Purpose:

The Python script automates the process of pausing new advertising campaigns with low Return on Advertising Spend (ROAS) based on predefined criteria.

To Elaborate

The script is designed to manage advertising campaigns by automatically pausing those that are new and have a low Return on Advertising Spend (ROAS) over the previous 30 days. It uses a set of predefined criteria to determine which campaigns should be paused. These criteria are based on the maturity stage of the campaign and specific ROAS thresholds. The script evaluates each campaign against these criteria and updates their status to ‘Paused’ if they meet the conditions. This process helps in optimizing the advertising budget by halting campaigns that are not performing well, thereby allowing for better allocation of resources to more successful campaigns.

Walking Through the Code

1. Define Pause Criteria
   • The script begins by defining the criteria for pausing campaigns. These criteria include the campaign’s maturity stage and specific ROAS thresholds. Each entry in the campaign_pause_criteria list specifies a maturity stage and a ROAS range.
2. Initialize Temporary Status Column
   • A temporary column is created in the input DataFrame to store the new status of each campaign. This column is initially set to empty.
3. Apply Pause Criteria
   • The script iterates over each set of pause criteria. For each campaign, it checks if the campaign’s maturity and ROAS fall within the specified range. If they do, the campaign’s status is updated to ‘Paused’ in the temporary status column.
4. Identify and Process Changed Campaigns
   • The script identifies campaigns whose status has changed to ‘Paused’. If there are any such campaigns, it creates a new DataFrame containing only these campaigns and updates their status. The date of the status change is also recorded.
5. Output Results
   • If there are campaigns with changed statuses, the script outputs the relevant information. If no campaigns meet the criteria, an empty DataFrame is prepared for output.

Vitals

• Script ID : 141
• Client ID / Customer ID: 1306925431 / 60269477
• Action Type: Bulk Upload (Preview)
• Item Changed: Campaign
• Output Columns: Account, Campaign, Status, Script Paused
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: Michael Huang (mhuang@marinsoftware.com)
• Created by Michael Huang on 2023-05-25 20:20
• Last Updated by alejandro@rainmakeradventures.com on 2024-01-26 18:55

> See it in Action

Python Code

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#
# Pause Low ROAS New Campaigns
#
#
# Author: Michael S. Huang
# Date: 2023-05-26
#


# define campaign LOW ROAS PAUSE criteria
# note: MIN values are inclusive; MAX values are non-inclusive
campaign_pause_criteria = [
    # format:
    #  (min spend, max spend, min roas, max roas),
    ('New - Launch', 0, .4),
    ('New - Round 2', 0, .2999),
    ('New - Round 3', 0, .5),
    ('New - Round 4', 0, .5),
    ('New - Round 5', 0, .5)
]

# define column parameters
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_STRATEGY = 'Strategy'
RPT_COL_CAMPAIGN_STATUS = 'Campaign Status'
RPT_COL_CAMP_MATURITY = 'Campaign Maturity'
RPT_COL_ROAS = 'CLICKS ROAS'
RPT_COL_PUBLISHER_TARGETCPA = 'Publisher Target CPA'
RPT_COL_DAILY_BUDGET = 'Daily Budget'
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_STATUS = 'Status'
BULK_COL_SCRIPT_PAUSED = 'Script Paused'

VAL_STATUS_PAUSED = 'Paused'


# create temp column to store new status and default to empty
TMP_STATUS = RPT_COL_CAMPAIGN_STATUS + '_'
inputDf[TMP_STATUS] = np.nan


# loop through each PAUSE criteria and apply
for (new_round, min_roas, max_roas) in campaign_pause_criteria:
	print(f"Applying pause criteria: new round={new_round}, min roas={min_roas}, max roas={max_roas}")

	inputDf.loc[ (inputDf[RPT_COL_CAMP_MATURITY] == new_round) & \
				 (inputDf[RPT_COL_ROAS] >= min_roas) & \
				 (inputDf[RPT_COL_ROAS] < max_roas) & \
                 (inputDf[RPT_COL_ACCOUNT] == 'ExplorAds Google'), \
				 TMP_STATUS \
			   ] = VAL_STATUS_PAUSED

# find changed campaigns
changed = inputDf[TMP_STATUS].notnull() & (inputDf[RPT_COL_CAMPAIGN_STATUS] != inputDf[TMP_STATUS])

if sum(changed) > 0:

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

    # only select changed rows
    cols = [RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN, TMP_STATUS]
    outputDf = inputDf.loc[ changed, cols ].copy() \
                    .rename(columns = { \
                        TMP_STATUS: BULK_COL_STATUS \
                    })

    outputDf[BULK_COL_SCRIPT_PAUSED] = datetime.datetime.now(CLIENT_TIMEZONE).date()
    
    print("outputDf", tableize(outputDf))
    
else:
    
  print("Empty outputDf")
  outputDf = pd.DataFrame(columns=[BULK_COL_ACCOUNT, BULK_COL_CAMPAIGN, BULK_COL_STATUS, BULK_COL_SCRIPT_PAUSED])

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