Script 153: Mature Campaigns Adj Google tCPA and Budget
Purpose
The 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 Python script is designed to optimize the performance of Google advertising campaigns that are labeled as ‘Mature’. It does this by adjusting the target Cost Per Acquisition (tCPA) and daily budget based on the Return on Advertising Spend (ROAS) observed over the past 14 days. The script uses predefined criteria to determine how much to adjust the tCPA and budget, aiming to improve campaign efficiency. Campaigns are categorized based on their ROAS into different criteria sets, and adjustments are applied accordingly. The script handles campaigns with and without previous updates separately, ensuring that changes are only made if a sufficient amount of time has passed since the last update. This structured approach helps in maintaining a balance between cost and performance, ensuring that campaigns are neither over nor underfunded.
Walking Through the Code
- Define Adjustment Criteria:
- The script begins by defining several sets of criteria for adjusting campaigns. Each set specifies a range of ROAS values and corresponding adjustments for tCPA and budget.
- Initialize DataFrames:
- Temporary columns are created to store new budget and tCPA values. DataFrames are initialized to separate campaigns with and without previous updates.
- Apply Adjustments for Campaigns with No Previous Updates:
- The script loops through each set of criteria, applying adjustments to campaigns that fall within the specified ROAS range and have no previous updates. Adjustments are calculated and stored in temporary columns.
- Identify and Process Changed Campaigns:
- Campaigns with changes in budget or tCPA are identified. These changes are then recorded in a new DataFrame, with the current date noted as the last update date.
- Apply Adjustments for Campaigns with Previous Updates:
- For campaigns with previous updates, the script calculates the number of days since the last update. Adjustments are applied only if more than 14 days have passed, ensuring that changes are not too frequent.
- Merge and Finalize Adjustments:
- The script merges all non-empty DataFrames containing adjusted campaigns. It ensures that the minimum tCPA is set to 2.50, and the final adjustments are prepared for output.
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 2024-11-27 06:58:46 GMT