Script 899: CC SBA Budget Pacing

Purpose

Python script to allocate budget for SBA campaigns based on various factors.

To Elaborate

The Python script solves the problem of allocating budget for SBA (Structured Budget Allocation) campaigns. SBA campaigns are allocated budget based on factors such as remaining budget for each Strategy Group, remaining weekdays in the month, historical spend and spend potential, campaigns with spend in the lookback period, and a minimum daily budget. The script calculates the budget allocation ratio for each campaign, allocates the remaining budget to each campaign, and calculates the recommended daily budget for each campaign. The script also applies a minimum rule to ensure that the allocated budget is above the minimum daily budget. Finally, the script calculates the Salesforce Item ID-level pacing compliance percentage.

Walking Through the Code

The script starts by defining a configurable parameter for the minimum daily budget.
It then defines column constants and client timezone.
The script converts the input dataframe to the desired format by changing column data types and coercing the Program End Date into a Date type.
The script calculates the full-potential spend by adjusting the historical spend based on the lost impression share due to budget.
It calculates the MTD spend by summing the spend for the current month.
The script groups the data by strategy and calculates various metrics such as last campaign status, last daily budget, last SBA campaign budget, last SBA allocation, last SBA recommended daily budget, last SBA budget pacing, last SBA traffic, sum of clicks, sum of conversions, sum of publisher cost, sum of MTD spend, sum of full-potential spend, and last program end date.
It excludes campaigns that are not active, without spend in the lookback period, or have a program end date in the past.
The script calculates the budget allocation ratio by capping the full-potential spend at 2 times the current spend and comparing it to the total spend within the same strategy budget group.
It calculates the remaining budget for each strategy budget group by subtracting the MTD strategy spend from the SBA monthly budget.
The script allocates the remaining budget to each campaign based on the allocation ratio.
It calculates the recommended daily budget for each campaign by dividing the allocated budget by the number of business days left in the current month.
The script applies a minimum rule to bump the allocated budget above the minimum daily budget.
It copies the recommended daily budget to the daily budget column for campaigns that need to be trafficked and blanks out the budget for non-traffic campaigns.
The script calculates the Salesforce Item ID-level pacing compliance percentage by dividing the MTD spend by the prorated total budget.
It checks for changes in the recommended daily budget, daily budget, allocation, and budget pacing.
If changes are detected, the script constructs the output dataframe with the changed campaigns’ account, campaign, daily budget, recommended daily budget, budget pacing, and allocation.
If no changes are detected, the script returns an empty dataframe.

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 2024-05-15 07:44:05 GMT

02 Apr 2024

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