Script 813: SBA Campaign Pacing with Rec. Budget and Current Budget

Purpose

Create columns of current Budget and recommended Budget

To Elaborate

This Python script solves the problem of allocating budgets for SBA (Structured Budget Allocation) campaigns in order to minimize lost impression share due to budget constraints. It takes into account various factors such as remaining budget, remaining weekdays in the month, historical spend, spend potential, campaigns with spend in the lookback period, and a minimum daily budget.

The key business rules of this script are as follows:

  • Allocate budget based on the remaining budget for each Salesforce Item ID Budget Group.
  • Allocate budget based on the remaining weekdays in the month.
  • Adjust historical spend by the lost impression share due to budget.
  • Exclude campaigns that are not active, without spend in the lookback period, or have a program end date in the past.
  • Cap the full potential spend at 2 times the current spend.
  • Calculate the budget allocation ratio for each campaign based on the full potential spend and the total spend within the same Salesforce Item ID budget group.
  • Calculate the remaining budget for each SBA Strategy budget group by subtracting the MTD (Month-to-Date) spend from the SBA monthly budget.
  • Allocate the remaining budget to each campaign according to the allocation ratio.
  • Calculate the next day’s daily budget by dividing the allocated budget by the number of days left in the current month.
  • Apply a minimum rule to bump the allocated budget above the minimum daily budget.
  • Calculate the Salesforce Item ID-level pacing compliance percentage, which ideally should be 100% each day.
  • Generate the output with the columns of current budget and recommended budget for each campaign.

Walking Through the Code

  1. The script starts by defining a configurable parameter for the minimum daily budget.
  2. It then checks if the code is running on the server or locally, and loads the necessary data accordingly.
  3. The script sets up the required timezone and imports the necessary libraries.
  4. It defines constants for the column names used in the script.
  5. The script initializes the output dataframe with a placeholder value for the daily budget.
  6. It retrieves the current date and time in the client’s timezone.
  7. The script converts the allocation column to a percentage string if it is of float type.
  8. It coerces the program end date column into a date type.
  9. The script sets the input dataframe as the index using the SBA strategy column and groups it by the SBA strategy.
  10. It calculates the full-potential spend by adjusting the historical spend with the lost impression share due to budget.
  11. The script calculates the MTD spend by summing the spend for the current month.
  12. It aggregates the campaign data by grouping it based on certain columns and applying various aggregation functions.
  13. The script excludes campaigns that are not active, without spend in the lookback period, or have a program end date in the past.
  14. It 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 Salesforce Item ID budget group.
  15. The script calculates the remaining budget for each SBA Strategy budget group by subtracting the MTD spend from the SBA monthly budget.
  16. It allocates the remaining budget to each campaign according to the allocation ratio.
  17. The script calculates the next day’s daily budget by dividing the allocated budget by the number of days left in the current month.
  18. It applies a minimum rule to bump the allocated budget above the minimum daily budget.
  19. The script handles the traffic budget for campaigns that need to be trafficked separately.
  20. It calculates the Salesforce Item ID-level pacing compliance percentage by dividing the MTD spend by the prorated total budget.
  21. The script generates the output dataframe by checking for changes in the recommended daily budget, daily budget, allocation, budget pacing, current daily budget, and budget difference columns.
  22. If there are changes, the script constructs the output dataframe with the relevant columns.
  23. If there are no changes, the script returns an empty dataframe.

Vitals

  • Script ID : 813
  • Client ID / Customer ID: 1306926629 / 60270083
  • Action Type: Bulk Upload
  • Item Changed: Campaign
  • Output Columns: Account, Campaign, Daily Budget, Rec. Daily Budget, SBA Allocation, SBA Budget Pacing, Current Daily Budget, Budget Difference
  • Linked Datasource: M1 Report
  • Reference Datasource: None
  • Owner: dwaidhas@marinsoftware.com (dwaidhas@marinsoftware.com)
  • Created by dwaidhas@marinsoftware.com on 2024-03-14 15:20
  • Last Updated by dwaidhas@marinsoftware.com on 2024-04-02 09:21
> 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 Salesforce Item ID Budget Group
# * Remaining weekdays in month
# * Historical spend and spend potential
# * Campaigns with spend in lookback period
# * Minimum daily budget
#
# Author: Dana Waidhas
#
# Created: 2024-02-26
#

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

MINIMUM_DAILY_BUDGET = 10

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

########### START - Local Mode Config ###########
# Step 1: Uncomment download_preview_input flag and run Preview successfully with the Datasources you want
download_preview_input=False
# Step 2: In MarinOne, go to Scripts -> Preview -> Logs, download 'dataSourceDict' pickle file, and update pickle_path below
# pickle_path = ''
pickle_path = '/Users/mhuang/Downloads/pickle/avb_marketing_datasource_dict_1702622906522.pkl'
# Step 3: Copy this script into local IDE with Python virtual env loaded with pandas and numpy.
# Step 4: Run locally with below code to init dataSourceDict

# determine if code is running on server or locally
def is_executing_on_server():
    try:
        # Attempt to access a known restricted builtin
        dict_items = dataSourceDict.items()
        return True
    except NameError:
        # NameError: dataSourceDict object is missing (indicating not on server)
        return False

if is_executing_on_server():
    print("Code is executing on server. Skip init.")
elif len(pickle_path) > 3:
    print("Code is NOT executing on server. Doing init.")
    # load dataSourceDict via pickled file
    import pickle
    dataSourceDict = pickle.load(open(pickle_path, 'rb'))

    # print shape and first 5 rows for each entry in dataSourceDict
    for key, value in dataSourceDict.items():
        print(f"Shape of dataSourceDict[{key}]: {value.shape}")
        # print(f"First 5 rows of dataSourceDict[{key}]:\n{value.head(5)}")

    # set outputDf same as inputDf
    inputDf = dataSourceDict["1"]
    outputDf = inputDf.copy()

    # setup timezone
    import datetime
    # Chicago Timezone is GMT-5. Adjust as needed.
    CLIENT_TIMEZONE = datetime.timezone(datetime.timedelta(hours=-5))

    # import pandas
    import pandas as pd
    import numpy as np

    # other imports
    import re
    import urllib

    # import Marin util functions
else:
   from marin_scripts_utils import tableize, select_changed
   print("Running locally but no pickle path defined. dataSourceDict not loaded.")
   exit(1)
########### END - Local Mode Setup ###########



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_SBA_STRATEGY = 'SBA Strategy'
RPT_COL_SBA_CAMPAIGN_BUDGET = 'SBA Campaign Budget'
RPT_COL_SBA_ALLOCATION = 'SBA Allocation'
RPT_COL_REC_DAILY_BUDGET = 'Rec. Daily Budget'
RPT_COL_SBA_BUDGET_PACING = 'SBA Budget Pacing'
RPT_COL_SBA_TRAFFIC = 'SBA Traffic'
RPT_COL_PROGRAM_END_Date = 'Program End Date'
RPT_COL_CURRENT_DAILY_BUDGET = 'Current Daily Budget'
RPT_COL_BUDGET_DIFFERENCE = 'Budget Difference'

BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_DAILY_BUDGET = 'Daily Budget'
BULK_COL_SBA_ALLOCATION = 'SBA Allocation'
BULK_COL_SBA_BUDGET_PACING = 'SBA Budget Pacing'
BULK_COL_REC_DAILY_BUDGET = 'Rec. Daily Budget'
BULK_COL_CURRENT_DAILY_BUDGET = 'Current Daily Budget'
BULK_COL_BUDGET_DIFFERENCE = 'Budget Difference'


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_SBA_ALLOCATION + '_new_float'
COL_SBA_ALLOCATION_NEW = RPT_COL_SBA_ALLOCATION + '_new'
COL_SBA_STRATEGY_BUDGET_REMAINING = 'SBA_Campaign_budget_remaining'
COL_SBA_BUDGET_PACING_NEW = RPT_COL_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_REC_DAILY_BUDGET + '_new'
COL_CURRENT_DAILY_BUDGET_NEW = RPT_COL_CURRENT_DAILY_BUDGET + '_new'
COL_BUDGET_DIFFERENCE_NEW = RPT_COL_BUDGET_DIFFERENCE + '_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_SBA_ALLOCATION].dtype == "float":
    inputDf[RPT_COL_SBA_ALLOCATION] = round(inputDf[RPT_COL_SBA_ALLOCATION] * 100.0, 0).astype(str) + '%'
if inputDf[BULK_COL_SBA_BUDGET_PACING].dtype == "float":
    inputDf[BULK_COL_SBA_BUDGET_PACING] = round(inputDf[BULK_COL_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_SBA_STRATEGY])
group_by_salesforce_item_ID = inputDf.groupby(RPT_COL_SBA_STRATEGY)

# ## Calculate Full-Potential Spend
# * Adjust Historical Spend by _Lost Impression Share due to Budget_ (see [Formula](https://docs.google.com/document/d/1EbCQ5z9Up8TZ6GISEeCaRSB3Fc15vCPCfeIydree23M/edit#bookmark=id.5fsx7jlseze6))
# 

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_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_SBA_CAMPAIGN_BUDGET: 'last', \
    RPT_COL_SBA_ALLOCATION: 'last', \
    RPT_COL_REC_DAILY_BUDGET: 'last', \
    RPT_COL_CURRENT_DAILY_BUDGET: 'last', \
    RPT_COL_BUDGET_DIFFERENCE: 'last', \
    RPT_COL_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_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 SALESFORCE_ITEM_ID 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 SALESFORCE_ITEM_ID 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_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 SBA Strategy budget group, calculate how much Budget is left by substracting SBA Monthly budget from MTD SALESFORCE_ITEM_ID spend

df_campaign_agg[COL_SBA_STRATEGY_BUDGET_REMAINING] =  \
        df_campaign_agg[RPT_COL_SBA_CAMPAIGN_BUDGET] - \
        df_campaign_agg.groupby(by=[RPT_COL_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 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("Traffic count", to_traffic.sum())

# preserve current budget in Dimensions field
df_campaign_agg[COL_CURRENT_DAILY_BUDGET_NEW] = df_campaign_agg[RPT_COL_DAILY_BUDGET]
# copy rec budget to publisher daily budget for trafficking campaigns
df_campaign_agg.loc[to_traffic, COL_DAILY_BUDGET_NEW] = df_campaign_agg.loc[to_traffic, COL_REC_DAILY_BUDGET_NEW]
# Calculate 'Budget Difference' column and round to two decimal places
df_campaign_agg[COL_BUDGET_DIFFERENCE_NEW] = round(df_campaign_agg[COL_REC_DAILY_BUDGET_NEW]-df_campaign_agg[RPT_COL_CURRENT_DAILY_BUDGET], 2)


# ## 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_SBA_CAMPAIGN_BUDGET] > 0
df_campaign_agg[COL_PACING_CALC] = round(100.0 * \
                                    df_campaign_agg.groupby(by=[RPT_COL_SBA_STRATEGY])[COL_SPEND_MTD].sum() / \
                                    (prorated_ratio * df_campaign_agg[RPT_COL_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_SALESFORCE_ITEM_ID_budget = group_by_salesforce_item_ID[[RPT_COL_SBA_CAMPAIGN_BUDGET]].transform('max').dropna().drop_duplicates()
print("Salesforce Item ID budgets", df_SALESFORCE_ITEM_ID_budget.head().to_string())


# ## Generate outputDf
# Check for changes
changed = df_campaign_agg[COL_REC_DAILY_BUDGET_NEW].notnull() & \
    ( \
       (df_campaign_agg[RPT_COL_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_SBA_ALLOCATION] != df_campaign_agg[COL_SBA_ALLOCATION_NEW]) | \
       (df_campaign_agg[RPT_COL_SBA_BUDGET_PACING] != df_campaign_agg[COL_SBA_BUDGET_PACING_NEW]) | \
       (df_campaign_agg[RPT_COL_CURRENT_DAILY_BUDGET] != df_campaign_agg[COL_CURRENT_DAILY_BUDGET_NEW]) | \
       (df_campaign_agg[RPT_COL_BUDGET_DIFFERENCE] != df_campaign_agg[COL_BUDGET_DIFFERENCE_NEW]) \
    )

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

# Debug
debugDf = df_campaign_agg.loc[changed] \
      .reset_index() \
      .sort_values(by=[RPT_COL_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,
                                     COL_CURRENT_DAILY_BUDGET_NEW, COL_BUDGET_DIFFERENCE_NEW]] \
                  .copy() \
                  .rename(columns={ \
                        COL_DAILY_BUDGET_NEW: BULK_COL_DAILY_BUDGET, \
                        COL_REC_DAILY_BUDGET_NEW: BULK_COL_REC_DAILY_BUDGET, \
                        COL_SBA_BUDGET_PACING_NEW: BULK_COL_SBA_BUDGET_PACING, \
                        COL_SBA_ALLOCATION_NEW: BULK_COL_SBA_ALLOCATION, \
                        COL_CURRENT_DAILY_BUDGET_NEW: BULK_COL_CURRENT_DAILY_BUDGET, \
                        COL_BUDGET_DIFFERENCE_NEW: BULK_COL_BUDGET_DIFFERENCE, \
                    }) \
                  .reset_index() \
                  .sort_values(by=[RPT_COL_SBA_STRATEGY, BULK_COL_DAILY_BUDGET, BULK_COL_REC_DAILY_BUDGET], ascending=False) \
                  .drop(RPT_COL_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, BULK_COL_SBA_BUDGET_PACING, BULK_COL_SBA_ALLOCATION, BULK_COL_CURRENT_DAILY_BUDGET, BULK_COL_BUDGET_DIFFERENCE])
    

Post generated on 2024-05-15 07:44:05 GMT

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