Script 653: Search SBA Budget Pacing

Purpose

The Python script optimizes daily budget allocation for Search SBA strategies by considering factors like remaining budget, weekdays, historical spend, and campaign activity.

To Elaborate

The script addresses the challenge of efficiently pacing daily budgets for Search SBA strategies to minimize lost impression share due to budget constraints. It calculates the optimal daily budget allocation by considering several factors, including 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 also takes into account campaigns that have been active in the recent past and ensures that the minimum daily budget is maintained. By doing so, it aims to allocate the remaining budget in a way that maximizes the effectiveness of the campaigns while adhering to the constraints of the available budget and time.

Walking Through the Code

  1. Configuration and Setup
    • The script begins by setting a configurable parameter, MINIMUM_DAILY_BUDGET, which determines the minimum daily budget for campaigns.
    • It defines various column names used in the data processing steps.
  2. Data Preparation
    • The script processes the input data, converting necessary columns to appropriate data types, such as dates.
    • It groups the data by the Search SBA Strategy to facilitate further calculations.
  3. Spend Calculations
    • The script calculates the full potential spend by adjusting historical spend based on lost impression share due to budget constraints.
    • It computes the month-to-date (MTD) spend by summing up the spend for the current month.
  4. Campaign Filtering
    • Campaigns that are inactive, have no spend in the lookback period, or have expired program dates are excluded from budget allocation.
  5. Budget Allocation
    • The script calculates a budget allocation ratio by capping the full potential spend and comparing it to the total spend within the same strategy budget group.
    • It determines the remaining budget for each strategy group by subtracting the MTD spend from the SBA monthly budget.
  6. Daily Budget Calculation
    • The remaining budget is allocated to each campaign based on the calculated ratio.
    • The script calculates the recommended daily budget by dividing the allocated budget by the number of business days left in the month, ensuring it meets the minimum daily budget requirement.
  7. Traffic Budget and Compliance
    • The script identifies campaigns to traffic and adjusts their daily budgets accordingly.
    • It calculates pacing compliance percentages to ensure budget adherence.
  8. Output Generation
    • The script identifies changes in the recommended daily budgets and generates an output dataframe for campaigns with updated budgets.

Vitals

  • Script ID : 653
  • Client ID / Customer ID: 1306923845 / 60269271
  • Action Type: Bulk Upload
  • Item Changed: Campaign
  • Output Columns: Account, Campaign, Daily Budget, Search SBA Allocation, Search SBA Budget Pacing
  • Linked Datasource: M1 Report
  • Reference Datasource: None
  • Owner: emerryfield@marinsoftware.com (emerryfield@marinsoftware.com)
  • Created by emerryfield@marinsoftware.com on 2024-01-19 21:48
  • Last Updated by ascott@marinsoftware.com on 2024-04-03 16:50
> See it in Action

Python Code

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#
# 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_SEARCH_SBA_STRATEGY = 'Search SBA Strategy'
RPT_COL_SEARCH_SBA_CAMPAIGN_BUDGET = 'Search SBA Campaign Budget'
RPT_COL_SEARCH_SBA_ALLOCATION = 'Search SBA Allocation'
RPT_COL_SEARCH_SBA_REC_DAILY_BUDGET = 'Search SBA Rec. Daily Budget'
RPT_COL_SEARCH_SBA_BUDGET_PACING = 'Search 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_Search_SBA_ALLOCATION = 'Search SBA Allocation'
BULK_COL_Search_SBA_BUDGET_PACING = 'Search SBA Budget Pacing'
BULK_COL_REC_DAILY_BUDGET_Search_SBA = 'Search 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_SEARCH_SBA_ALLOCATION + '_new_float'
COL_SBA_ALLOCATION_NEW = RPT_COL_SEARCH_SBA_ALLOCATION + '_new'
COL_SBA_STRATEGY_BUDGET_REMAINING = 'SBA_Campaign_budget_remaining'
COL_SBA_BUDGET_PACING_NEW = RPT_COL_SEARCH_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_SEARCH_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_SEARCH_SBA_ALLOCATION ].dtype == "float":
    inputDf[RPT_COL_SEARCH_SBA_ALLOCATION ] = round(inputDf[RPT_COL_SEARCH_SBA_ALLOCATION ] * 100.0, 0).astype(str) + '%'
if inputDf[BULK_COL_Search_SBA_BUDGET_PACING].dtype == "float":
    inputDf[BULK_COL_Search_SBA_BUDGET_PACING] = round(inputDf[BULK_COL_Search_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_SEARCH_SBA_STRATEGY])
group_by_strategy = inputDf.groupby(RPT_COL_SEARCH_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_SEARCH_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_SEARCH_SBA_CAMPAIGN_BUDGET: 'last', \
    RPT_COL_SEARCH_SBA_ALLOCATION : 'last', \
    RPT_COL_SEARCH_SBA_REC_DAILY_BUDGET: 'last', \
    RPT_COL_SEARCH_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_SEARCH_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_SEARCH_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_SEARCH_SBA_CAMPAIGN_BUDGET] - \
        df_campaign_agg.groupby(by=[RPT_COL_SEARCH_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_SEARCH_SBA_CAMPAIGN_BUDGET] > 0
df_campaign_agg[COL_PACING_CALC] = round(100.0 * \
                                    df_campaign_agg.groupby(by=[RPT_COL_SEARCH_SBA_STRATEGY])[COL_SPEND_MTD].sum() / \
                                    (prorated_ratio * df_campaign_agg[RPT_COL_SEARCH_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_SEARCH_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_SEARCH_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_SEARCH_SBA_ALLOCATION ] != df_campaign_agg[COL_SBA_ALLOCATION_NEW]) | \
       (df_campaign_agg[RPT_COL_SEARCH_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_SEARCH_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_Search_SBA, \
                            COL_SBA_BUDGET_PACING_NEW: BULK_COL_Search_SBA_BUDGET_PACING, \
                            COL_SBA_ALLOCATION_NEW: BULK_COL_Search_SBA_ALLOCATION, \
                        }) \
                      .reset_index() \
                      .sort_values(by=[RPT_COL_SEARCH_SBA_STRATEGY, BULK_COL_DAILY_BUDGET, BULK_COL_REC_DAILY_BUDGET_Search_SBA], ascending=False) \
                      .drop(RPT_COL_SEARCH_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_Search_SBA, BULK_COL_Search_SBA_BUDGET_PACING, BULK_COL_Search_SBA_ALLOCATION])

Post generated on 2024-11-27 06:58:46 GMT

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