Purpose:

The script identifies and pauses mature advertising campaigns with low Return on Ad Spend (ROAS) over the past 14 days.

To Elaborate

The Python script is designed to manage advertising campaigns by pausing those that are considered mature but have a low Return on Ad Spend (ROAS) over the previous 14 days. The script uses predefined criteria to determine which campaigns should be paused. Specifically, it checks if a campaign is mature and if its ROAS falls within a specified range. If these conditions are met, the campaign’s status is updated to ‘Paused’. This process helps in optimizing the budget allocation by ensuring that funds are not wasted on underperforming campaigns, thereby improving the overall efficiency of advertising efforts.

Walking Through the Code

1. Define Pause Criteria:
   • The script begins by defining the criteria for pausing campaigns. This includes specifying the maturity level of the campaign and the ROAS range. For example, campaigns labeled as ‘Mature’ with a ROAS between 0 and 0.25 are targeted for pausing.
2. Initialize Temporary Status Column:
   • A temporary column is created in the input DataFrame to store the new status of campaigns. 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 maturity level and ROAS fall within the specified range. If they do, the campaign’s status is updated to ‘Paused’ in the temporary column.
4. Identify and Process Changed Campaigns:
   • The script identifies campaigns whose status has changed based on the criteria. If there are any changes, it creates a new DataFrame with the relevant details of these campaigns, including the account, campaign name, and new status. The date when the script paused the campaign is also recorded.
5. Output Results:
   • If there are campaigns with changed statuses, the script outputs the details. If no campaigns meet the criteria, an empty DataFrame is created to indicate that no changes were made.

Vitals

• Script ID : 163
• Client ID / Customer ID: 1306925431 / 60269477
• Action Type: Bulk Upload (Preview)
• Item Changed: Campaign
• Output Columns: Account, Campaign, Script Paused, Status
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: Byron Porter (bporter@marinsoftware.com)
• Created by Byron Porter on 2023-06-06 22:04
• 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 Mature Campaigns
#
#
# Author: Byron Porter
# Date: 2023-06-06
#


# define campaign LOW ROAS PAUSE criteria
# note: MIN values are inclusive; MAX values are non-inclusive
campaign_pause_criteria = [
    # format:
    #  (mature, min roas, max roas),
    ('Mature', 0, .25)
    #('Mature II', 0, 1.2)
]


# 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'


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 (mature, min_roas, max_roas) in campaign_pause_criteria:
	print(f"Applying pause criteria: maturity={mature}, min roas={min_roas}, max roas={max_roas}")

	inputDf.loc[ (inputDf[RPT_COL_CAMP_MATURITY] == mature) & \
				 (inputDf[RPT_COL_ROAS] >= min_roas) & \
				 (inputDf[RPT_COL_ROAS] < max_roas), \
				 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['Script Paused'] = datetime.datetime.now().date()

    print("outputDf", tableize(outputDf))
    
else:
    
  print("Empty outputDf")
  outputDf = pd.DataFrame(columns=[RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN, BULK_COL_STATUS, 'Script Paused'])

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