Script 521: Budget Boosts for Bing Campaigns

Purpose:

The Python script automates budget adjustments for Bing campaigns by increasing the budget by 5% for campaigns that meet specific performance criteria over the past 30 days.

To Elaborate

The script is designed to optimize the budget allocation for Bing advertising campaigns by analyzing their performance over a 30-day period. It specifically targets campaigns that are not part of certain excluded categories and evaluates their Return on Advertising Spend (ROAS) and Impression Share lost due to budget constraints. If a campaign’s ROAS is 3.5% or higher and it has lost at least 0.5% of its impression share due to budget limitations, the script increases its daily budget by 5%. This approach aims to enhance the performance of campaigns that are already performing well but are limited by budget constraints, thereby maximizing their potential reach and effectiveness.

Walking Through the Code

  1. Parameter Definition:
    • The script begins by defining key parameters such as the impression share lost to budget threshold (IS_LOST_TO_BUDGET_THRESHOLD), the ROAS threshold (ROAS_THRESHOLD), and the budget increase percentage (BUDGET_INCREASE).
    • It also specifies campaign IDs to exclude from the analysis.
  2. Data Preparation:
    • The script reads the primary data source into a DataFrame (inputDf) and initializes a new column for tracking budget boosts.
    • It groups the data by ‘Publisher’, ‘Account’, and ‘Campaign’ to aggregate necessary metrics like daily budget, revenue, and public cost.
  3. Performance Calculation:
    • The script calculates the ROAS for each campaign by dividing total revenue by total public cost.
    • It flags campaigns that meet the criteria for impression share lost and ROAS, while excluding specified campaign IDs.
  4. Budget Adjustment:
    • For campaigns that qualify based on the defined criteria, the script increases their daily budget by 5%.
    • It records this budget increase in the designated column for tracking purposes.
  5. Output Preparation:
    • The script prepares the final output by selecting campaigns with changed budgets and relevant columns for further analysis or reporting.

Vitals

  • Script ID : 521
  • Client ID / Customer ID: 314475863 / 63970
  • Action Type: Bulk Upload
  • Item Changed: Campaign
  • Output Columns: Account, Campaign, Daily Budget, AUTOMATION - Budget Boost
  • Linked Datasource: M1 Report
  • Reference Datasource: None
  • Owner: dwaidhas@marinsoftware.com (dwaidhas@marinsoftware.com)
  • Created by dwaidhas@marinsoftware.com on 2023-11-13 17:23
  • Last Updated by Scott Fredrickson on 2024-12-06 14:37
> See it in Action

Python Code

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#
## name: Budget Boosts for Bing Campaigns
## author: Dana W.
## created: 2023-11-13
## 

####################### Params ################
# Define your performance threshold and budget increase
IS_LOST_TO_BUDGET_THRESHOLD = 0.005  # 0.5%
ROAS_THRESHOLD = 3.5
BUDGET_INCREASE = 0.05  # 5%

# Campaign IDs to exclude
# Currently two Competitor campaigns and Search | Product
EXCLUDED_CAMPAIGN_IDS = [23496937, 23496938, 23392357]
###############################################

today = datetime.datetime.now(CLIENT_TIMEZONE).date()

# primary data source and columns
inputDf = dataSourceDict["1"]

# output columns and initial values
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_DATE = 'Date'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_PUBLISHER = 'Publisher'
RPT_COL_CAMPAIGN_ID = 'Campaign ID'
RPT_COL_CAMPAIGN_TYPE = 'Campaign Type'
RPT_COL_CAMPAIGN_STATUS = 'Campaign Status'
RPT_COL_DAILY_BUDGET = 'Daily Budget'
RPT_COL_PUB_COST = 'Pub. Cost $'
RPT_COL_COST_PER_CONV = 'Cost/Conv. $'
RPT_COL_ROAS = 'ROAS'
RPT_COL_AVG_CPC = 'Avg. CPC $'
RPT_COL_CONV_RATE = 'Conv. Rate %'
RPT_COL_CTR = 'CTR %'
RPT_COL_CONV = 'Conv.'
RPT_COL_REVENUE = 'Revenue $'
RPT_COL_LOST_IMPRSHAREBUDGET = 'Lost Impr. Share (Budget) %'
RPT_COL_AVG_BID = 'Avg. Bid $'
RPT_COL_HIST_QS = 'Hist. QS'
RPT_COL_IMPR = 'Impr.'
RPT_COL_CLICKS = 'Clicks'
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_DAILY_BUDGET = 'Daily Budget'
BULK_COL_AUTOMATION_BUDGETBOOST = 'AUTOMATION - Budget Boost'

### user code starts here

print("inputDf.shape", inputDf.shape)
print("inputDf.head", tableize(inputDf.head()))

# add new column for output
inputDf[BULK_COL_AUTOMATION_BUDGETBOOST] = np.nan

# Group by Publisher, Account, Campaign
grouped = inputDf.groupby(['Publisher', 'Account', 'Campaign'])

# Calculate new ROAS and get the last 'Lost Impression Share'
campaigns_df = grouped.agg({
    BULK_COL_DAILY_BUDGET:'last',
    RPT_COL_REVENUE:'sum',
    RPT_COL_PUB_COST:'sum',
    RPT_COL_LOST_IMPRSHAREBUDGET:'last',
    RPT_COL_CAMPAIGN_ID:'last',
    }
).reset_index()

campaigns_df[RPT_COL_ROAS] = campaigns_df[RPT_COL_REVENUE] / campaigns_df[RPT_COL_PUB_COST]

print("campaigns_df", tableize(campaigns_df.head()))

# make copy for comparison later
original_df = campaigns_df.copy()

# define business criteria
flag_impression_share = campaigns_df[RPT_COL_LOST_IMPRSHAREBUDGET] >= IS_LOST_TO_BUDGET_THRESHOLD
flag_roas = campaigns_df[RPT_COL_ROAS] >= ROAS_THRESHOLD

# Exclude specified campaign IDs
flag_excluded_campaigns = ~campaigns_df[RPT_COL_CAMPAIGN_ID].isin(EXCLUDED_CAMPAIGN_IDS)

# Combine criteria
qualified = flag_roas & flag_impression_share & flag_excluded_campaigns

print(f"qualified count: {sum(qualified)}")

campaigns_df.loc[qualified, RPT_COL_DAILY_BUDGET] = (1 + BUDGET_INCREASE) * \
                        campaigns_df.loc[qualified, RPT_COL_DAILY_BUDGET]

# Keep track of budget increase
campaigns_df.loc[qualified, BULK_COL_AUTOMATION_BUDGETBOOST] = BUDGET_INCREASE

# tag for debugging
campaigns_df['qualified'] = qualified

outputDf, debugDf = select_changed(campaigns_df, 
                                    original_df,
                                    diff_cols=[RPT_COL_DAILY_BUDGET],
                                    select_cols=[RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN, RPT_COL_DAILY_BUDGET, BULK_COL_AUTOMATION_BUDGETBOOST],
                                    merged_cols=[RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN]
                                    )

print("outputDf.shape", outputDf.shape)
print("outputDf.head")
print(tableize(outputDf.head()))

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

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