Script 899: CC SBA Budget Pacing

Purpose:

The Python script optimizes campaign budget allocation to minimize lost impression share due to budget constraints by considering historical spend, remaining budget, and remaining weekdays in the month.

To Elaborate

The script addresses the challenge of efficiently allocating campaign budgets to minimize lost impression share due to budget constraints. It takes into account various factors such as the remaining budget for each strategy group, the number of weekdays left in the month, historical spending patterns, and the potential for future spending. The script ensures that only active campaigns with recent spending are considered for budget allocation. It calculates the full potential spend for each campaign, caps it to avoid excessive spending, and allocates the remaining budget proportionally. The script also calculates a recommended daily budget for each campaign, ensuring it meets a minimum threshold, and adjusts the budget allocation based on pacing compliance to ensure optimal budget utilization throughout the month.

Walking Through the Code

Configuration and Setup
- The script begins by defining a configurable parameter, MINIMUM_DAILY_BUDGET, which sets the minimum daily budget for campaigns.
- It sets up necessary column names and prepares the input data by coercing date types and setting indices for grouping.
Full-Potential Spend Calculation
- The script calculates the full potential spend by adjusting historical spend based on lost impression share due to budget constraints.
- It groups data by strategy and aggregates necessary metrics, such as clicks, conversions, and publisher costs.
Campaign Filtering
- It filters out inactive campaigns and those with expired program dates to focus on active campaigns with recent spending.
Budget Allocation Ratio Calculation
- The script caps the full potential spend at twice the historical spend to prevent excessive allocation.
- It calculates the budget allocation ratio for each campaign within a strategy group.
Remaining Budget Calculation
- For each strategy group, the script calculates the remaining budget by subtracting the month-to-date spend from the monthly budget.
Daily Budget Calculation
- It calculates the recommended daily budget by dividing the allocated budget by the remaining business days in the month.
- The script ensures the daily budget meets the minimum threshold and adjusts accordingly.
Traffic Budget Adjustment
- The script identifies campaigns marked for traffic and adjusts their daily budgets accordingly, setting non-traffic campaigns’ budgets to NaN.
Pacing Compliance Calculation
- It calculates pacing compliance to ensure the budget is utilized optimally throughout the month, aiming for 100% compliance.
Output Generation
- The script identifies changes in budget recommendations and generates an output DataFrame with updated budget allocations for campaigns that require changes.

Vitals

Script ID : 899
Client ID / Customer ID: 1306923845 / 60269271
Action Type: Bulk Upload
Item Changed: Campaign
Output Columns: Account, Campaign, Daily Budget, CC SBA Allocation, CC SBA Budget Pacing, CC SBA Rec. Daily Budget
Linked Datasource: M1 Report
Reference Datasource: None
Owner: emerryfield@marinsoftware.com (emerryfield@marinsoftware.com)
Created by emerryfield@marinsoftware.com on 2024-04-02 23:28
Last Updated by ascott@marinsoftware.com on 2024-04-03 18:04

> See it in Action

Python Code

#
# SBA Campaign Budget Pacing - Minimize Lost IS (Budget)
#
# Allocates according to:
# * Remaining budget for each Strategy Group
# * Remaining weekdays in month
# * Historical spend and spend potential
# * Campaigns with spend in lookback period
# * Minimum daily budget
#
# Author: Michael S. Huang, Adam Scott
#
# Created: 2023-09-30
# Updated: 2024-02-10
#

##### Configurable Param #####

MINIMUM_DAILY_BUDGET = 10

##############################


RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_DATE = 'Date'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_PUBLISHER_NAME = 'Publisher Name'
RPT_COL_STRATEGY = 'Strategy'
RPT_COL_DAILY_BUDGET = 'Daily Budget'
RPT_COL_CAMPAIGN_STATUS = 'Campaign Status'
RPT_COL_PUB_COST = 'Pub. Cost $'
RPT_COL_CLICKS = 'Clicks'
RPT_COL_CONV = 'Conv.'
RPT_COL_IMPR_SHARE = 'Impr. share %'
RPT_COL_LOST_IMPR_SHARE_BUDGET = 'Lost Impr. Share (Budget) %'
RPT_COL_LOST_IMPR_SHARE_RANK = 'Lost Impr. Share (Rank) %'
RPT_COL_CC_SBA_STRATEGY = 'CC SBA Strategy'
RPT_COL_CC_SBA_CAMPAIGN_BUDGET = 'CC SBA Campaign Budget'
RPT_COL_CC_SBA_ALLOCATION = 'CC SBA Allocation'
RPT_COL_CC_SBA_REC_DAILY_BUDGET = 'CC SBA Rec. Daily Budget'
RPT_COL_CC_SBA_BUDGET_PACING = 'CC SBA Budget Pacing'
RPT_COL_SBA_TRAFFIC = 'SBA Traffic'
RPT_COL_PROGRAM_END_Date = 'Program End Date'

BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_DAILY_BUDGET = 'Daily Budget'
BULK_COL_CC_SBA_ALLOCATION = 'CC SBA Allocation'
BULK_COL_CC_SBA_BUDGET_PACING = 'CC SBA Budget Pacing'
BULK_COL_REC_DAILY_BUDGET_CC_SBA = 'CC SBA Rec. Daily Budget'

COL_SPEND_FULL_POTENTIAL = 'spend_lookback_full_potential'
COL_SPEND_FULL_POTENTIAL_CAPPED = 'spend_lookback_full_potential_capped'
COL_SPEND_MTD = 'spend_mtd'
COL_SBA_ALLOCATION_NEW_FLOAT = RPT_COL_CC_SBA_ALLOCATION + '_new_float'
COL_SBA_ALLOCATION_NEW = RPT_COL_CC_SBA_ALLOCATION + '_new'
COL_SBA_STRATEGY_BUDGET_REMAINING = 'SBA_Campaign_budget_remaining'
COL_SBA_BUDGET_PACING_NEW = RPT_COL_CC_SBA_BUDGET_PACING + '_new'
COL_BUDGET_REMAINING = 'budget_remaining'
COL_DAILY_BUDGET_NEW = RPT_COL_DAILY_BUDGET + '_new'
COL_REC_DAILY_BUDGET_NEW = RPT_COL_CC_SBA_REC_DAILY_BUDGET + '_new'
COL_DAYS_REMAINING= 'weekdays_remaining'
COL_DAYS_TOTAL= 'weekdays_total'
COL_PACING_CALC = 'pacing_calc'

outputDf[BULK_COL_DAILY_BUDGET] = "<<YOUR VALUE>>"

today = datetime.datetime.now(CLIENT_TIMEZONE).date()
print("inputDf shape", inputDf.shape)
print("inputDf dtypes", inputDf.dtypes)

# change back to percent string
if inputDf [RPT_COL_CC_SBA_ALLOCATION ].dtype == "float":
    inputDf[RPT_COL_CC_SBA_ALLOCATION ] = round(inputDf[RPT_COL_CC_SBA_ALLOCATION ] * 100.0, 0).astype(str) + '%'
if inputDf[BULK_COL_CC_SBA_BUDGET_PACING].dtype == "float":
    inputDf[BULK_COL_CC_SBA_BUDGET_PACING] = round(inputDf[BULK_COL_CC_SBA_BUDGET_PACING] * 100.0, 0).astype(str) + '%'

# coerce Program End Date into Date type
inputDf[RPT_COL_PROGRAM_END_Date] = pd.to_datetime(inputDf[RPT_COL_PROGRAM_END_Date], errors='coerce')

inputDf = inputDf.set_index([RPT_COL_CC_SBA_STRATEGY])
group_by_strategy = inputDf.groupby(RPT_COL_CC_SBA_STRATEGY)

# ## Calculate Full-Potential Spend
# * Adjust Historical Spend by _Lost Impression Share due to Budget
# 

adj_ratio = 1 + (inputDf[RPT_COL_LOST_IMPR_SHARE_BUDGET] / (1 - inputDf[RPT_COL_LOST_IMPR_SHARE_BUDGET]))

inputDf[COL_SPEND_FULL_POTENTIAL] = round(inputDf[RPT_COL_PUB_COST] * adj_ratio, 2)

# ## Remove Date Segmentation
# * Calculate MTD Spend

# SUM Series with Date index and only includes current month
def current_month_sum(x):
    x = x.sort_index()
    mtd = x[ (x.index.month == today.month) & (x.values > 0)]
    return mtd.sum()

groupby_cols = [ \
    RPT_COL_CC_SBA_STRATEGY, \
    RPT_COL_STRATEGY, \
    RPT_COL_PUBLISHER_NAME, \
    RPT_COL_ACCOUNT, \
    RPT_COL_CAMPAIGN, \
]


agg_spec = {
    RPT_COL_CAMPAIGN_STATUS: 'last', \
    RPT_COL_DAILY_BUDGET: 'last', \
    RPT_COL_CC_SBA_CAMPAIGN_BUDGET: 'last', \
    RPT_COL_CC_SBA_ALLOCATION : 'last', \
    RPT_COL_CC_SBA_REC_DAILY_BUDGET: 'last', \
    RPT_COL_CC_SBA_BUDGET_PACING: 'last', \
    RPT_COL_SBA_TRAFFIC: 'last', \
    RPT_COL_CLICKS: 'sum', \
    RPT_COL_CONV: 'sum', \
    RPT_COL_PUB_COST: 'sum', \
    COL_SPEND_MTD: current_month_sum, \
    COL_SPEND_FULL_POTENTIAL: 'sum', \
    RPT_COL_PROGRAM_END_Date: 'last', \
}

inputDf[COL_SPEND_MTD] = inputDf[RPT_COL_PUB_COST]

df_campaign_agg = inputDf.reset_index() \
                         .set_index(RPT_COL_DATE) \
                         .groupby(groupby_cols) \
                         .agg(agg_spec) \
                         .reset_index() \
                         .set_index(RPT_COL_CC_SBA_STRATEGY)


# ## Only allocate budget for recently trafficking campaigns
# * Exclude Campaigns that are:
# ** not ACTIVE 
# ** without spend in lookback period
# ** Program Date is in the past

inactive_campaigns = (df_campaign_agg[RPT_COL_CAMPAIGN_STATUS] != 'Active') & (df_campaign_agg[RPT_COL_PUB_COST] == 0)
expired_campaigns = df_campaign_agg[RPT_COL_PROGRAM_END_Date].notnull() & (df_campaign_agg[RPT_COL_PROGRAM_END_Date] < pd.to_datetime(today))
df_campaign_agg = df_campaign_agg.loc[ ~(inactive_campaigns | expired_campaigns) ]


# ## Calculate Budget Allocation Ratio 
# * Cap full potential spend at 2X (don't spend twice as much as before)
# * Compare full potential spend for each campaign to total spend within same Strategy budget group

df_campaign_agg[COL_SPEND_FULL_POTENTIAL_CAPPED] = df_campaign_agg \
    .apply(lambda row: min(row[COL_SPEND_FULL_POTENTIAL], 2 * row[RPT_COL_PUB_COST]), axis=1)


# use transform to calculate sum for each Stratgey and make it available on every row
# note: no need to build aggregate DataFrame and JOIN back to original

df_campaign_agg[COL_SBA_ALLOCATION_NEW_FLOAT] = 100.0 * \
        df_campaign_agg[COL_SPEND_FULL_POTENTIAL_CAPPED] / \
        df_campaign_agg.groupby(RPT_COL_CC_SBA_STRATEGY)[COL_SPEND_FULL_POTENTIAL_CAPPED].transform('sum')

df_campaign_agg[COL_SBA_ALLOCATION_NEW] = round(df_campaign_agg[COL_SBA_ALLOCATION_NEW_FLOAT],0).astype(str) + '%'


# 
# ## Calculate Remaining Budget
# * For each Straetgy budget group, calculate how much Budget is left by substracting SBA Monthly budget from MTD Strategy spend

df_campaign_agg[COL_SBA_STRATEGY_BUDGET_REMAINING] =  \
        df_campaign_agg[RPT_COL_CC_SBA_CAMPAIGN_BUDGET] - \
        df_campaign_agg.groupby(by=[RPT_COL_CC_SBA_STRATEGY])[COL_SPEND_MTD].sum()


# ## Allocate Budget
# * Allocate remaining budget to each campaign according to ratio calculated above

df_campaign_agg[COL_BUDGET_REMAINING] = round(df_campaign_agg[COL_SBA_STRATEGY_BUDGET_REMAINING] * df_campaign_agg[COL_SBA_ALLOCATION_NEW_FLOAT] / 100.0, 1)


# ## Calculate SBA Daily Budget
# 
# * Calcualte next day Daily Budget by dividing allocated budget by number of Business Days left in the current month


today_numpy = pd.to_datetime(today).to_numpy().astype('datetime64[D]')
next_month_start = (today_numpy + pd.offsets.BMonthBegin()).to_numpy().astype('datetime64[D]')

# for months ending on weekends, use max(1,x) to avoid dividing by zero
days_left = max(1, (next_month_start - today_numpy).astype('timedelta64[D]').astype(int))

df_campaign_agg[COL_DAYS_REMAINING] = days_left
df_campaign_agg[COL_REC_DAILY_BUDGET_NEW] = round(df_campaign_agg[COL_BUDGET_REMAINING] / days_left, 0)

# ### Apply Minimum Rule
# * Bump allocated budget above minimum

allocated_below_min = (df_campaign_agg[COL_REC_DAILY_BUDGET_NEW] < MINIMUM_DAILY_BUDGET)
df_campaign_agg.loc[allocated_below_min, COL_REC_DAILY_BUDGET_NEW] = MINIMUM_DAILY_BUDGET


# ### Traffic Budget
df_campaign_agg[COL_DAILY_BUDGET_NEW] = np.nan

# campaigns to traffic
to_traffic = df_campaign_agg[RPT_COL_SBA_TRAFFIC].notnull() & \
            (df_campaign_agg[RPT_COL_SBA_TRAFFIC].astype(str).str.lower() == 'traffic')
print("Not weekend. Traffic count", to_traffic.sum())

# copy budgets over
df_campaign_agg[COL_DAILY_BUDGET_NEW] = df_campaign_agg[COL_REC_DAILY_BUDGET_NEW]
# then blank out budget for non-traffic campaigns
df_campaign_agg.loc[~to_traffic, COL_DAILY_BUDGET_NEW] = np.nan


# ## Calculate Salesforece Item ID -level Pacing compliance percentage. Ideally should be 100% each day.

# number of elapsed workdays
current_month_start = pd.to_datetime(today.replace(day=1)).to_numpy().astype('datetime64[D]')
total_days_in_month = (next_month_start - current_month_start).astype('timedelta64[D]').astype(int)
df_campaign_agg[COL_DAYS_TOTAL] = total_days_in_month
prorated_ratio = (total_days_in_month - days_left) / total_days_in_month

print("today", today)
print("current_month_start", current_month_start)
print("next_month_start", next_month_start)
print("weekdays_in_month", total_days_in_month)
print("weekdays_left", days_left)
print("prorated_ratio", prorated_ratio)

# divide MTD spend by prorated total budget
mask = df_campaign_agg[RPT_COL_CC_SBA_CAMPAIGN_BUDGET] > 0
df_campaign_agg[COL_PACING_CALC] = round(100.0 * \
                                    df_campaign_agg.groupby(by=[RPT_COL_CC_SBA_STRATEGY])[COL_SPEND_MTD].sum() / \
                                    (prorated_ratio * df_campaign_agg[RPT_COL_CC_SBA_CAMPAIGN_BUDGET]), \
                                    0).astype(str) + '%'
df_campaign_agg.loc[mask, COL_SBA_BUDGET_PACING_NEW] = df_campaign_agg.loc[mask, COL_PACING_CALC]



# Debug DF with full details
df_strategy_budget = group_by_strategy[[RPT_COL_CC_SBA_CAMPAIGN_BUDGET]].transform('max').dropna().drop_duplicates()
print("Strategy budgets", df_strategy_budget.to_string())


# ## Generate outputDf

# Check for changes
changed = df_campaign_agg[COL_REC_DAILY_BUDGET_NEW].notnull() & \
    ( \
       (df_campaign_agg[RPT_COL_CC_SBA_REC_DAILY_BUDGET] != df_campaign_agg[COL_REC_DAILY_BUDGET_NEW]) | \
       (df_campaign_agg[RPT_COL_DAILY_BUDGET] != df_campaign_agg[COL_DAILY_BUDGET_NEW]) | \
       (df_campaign_agg[RPT_COL_CC_SBA_ALLOCATION ] != df_campaign_agg[COL_SBA_ALLOCATION_NEW]) | \
       (df_campaign_agg[RPT_COL_CC_SBA_BUDGET_PACING] != df_campaign_agg[COL_SBA_BUDGET_PACING_NEW]) \
    )

print("Changed rows:", changed.sum())

# Debug
debugDf = df_campaign_agg.loc[changed] \
      .reset_index() \
      .sort_values(by=[RPT_COL_CC_SBA_STRATEGY, COL_DAILY_BUDGET_NEW, COL_REC_DAILY_BUDGET_NEW], ascending=False) 

# print("debugDf", tableize(debugDf))

# Only emit output for changed campaigns
if changed.sum() > 0:

    # construct outputDf
    outputDf = df_campaign_agg.loc[changed, [RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN, COL_DAILY_BUDGET_NEW, COL_REC_DAILY_BUDGET_NEW, COL_SBA_BUDGET_PACING_NEW, COL_SBA_ALLOCATION_NEW]] \
                      .copy() \
                      .rename(columns={ \
                            COL_DAILY_BUDGET_NEW: BULK_COL_DAILY_BUDGET, \
                            COL_REC_DAILY_BUDGET_NEW: BULK_COL_REC_DAILY_BUDGET_CC_SBA, \
                            COL_SBA_BUDGET_PACING_NEW: BULK_COL_CC_SBA_BUDGET_PACING, \
                            COL_SBA_ALLOCATION_NEW: BULK_COL_CC_SBA_ALLOCATION, \
                        }) \
                      .reset_index() \
                      .sort_values(by=[RPT_COL_CC_SBA_STRATEGY, BULK_COL_DAILY_BUDGET, BULK_COL_REC_DAILY_BUDGET_CC_SBA], ascending=False) \
                      .drop(RPT_COL_CC_SBA_STRATEGY, axis=1)

    print("outputDf shape", outputDf.shape)
    print("outputDf", tableize(outputDf.head()))
else:
    print("No changes detected, returning an empty dataframe")
    outputDf = pd.DataFrame(columns=[BULK_COL_ACCOUNT, BULK_COL_CAMPAIGN, BULK_COL_DAILY_BUDGET, BULK_COL_REC_DAILY_BUDGET_CC_SBA, BULK_COL_CC_SBA_BUDGET_PACING, BULK_COL_CC_SBA_ALLOCATION])##

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

02 Apr 2024

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