Script 115: Strategy Assignment

Purpose:

The Python script assigns marketing campaigns to specific strategies based on campaign name, creation date, and accumulated clicks.

To Elaborate

The script is designed to automate the assignment of marketing campaigns to predefined strategies. It uses specific criteria such as the presence of certain tokens in the campaign name, the age of the campaign, and the number of accumulated clicks to determine the appropriate strategy. The script ensures that campaigns are categorized into strategies like “Exc-Launch,” “NonExc-Launch,” “BAU,” or “Evergreen” based on these conditions. This structured approach helps in managing and optimizing marketing efforts by aligning campaigns with the most suitable strategy, thereby improving efficiency and effectiveness in budget allocation and campaign management.

Walking Through the Code

  1. Initialization and Setup
    • The script begins by defining constants for column names and strategies. These constants are used throughout the script to reference specific data fields and strategy names.
    • User changeable parameters include strategy names and campaign name tokens, which can be adjusted to fit different business needs.
  2. Strategy Assignment Rules
    • Rule 1: Campaigns are initially assigned to either “Exc-Launch” or “NonExc-Launch” strategies based on the presence of specific tokens in their names. This is done using a case-insensitive search.
    • Rule 2: If a campaign with an “Exc” or “NonExc” token is older than 14 days and has accumulated 100 or more clicks, it is reassigned to the “CPA BAU” strategy.
    • Rule 3: Any campaign older than 60 days is assigned to the “CPA Evergreen” strategy, regardless of its name or click count.
  3. Output Preparation
    • The script identifies campaigns whose strategy has changed based on the new assignments.
    • It then prepares an output DataFrame containing only the campaigns with changed strategies, ensuring that only relevant updates are processed further. If no changes are detected, an empty DataFrame is prepared.

Vitals

  • Script ID : 115
  • Client ID / Customer ID: 1306924345 / 69058
  • Action Type: Bulk Upload
  • Item Changed: Campaign
  • Output Columns: Account, Campaign, Strategy
  • Linked Datasource: M1 Report
  • Reference Datasource: None
  • Owner: Jeremy Brown (jbrown@marinsoftware.com)
  • Created by Jeremy Brown on 2023-05-19 11:09
  • Last Updated by Jonathan Reichl on 2023-12-13 11:17
> See it in Action

Python Code

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#
# Assign Campaign to Strategy according to:
#  - Campaign Name
#  - Campaign Creation Date
#  - accumulated clicks
#
# Author: Michael S. Huang
# Date: 2023-05-18
#


RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_STRATEGY = 'Strategy'
RPT_COL_CAMPAIGN_CREATIONDATE = 'Campaign Creation Date'
RPT_COL_CLICKS = 'Clicks'
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_STRATEGY = 'Strategy'

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

# define Strategies to map to
VAL_STRATEGY_IS_EXC = "SVOD - PH - Title_Exc-Launch - TIS"
VAL_STRATEGY_IS_NON_EXC = "SVOD - PH - Title_NonExc-Launch - TIS"
VAL_STRATEGY_CPA_BAU = "SVOD - PH - Title-BAU - CPS"
VAL_STRATEGY_CPA_EVERGREEN = "SVOD - PH - Title-Evergreen - CPS"

# define campaign tokens to match
VAL_CAMPAIGN_NAME_TOKEN_EXC = "_ Exc"
VAL_CAMPAIGN_NAME_TOKEN_NON_EXC = "_ NonExc"

# defines dates to check
print("timezone", CLIENT_TIMEZONE)
today = datetime.datetime.now(CLIENT_TIMEZONE).date()
date_14_days_ago = pd.to_datetime(today - datetime.timedelta(days=14))
date_60_days_ago = pd.to_datetime(today - datetime.timedelta(days=60))
print(today, date_14_days_ago, date_60_days_ago)

# define tmp column for new Strategy and set to empty
TMP_STRATEGY = RPT_COL_STRATEGY + '_'
inputDf[TMP_STRATEGY] = np.nan

### Rule 1: Assign to Exc/NonExc Strategy based on token in campaign name
###  case insensitive

inputDf.loc[ inputDf[RPT_COL_CAMPAIGN].str.contains(VAL_CAMPAIGN_NAME_TOKEN_EXC, case=False), \
             TMP_STRATEGY \
           ] = VAL_STRATEGY_IS_EXC

inputDf.loc[ inputDf[RPT_COL_CAMPAIGN].str.contains(VAL_CAMPAIGN_NAME_TOKEN_NON_EXC, case=False), \
             TMP_STRATEGY \
           ] = VAL_STRATEGY_IS_NON_EXC

### Rule 2: if Exc/NonExc campaign created more than 14 days ago and clicks >= 100,
###    assign to CPA BAU

inputDf.loc[ ( inputDf[RPT_COL_CAMPAIGN].str.contains(VAL_CAMPAIGN_NAME_TOKEN_EXC, case=False) | \
               inputDf[RPT_COL_CAMPAIGN].str.contains(VAL_CAMPAIGN_NAME_TOKEN_NON_EXC, case=False) ) & \
             (inputDf[RPT_COL_CAMPAIGN_CREATIONDATE] <= date_14_days_ago) & \
             (inputDf[RPT_COL_CLICKS] >= 100), \
             TMP_STRATEGY \
           ] = VAL_STRATEGY_CPA_BAU

### Rule 3: if campaign created more than 60 days ago,
###    assign to CPA Evergreen

inputDf.loc[ (inputDf[RPT_COL_CAMPAIGN_CREATIONDATE] <= date_60_days_ago), \
             TMP_STRATEGY \
           ] = VAL_STRATEGY_CPA_EVERGREEN

# find changed campaigns
changed = inputDf[TMP_STRATEGY].notnull() & (inputDf[RPT_COL_STRATEGY] != inputDf[TMP_STRATEGY])

# put changed campaigns into outputDf; if none, prepare empty outputDf
if sum(changed) > 0:
  print("== Campaigns with Changed Strategy ==", tableize(inputDf.loc[changed]))

  # only select changed rows
  cols = [RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN, TMP_STRATEGY]
  outputDf = inputDf.loc[ changed, cols ].copy() \
                    .rename(columns = { \
                      TMP_STRATEGY: BULK_COL_STRATEGY \
                    })
  print("outputDf", tableize(outputDf))

else:
  print("Empty outputDf")
  outputDf = outputDf.iloc[0:0]

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

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