'Date' column to datetime format
#inputDf['Date'] = pd.to_datetime(inputDf['Date'], format='%m/%d/%y')


# loop through each adj criteria tuple and apply
for (min_roas, max_roas, budget_adj, tcpa_adj, status, maturity) 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}, maturity={maturity}")

    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

        inputDf.loc[ matched_campaigns, TMP_MATURITY] = maturity

        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])) | \
          (inputDf[TMP_MATURITY].notnull() & (inputDf[RPT_COL_MATURITY] != inputDf[TMP_MATURITY]))

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, TMP_MATURITY]
    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, \
                        TMP_MATURITY: BULK_COL_MATURITY \
                    })
    #nodateDf[RPT_COL_LAST_TCPA_BUDGET_UPDATE] = datetime.date.today()
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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