Script 459: Epicor Budget Cap

Purpose

The Python script manages campaign statuses by pausing them when the month-to-date (MTD) spend reaches the Epicor Monthly Budget.

To Elaborate

The script is designed to automate the management of advertising campaigns by monitoring their spending against a predefined monthly budget. It calculates the month-to-date (MTD) spend for each campaign and compares it to the allocated monthly budget. If the MTD spend exceeds the budget, adjusted by a safety margin to account for system lag and non-linear spending patterns, the script recommends pausing the campaign. Conversely, if the MTD spend is below the budget, it suggests reactivating paused campaigns. This ensures that campaigns do not overspend while allowing for reactivation if there is budget available. The script is particularly useful for businesses using Epicor for budget management, as it helps maintain financial control over advertising expenditures.

Walking Through the Code

Configuration and Setup
- The script begins by defining a configurable parameter, BUDGET_CAP_SAFETY_MARGIN, which sets a safety margin for budget calculations.
- It checks whether the script is running on a server or locally, loading necessary data from a pickle file if running locally.
Data Preparation
- The script loads data into a DataFrame, inputDf, and prepares it by filling missing values and converting data types as needed.
- It calculates the MTD spend for each campaign by summing the publication costs grouped by epicorID.
Campaign Status Management
- The script identifies campaigns that have exceeded their budget and recommends pausing them by setting a flag.
- It also identifies campaigns that can be resumed if their spend is below the budget and they have a valid pause date.
- The script updates the recommended status for each campaign based on these calculations.
Traffic Management
- The script determines whether campaigns should actually be paused or resumed based on their current status and recommended status.
- It updates the campaign status and pause date accordingly.
Cleanup and Output Preparation
- The script cleans up any orphaned pause dates that could cause confusion.
- It prepares the output by selecting only the changed rows and renaming columns for clarity.
- If running locally, it writes the output and debug information to CSV files for further analysis.

Vitals

Script ID : 459
Client ID / Customer ID: 1306917127 / 60268084
Action Type: Bulk Upload
Item Changed: Campaign
Output Columns: Account, Campaign, Status, SBA Recommended Status, SBA Pause Date
Linked Datasource: M1 Report
Reference Datasource: None
Owner: Michael Huang (mhuang@marinsoftware.com)
Created by Michael Huang on 2023-10-31 13:27
Last Updated by Michael Huang on 2024-03-08 22:12

> See it in Action

Python Code

##
## name: Epicor Budget Cap
## description:
##  Pause campaigns when MTD spend reaches Epicor Monthly Budget
## 
## author: Michael S. Huang
## created: 2023-10-31
## 

##### Configurable Param #####
# Define how close MTD spend can get to Monthly Budget before being Paused
#  - compensates for lag in system
#  - compendates for non-linearity in intraday spend
BUDGET_CAP_SAFETY_MARGIN = 0.02 # set to 2%
##############################


########### 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 = '/Users/mhuang/Downloads/pickle/heartland_dental_cap_20240308.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

local_dev = 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.")
    local_dev = True
    # 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

    # Printing out the version of Python, Pandas and Numpy
    # import sys
    # python_version = sys.version
    # pandas_version = pd.__version__
    # numpy_version = np.__version__

    # print(f"python version: {python_version}")
    # print(f"pandas version: {pandas_version}")
    # print(f"numpy version: {numpy_version}")

    # other imports
    import re
    import urllib

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

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

# primary data source and columns
inputDf = dataSourceDict["1"]
RPT_COL_ACCOUNT = 'Account'
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_PUBLISHER_NAME = 'Publisher Name'
RPT_COL_STRATEGY = 'Strategy'
RPT_COL_CAMPAIGN_STATUS = 'Campaign Status'
RPT_COL_SBA_TRAFFIC = 'SBA Traffic'
RPT_COL_SBA_RECOMMENDED_STATUS = 'SBA Recommended Status'
RPT_COL_SBA_PAUSE_DATE = 'SBA Pause Date'
RPT_COL_DAILY_BUDGET = 'Daily Budget'
RPT_COL_SBA_CALCULATED_BUDGET_DAILY = 'SBA Calculated Budget Daily'
RPT_COL_SBA_BUDGET_PACING = 'SBA Budget Pacing'
RPT_COL_SBA_ALLOCATION = 'SBA Allocation'
RPT_COL_PUB_COST = 'Pub. Cost $'
RPT_COL_EPICORID = 'epicorID'
RPT_COL_EPICORID_MONTHLY_BUDGET = 'epicorID - Monthly Budget'

# output columns and initial values
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_STATUS = 'Status'
BULK_COL_SBA_PAUSE_DATE = 'SBA Pause Date'
BULK_COL_SBA_RECOMMENDED_STATUS = 'SBA Recommended Status'

# Make inputDf a copy of original for comparison via select_change() later
originalDf = dataSourceDict["1"]
inputDf = originalDf.copy()

# define some intermediate columns
COL_MTD_EPICOR_SPEND = 'mtd_epicor_spend'

# define Status values
VAL_STATUS_ACTIVE = 'Active'
VAL_STATUS_PAUSED = 'Paused'
VAL_BLANK = ''

## Fix types and values

# Replace nan with empty strings to declutter output
inputDf.fillna(VAL_BLANK, inplace=True)
# Convert RPT_COL_EPICORID_MONTHLY_BUDGET to numeric, coercing errors to NaN
inputDf[RPT_COL_EPICORID_MONTHLY_BUDGET] = pd.to_numeric(inputDf[RPT_COL_EPICORID_MONTHLY_BUDGET], errors='coerce')
# Replace NaN values with 0.0 if that's the desired behavior
inputDf[RPT_COL_EPICORID_MONTHLY_BUDGET].fillna(0.0, inplace=True)
# Force RPT_COL_SBA_PAUSE_DATE to be String type
inputDf[RPT_COL_SBA_PAUSE_DATE] = inputDf[RPT_COL_SBA_PAUSE_DATE].astype(str)

print("inputDf shape", inputDf.shape)
print("inputDf info", inputDf.info())


# Calculate MTD Epicor Spend
inputDf[COL_MTD_EPICOR_SPEND] = inputDf.groupby(RPT_COL_EPICORID)[RPT_COL_PUB_COST].transform('sum')

# Recommend to Pause camapigns with MTD Epicor Spend over Monthly Epicor Budget (by a margin)
has_epicor_budget = inputDf[RPT_COL_EPICORID_MONTHLY_BUDGET] > 0.0
over_spent_campaigns = inputDf[COL_MTD_EPICOR_SPEND] >= inputDf[RPT_COL_EPICORID_MONTHLY_BUDGET] * (1 - BUDGET_CAP_SAFETY_MARGIN)
active_campaigns = inputDf[RPT_COL_CAMPAIGN_STATUS] == VAL_STATUS_ACTIVE
campaigns_to_pause = active_campaigns & has_epicor_budget & over_spent_campaigns

inputDf.loc[campaigns_to_pause, 'pause'] = 1
print(f"campaigns_to_pause count: {sum(campaigns_to_pause)}")

inputDf.loc[ campaigns_to_pause, \
             RPT_COL_SBA_RECOMMENDED_STATUS \
           ] = VAL_STATUS_PAUSED
 
# Recommend to reactivate campaigns with MTD Epicor Spend under Monthly Epicor Budget (by a margin)
# but limited to campaigns with SBA Pause Date populated 10 digit date
under_spent_campaigns = inputDf[COL_MTD_EPICOR_SPEND] < inputDf[RPT_COL_EPICORID_MONTHLY_BUDGET] * (1 - BUDGET_CAP_SAFETY_MARGIN)
sba_paused_campaigns = inputDf[RPT_COL_SBA_PAUSE_DATE].astype('str').str.len() >= 10
paused_campaigns = inputDf[RPT_COL_CAMPAIGN_STATUS] == VAL_STATUS_PAUSED
campaigns_to_resume = paused_campaigns & sba_paused_campaigns & under_spent_campaigns 

inputDf.loc[campaigns_to_resume, 'resume'] = 1
print(f"campaigns_to_resume count: {sum(campaigns_to_resume)}")

inputDf.loc[ campaigns_to_resume, \
             RPT_COL_SBA_RECOMMENDED_STATUS  \
           ] = VAL_STATUS_ACTIVE

# Clear Rec Status for everyone else
campaigns_to_clear = ~(campaigns_to_pause | campaigns_to_resume)

inputDf.loc[campaigns_to_clear, 'clear'] = 1
print(f"campaigns_to_clear count: {sum(campaigns_to_clear)}")

inputDf.loc[ campaigns_to_clear, \
             RPT_COL_SBA_RECOMMENDED_STATUS  \
           ] = VAL_BLANK

## Actually taffic PAUSE

should_traffic = inputDf[RPT_COL_SBA_TRAFFIC].astype(str).str.lower() == 'traffic'

should_traffic_pause = should_traffic & \
                       (inputDf[RPT_COL_SBA_RECOMMENDED_STATUS] == VAL_STATUS_PAUSED) & \
                       (inputDf[RPT_COL_SBA_RECOMMENDED_STATUS] != inputDf[RPT_COL_CAMPAIGN_STATUS])


inputDf.loc[should_traffic_pause, 'traffic_pause'] = 1
print(f"should_traffic_pause count: {sum(should_traffic_pause)}")


inputDf.loc[should_traffic_pause, RPT_COL_CAMPAIGN_STATUS] = inputDf.loc[should_traffic_pause, RPT_COL_SBA_RECOMMENDED_STATUS]
inputDf.loc[should_traffic_pause, RPT_COL_SBA_PAUSE_DATE] = today.strftime('%Y-%m-%d')

## Actually taffic RESUME

should_traffic_resume = should_traffic & \
                       (inputDf[RPT_COL_SBA_RECOMMENDED_STATUS] == VAL_STATUS_ACTIVE) & \
                       (inputDf[RPT_COL_SBA_RECOMMENDED_STATUS] != inputDf[RPT_COL_CAMPAIGN_STATUS]) & \
                       sba_paused_campaigns

inputDf.loc[should_traffic_resume, 'traffic_resume'] = 1
print(f"should_traffic_resume count: {sum(should_traffic_resume)}")

inputDf.loc[should_traffic_resume, RPT_COL_CAMPAIGN_STATUS] = inputDf.loc[should_traffic_resume, RPT_COL_SBA_RECOMMENDED_STATUS]
inputDf.loc[should_traffic_resume, RPT_COL_SBA_PAUSE_DATE] = VAL_BLANK

## Cleanup. "Pause Date" is a marker to indicate this Script actioned the Pause. If not Paused, for whatever reason, then a non-blank "Pause Date" causes confusion. 

orphan_pause_date = sba_paused_campaigns & (inputDf[RPT_COL_CAMPAIGN_STATUS] == VAL_STATUS_ACTIVE)
inputDf.loc[orphan_pause_date, RPT_COL_SBA_PAUSE_DATE] = VAL_BLANK
print(f"Cleaned up {orphan_pause_date.sum()} orphaned Pause Date")


## Prepare output

print(f"select_changed with inputDf shape {inputDf.shape} and originalDf shape {originalDf.shape}")

# only include changed rows in bulk file
(outputDf, debugDf) = select_changed(inputDf, \
                                    originalDf, \
                                    diff_cols = [ \
                                        RPT_COL_CAMPAIGN_STATUS, \
                                        RPT_COL_SBA_RECOMMENDED_STATUS, \
                                        RPT_COL_SBA_PAUSE_DATE, \
                                    ], \
                                    select_cols = [ \
                                        RPT_COL_ACCOUNT, \
                                        RPT_COL_CAMPAIGN, \
                                        RPT_COL_CAMPAIGN_STATUS, \
                                        RPT_COL_SBA_RECOMMENDED_STATUS, \
                                        RPT_COL_SBA_PAUSE_DATE, \
                                    ], \
                                    merged_cols=[RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN] \
                                    )


# mark changed rows in debugDf
outputDf = outputDf.reset_index(drop=True) \
                   .set_index([RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN])
debugDf = debugDf.reset_index(drop=True) \
                 .set_index([RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN])
debugDf.loc[outputDf.index, 'changed'] = 1
outputDf = outputDf.reset_index()
debugDf = debugDf.reset_index()

# remember to use Bulk column header for Status
outputDf = outputDf.rename(columns = { \
                RPT_COL_CAMPAIGN_STATUS: BULK_COL_STATUS \
                })

print("outputDf shape", outputDf.shape)
print("sample outputDf", tableize(outputDf.tail(5)))

## local debug
if local_dev:
    output_filename = 'outputDf.csv'
    outputDf.to_csv(output_filename, index=False)
    print(f"Local Dev: Output written to: {output_filename}")

    debug_filename = 'debugDf.csv'
    debugDf.to_csv(debug_filename, index=False)
    print(f"Local Dev: Debug written to: {debug_filename}")

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

31 Oct 2023

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