Script 153: Mature Campaigns Adj Google tCPA and Budget
Purpose:
The Python script adjusts the target CPA and daily budget of Google campaigns labeled as ‘Mature’ based on their ROAS over the previous 14 days.
To Elaborate
The script is designed to optimize Google advertising campaigns that are labeled as ‘Mature’ by adjusting their target Cost Per Acquisition (tCPA) and daily budget. The adjustments are based on the Return on Advertising Spend (ROAS) over the past 14 days. The script categorizes campaigns into different criteria based on their ROAS values and applies specific adjustments to the tCPA and budget accordingly. Campaigns with no previous updates and those with updates older than 14 days are considered for adjustments. The script ensures that the tCPA does not fall below a minimum threshold of 2.50. This process helps in maintaining efficient budget allocation and performance optimization for mature campaigns.
Walking Through the Code
- Define Adjustment Criteria:
- The script begins by defining several sets of criteria for adjusting campaigns based on their ROAS. Each set includes minimum and maximum ROAS values, and corresponding adjustments for tCPA and budget.
- Initialize Temporary Columns:
- Temporary columns are created in the input DataFrame to store new budget and tCPA values. These columns are initially set to NaN.
- Identify Campaigns for Adjustment:
- The script checks for campaigns with no previous tCPA or budget updates and separates them into different DataFrames for processing.
- Apply Adjustments for No Previous Updates:
- For campaigns with no previous updates, the script loops through each set of criteria, identifies matching campaigns, and applies the specified adjustments to the budget and tCPA.
- Check and Record Changes:
- After applying adjustments, the script checks for any changes in the campaigns’ budget or tCPA. If changes are detected, the updated campaigns are recorded in a new DataFrame with the current date.
- Apply Adjustments for Previous Updates:
- For campaigns with previous updates, the script calculates the days since the last update and applies adjustments only if more than 14 days have passed. The same process of checking and recording changes is followed.
- Merge and Finalize Adjustments:
- The script merges all non-empty DataFrames containing updated campaigns and ensures that the tCPA does not fall below 2.50. The final adjustments are printed for review.
Vitals
- Script ID : 153
- Client ID / Customer ID: 1306925431 / 60269477
- Action Type: Bulk Upload (Preview)
- Item Changed: Campaign
- Output Columns: Account, Campaign, Daily Budget, Publisher Target CPA, Date of Last tCPA / Daily Budget Adj.
- Linked Datasource: M1 Report
- Reference Datasource: None
- Owner: Byron Porter (bporter@marinsoftware.com)
- Created by Byron Porter on 2023-05-30 20:38
- Last Updated by alejandro@rainmakeradventures.com on 2024-01-26 18:56
> See it in Action
Python Code
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#
# Publisher Budget and Target CPA Adjustment - Mature Camapaigns
#
#
# Author: Byron Porter
# Date: 2023-05-30
#
# define criteria for campaign Strategy Target and Daily Budget adjustments
# note: MIN values are inclusive; MAX values are non-inclusive
campaign_adj_criteria = [
# format:
# (min roas, max roas, tCPA adj, budget adj),
(0.75, 1.00, -0.10, -0.25),
(1.00, 1.25, 0.0, 0.25),
(1.25, 1.50, 0.0, 0.5),
(1.50, 2.00, 0.0, 0.75),
(2.00, 999999.0, 0.0, 1.0)
]
campaign_adj_criteria_2 = [
# format:
# (min roas, max roas, tCPA adj, budget adj),
(0.50, 0.75, -0.20, 25.00)
]
campaign_adj_criteria_3 = [
# format:
# (min roas, max roas, tCPA adj, budget adj),
(0.25, 0.50, 2.50, 15.00)
]
# define column parameters
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_STRATEGY = 'Strategy'
RPT_COL_CAMPAIGN_STATUS = 'Campaign Status'
RPT_COL_DAILY_BUDGET = 'Daily Budget'
RPT_COL_ROAS = 'CLICKS ROAS'
RPT_COL_PUBLISHER_TARGETCPA = 'Publisher Target CPA'
RPT_COL_CAMP_MATURITY = 'Campaign Maturity'
RPT_COL_LAST_TCPA_BUDGET_UPDATE = 'Date of Last tCPA / Daily Budget Adj.'
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_DAILY_BUDGET = 'Daily Budget'
BULK_COL_PUBLISHER_TARGETCPA = 'Publisher Target CPA'
# Assign current date to a parameter
today = datetime.datetime.now()
# create temp column to store new values and default to empty
TMP_BUDGET = RPT_COL_DAILY_BUDGET + '_'
inputDf[TMP_BUDGET] = np.nan
TMP_TARGETCPA = RPT_COL_PUBLISHER_TARGETCPA + '_'
inputDf[TMP_TARGETCPA] = np.nan
# Check if RPT_COL_LAST_TCPA_BUDGET_UPDATE is blank
null_check = inputDf[RPT_COL_LAST_TCPA_BUDGET_UPDATE].isnull()
# Use check to create DataFrame for campaigns with no Last Updated value and assign current date
blankDf = inputDf.loc[null_check, :].copy()
blank2Df = inputDf.loc[null_check, :].copy()
blank3Df = inputDf.loc[null_check, :].copy()
nonblankDf = inputDf.loc[~null_check, :].copy()
nonblank2Df = inputDf.loc[~null_check, :].copy()
nonblank3Df = inputDf.loc[~null_check, :].copy()
nodateDf = pd.DataFrame()
nodate2Df = pd.DataFrame()
nodate3Df = pd.DataFrame()
# Define empty data frame for campaigns with changed RPT_COL_LAST_TCPA_BUDGET_UPDATE values
dateDf = pd.DataFrame()
date2Df = pd.DataFrame()
date3Df = pd.DataFrame()
############################# First Campaign Criteria Updates *No Previous Updates* #############################
# loop through each ROAS criteria and apply
for (min_roas, max_roas, tcpa_adj, budget_adj) in campaign_adj_criteria:
print(f"Applying adj criteria: min roas={min_roas}, max roas={max_roas}, tcpa adj={tcpa_adj}, budget adj={budget_adj}")
matched_campaigns = (blankDf[RPT_COL_ROAS] >= min_roas) & \
(blankDf[RPT_COL_ROAS] < max_roas)
if sum(matched_campaigns) > 0:
print("matched campaigns: ", sum(matched_campaigns))
new_budget = blankDf.loc[matched_campaigns, RPT_COL_DAILY_BUDGET] * (1 + budget_adj)
# print("new_budget", new_budget)
blankDf.loc[ matched_campaigns, TMP_BUDGET ] = new_budget
new_tcpa = blankDf.loc[matched_campaigns, RPT_COL_PUBLISHER_TARGETCPA] * (1 + tcpa_adj)
blankDf.loc[ matched_campaigns, TMP_TARGETCPA ] = new_tcpa
print("adj applied", tableize(blankDf.loc[matched_campaigns]))
# find changed campaigns
changed = (blankDf[TMP_BUDGET].notnull() & (blankDf[RPT_COL_DAILY_BUDGET] != blankDf[TMP_BUDGET])) | \
(blankDf[TMP_TARGETCPA].notnull() & (blankDf[RPT_COL_PUBLISHER_TARGETCPA] != blankDf[TMP_TARGETCPA]))
if sum(changed) > 0:
print("== Campaigns with Changed Adj ==", tableize(blankDf.loc[changed]))
# only select changed rows
cols = [RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN, TMP_BUDGET, TMP_TARGETCPA, RPT_COL_LAST_TCPA_BUDGET_UPDATE]
nodateDf = blankDf.loc[ changed, cols ].copy() \
.rename(columns = { \
TMP_BUDGET: BULK_COL_DAILY_BUDGET, \
TMP_TARGETCPA: BULK_COL_PUBLISHER_TARGETCPA \
})
nodateDf[RPT_COL_LAST_TCPA_BUDGET_UPDATE] = datetime.date.today()
else:
print("Empty nodateDf")
nodateDf = nodateDf.iloc[0:0]
############################# Second Campaign Criteria Updates *No Previous Updates* #############################
# loop through other ORAS criteria and apply
for (min_roas, max_roas, tcpa_adj, budget_adj) in campaign_adj_criteria_2:
print(f"Applying adj criteria: min roas={min_roas}, max roas={max_roas}, tcpa adj={tcpa_adj}, budget adj={budget_adj}")
matched_campaigns = (blank2Df[RPT_COL_ROAS] >= min_roas) & \
(blank2Df[RPT_COL_ROAS] < max_roas)
if sum(matched_campaigns) > 0:
print("matched campaigns: ", sum(matched_campaigns))
#new_budget = blankDf.loc[matched_campaigns, RPT_COL_DAILY_BUDGET] - budget_adj
# print("new_budget", new_budget)
blank2Df.loc[ matched_campaigns, TMP_BUDGET ] = budget_adj
new_tcpa = blank2Df.loc[matched_campaigns, RPT_COL_PUBLISHER_TARGETCPA] * (1 + tcpa_adj)
blank2Df.loc[ matched_campaigns, TMP_TARGETCPA ] = new_tcpa
print("adj applied", tableize(blank2Df.loc[matched_campaigns]))
# find changed campaigns
changed = (blank2Df[TMP_BUDGET].notnull() & (blank2Df[RPT_COL_DAILY_BUDGET] != blank2Df[TMP_BUDGET])) | \
(blank2Df[TMP_TARGETCPA].notnull() & (blank2Df[RPT_COL_PUBLISHER_TARGETCPA] != blank2Df[TMP_TARGETCPA]))
if sum(changed) > 0:
print("== Campaigns with Changed Adj ==", tableize(blank2Df.loc[changed]))
# only select changed rows
cols = [RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN, TMP_BUDGET, TMP_TARGETCPA, RPT_COL_LAST_TCPA_BUDGET_UPDATE]
nodate2Df = blank2Df.loc[ changed, cols ].copy() \
.rename(columns = { \
TMP_BUDGET: BULK_COL_DAILY_BUDGET, \
TMP_TARGETCPA: BULK_COL_PUBLISHER_TARGETCPA \
})
nodate2Df[RPT_COL_LAST_TCPA_BUDGET_UPDATE] = datetime.date.today()
else:
print("Empty nodate2Df")
nodate2Df = nodate2Df.iloc[0:0]
############################# Third Campaign Criteria Updates *No Previous Updates* #############################
for (min_roas, max_roas, tcpa_adj, budget_adj) in campaign_adj_criteria_3:
print(f"Applying adj criteria: min roas={min_roas}, max roas={max_roas}, tcpa adj={tcpa_adj}, budget adj={budget_adj}")
matched_campaigns = (blank3Df[RPT_COL_ROAS] >= min_roas) & \
(blank3Df[RPT_COL_ROAS] < max_roas)
if sum(matched_campaigns) > 0:
print("matched campaigns: ", sum(matched_campaigns))
#new_budget = blankDf.loc[matched_campaigns, RPT_COL_DAILY_BUDGET] - budget_adj
# print("new_budget", new_budget)
blank3Df.loc[ matched_campaigns, TMP_BUDGET ] = budget_adj
blank3Df.loc[ matched_campaigns, TMP_TARGETCPA ] = tcpa_adj
print("adj applied", tableize(blank3Df.loc[matched_campaigns]))
# find changed campaigns
changed = (blank3Df[TMP_BUDGET].notnull() & (blank3Df[RPT_COL_DAILY_BUDGET] != blank3Df[TMP_BUDGET])) | \
(blank3Df[TMP_TARGETCPA].notnull() & (blank3Df[RPT_COL_PUBLISHER_TARGETCPA] != blank3Df[TMP_TARGETCPA]))
if sum(changed) > 0:
print("== Campaigns with Changed Adj ==", tableize(blank3Df.loc[changed]))
# only select changed rows
cols = [RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN, TMP_BUDGET, TMP_TARGETCPA, RPT_COL_LAST_TCPA_BUDGET_UPDATE]
nodate3Df = blank3Df.loc[ changed, cols ].copy() \
.rename(columns = { \
TMP_BUDGET: BULK_COL_DAILY_BUDGET, \
TMP_TARGETCPA: BULK_COL_PUBLISHER_TARGETCPA \
})
nodate3Df[RPT_COL_LAST_TCPA_BUDGET_UPDATE] = datetime.date.today()
else:
print("Empty nodate3Df")
nodate3Df = nodate3Df.iloc[0:0]
############################# First Campaign Criteria Updates *Previous Updates* #############################
# create temp columm to store the days since last update
nonblankDf['ConvertedDate'] = pd.to_datetime(nonblankDf[RPT_COL_LAST_TCPA_BUDGET_UPDATE], format="%Y-%m-%d")
nonblankDf['DaysSinceUpdate'] = (today - nonblankDf['ConvertedDate']).dt.days
nonblank2Df['ConvertedDate'] = pd.to_datetime(nonblankDf[RPT_COL_LAST_TCPA_BUDGET_UPDATE], format="%Y-%m-%d")
nonblank2Df['DaysSinceUpdate'] = (today - nonblankDf['ConvertedDate']).dt.days
nonblank3Df['ConvertedDate'] = pd.to_datetime(nonblankDf[RPT_COL_LAST_TCPA_BUDGET_UPDATE], format="%Y-%m-%d")
nonblank3Df['DaysSinceUpdate'] = (today - nonblankDf['ConvertedDate']).dt.days
# loop through each ROAS criteria, check for last update, and apply
for (min_roas, max_roas, tcpa_adj, budget_adj) in campaign_adj_criteria:
print(f"Applying adj criteria: min roas={min_roas}, max roas={max_roas}, tcpa adj={tcpa_adj}, budget adj={budget_adj}")
matched_campaigns = (nonblankDf[RPT_COL_ROAS] >= min_roas) & \
(nonblankDf[RPT_COL_ROAS] < max_roas) & \
(nonblankDf['DaysSinceUpdate'] > 14)
if sum(matched_campaigns) > 0:
print("matched campaigns: ", sum(matched_campaigns))
new_budget = nonblankDf.loc[matched_campaigns, RPT_COL_DAILY_BUDGET] * (1 + budget_adj)
# print("new_budget", new_budget)
nonblankDf.loc[ matched_campaigns, TMP_BUDGET ] = new_budget
new_tcpa = nonblankDf.loc[matched_campaigns, RPT_COL_PUBLISHER_TARGETCPA] * (1 + tcpa_adj)
nonblankDf.loc[ matched_campaigns, TMP_TARGETCPA ] = new_tcpa
print("adj applied", tableize(nonblankDf.loc[matched_campaigns]))
# find changed campaigns
changed = (nonblankDf[TMP_BUDGET].notnull() & (nonblankDf[RPT_COL_DAILY_BUDGET] != nonblankDf[TMP_BUDGET])) | \
(nonblankDf[TMP_TARGETCPA].notnull() & (nonblankDf[RPT_COL_PUBLISHER_TARGETCPA] != nonblankDf[TMP_TARGETCPA]))
if sum(changed) > 0:
print("== Campaigns with Changed Adj ==", tableize(nonblankDf.loc[changed]))
# only select changed rows
cols = [RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN, TMP_BUDGET, TMP_TARGETCPA, RPT_COL_LAST_TCPA_BUDGET_UPDATE]
dateDf = nonblankDf.loc[ changed, cols ].copy() \
.rename(columns = { \
TMP_BUDGET: BULK_COL_DAILY_BUDGET, \
TMP_TARGETCPA: BULK_COL_PUBLISHER_TARGETCPA \
})
dateDf[RPT_COL_LAST_TCPA_BUDGET_UPDATE] = datetime.date.today()
else:
print("Empty dateDf")
dateDf = dateDf.iloc[0:0]
############################# Second Campaign Criteria Updates *Previous Updates* #############################
# loop through each ROAS other criteria, check for last update, and apply
for (min_roas, max_roas, tcpa_adj, budget_adj) in campaign_adj_criteria_2:
print(f"Applying adj criteria: min roas={min_roas}, max roas={max_roas}, tcpa adj={tcpa_adj}, budget adj={budget_adj}")
matched_campaigns = (nonblank2Df[RPT_COL_ROAS] >= min_roas) & \
(nonblank2Df[RPT_COL_ROAS] < max_roas) & \
(nonblank2Df['DaysSinceUpdate'] > 14)
if sum(matched_campaigns) > 0:
print("matched campaigns: ", sum(matched_campaigns))
#new_budget = nonblank2Df.loc[matched_campaigns, RPT_COL_DAILY_BUDGET] - budget_adj
# print("new_budget", new_budget)
nonblank2Df.loc[ matched_campaigns, TMP_BUDGET ] = budget_adj
new_tcpa = nonblank2Df.loc[matched_campaigns, RPT_COL_PUBLISHER_TARGETCPA] * (1 + tcpa_adj)
nonblank2Df.loc[ matched_campaigns, TMP_TARGETCPA ] = new_tcpa
print("adj applied", tableize(nonblank2Df.loc[matched_campaigns]))
# find changed campaigns
changed = (nonblank2Df[TMP_BUDGET].notnull() & (nonblank2Df[RPT_COL_DAILY_BUDGET] != nonblank2Df[TMP_BUDGET])) | \
(nonblank2Df[TMP_TARGETCPA].notnull() & (nonblank2Df[RPT_COL_PUBLISHER_TARGETCPA] != nonblank2Df[TMP_TARGETCPA]))
if sum(changed) > 0:
print("== Campaigns with Changed Adj ==", tableize(nonblank2Df.loc[changed]))
# only select changed rows
cols = [RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN, TMP_BUDGET, TMP_TARGETCPA, RPT_COL_LAST_TCPA_BUDGET_UPDATE]
date2Df = nonblank2Df.loc[ changed, cols ].copy() \
.rename(columns = { \
TMP_BUDGET: BULK_COL_DAILY_BUDGET, \
TMP_TARGETCPA: BULK_COL_PUBLISHER_TARGETCPA \
})
date2Df[RPT_COL_LAST_TCPA_BUDGET_UPDATE] = datetime.date.today()
else:
print("Empty date2Df")
dateotherDf = date2Df.iloc[0:0]
############################# Third Campaign Criteria Updates *Previous Updates* #############################
for (min_roas, max_roas, tcpa_adj, budget_adj) in campaign_adj_criteria_3:
print(f"Applying adj criteria: min roas={min_roas}, max roas={max_roas}, tcpa adj={tcpa_adj}, budget adj={budget_adj}")
matched_campaigns = (nonblank3Df[RPT_COL_ROAS] >= min_roas) & \
(nonblank3Df[RPT_COL_ROAS] < max_roas) & \
(nonblank2Df['DaysSinceUpdate'] > 14)
if sum(matched_campaigns) > 0:
print("matched campaigns: ", sum(matched_campaigns))
#new_budget = blankDf.loc[matched_campaigns, RPT_COL_DAILY_BUDGET] - budget_adj
# print("new_budget", new_budget)
nonblank3Df.loc[ matched_campaigns, TMP_BUDGET ] = budget_adj
nonblank3Df.loc[ matched_campaigns, TMP_TARGETCPA ] = tcpa_adj
print("adj applied", tableize(nonblank3Df.loc[matched_campaigns]))
# find changed campaigns
changed = (nonblank3Df[TMP_BUDGET].notnull() & (nonblank3Df[RPT_COL_DAILY_BUDGET] != nonblank3Df[TMP_BUDGET])) | \
(nonblank3Df[TMP_TARGETCPA].notnull() & (nonblank3Df[RPT_COL_PUBLISHER_TARGETCPA] != nonblank3Df[TMP_TARGETCPA]))
if sum(changed) > 0:
print("== Campaigns with Changed Adj ==", tableize(nonblank3Df.loc[changed]))
# only select changed rows
cols = [RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN, TMP_BUDGET, TMP_TARGETCPA, RPT_COL_LAST_TCPA_BUDGET_UPDATE]
date3Df = blank3Df.loc[ changed, cols ].copy() \
.rename(columns = { \
TMP_BUDGET: BULK_COL_DAILY_BUDGET, \
TMP_TARGETCPA: BULK_COL_PUBLISHER_TARGETCPA \
})
date3Df[RPT_COL_LAST_TCPA_BUDGET_UPDATE] = datetime.date.today()
else:
print("Empty date3Df")
date3Df = date3Df.iloc[0:0]
# Merge defined data, exlcuding those that are empty, and print the resulting outputDf
dataframes = [nodateDf, dateDf, nodate2Df, date2Df, nodate3Df, date3Df]
non_empty_dataframes = [df for df in dataframes if not df.empty]
if non_empty_dataframes:
outputDf = pd.concat(non_empty_dataframes)
else:
print("Empty outputDf")
outputDf = outputDf.iloc[0:0]
if (outputDf[BULK_COL_PUBLISHER_TARGETCPA] < 2.50).any():
outputDf.loc[outputDf[BULK_COL_PUBLISHER_TARGETCPA] < 2.50, BULK_COL_PUBLISHER_TARGETCPA] = 2.50
print("outputDf:", tableize(outputDf))
else:
print("outputDf", tableize(outputDf))
Post generated on 2025-03-11 01:25:51 GMT