Script 121: 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 evaluates each campaign based on its name, creation date, and the number of accumulated clicks. The script applies specific business rules to determine the appropriate strategy for each campaign. These rules include checking for specific tokens in the campaign name to assign initial strategies, adjusting strategies for campaigns older than 14 days with significant clicks, and reassigning strategies for campaigns older than 60 days. This structured approach ensures that campaigns are aligned with the most suitable strategy, optimizing their performance and management.

Walking Through the Code

  1. Initialization and Setup:
    • The script begins by defining constants for column names and strategies.
    • It initializes a temporary column in the input DataFrame to store new strategy assignments.
  2. Rule 1: Strategy Assignment Based on Campaign Name:
    • Campaigns containing “_ Exc” in their name are assigned the “SVOD - VN - Title_Exc-Launch - TIS” strategy.
    • Campaigns with “_ NonExc” in their name are assigned the “SVOD - VN - Title_NonExc-Launch - TIS” strategy.
    • This assignment is case-insensitive.
  3. Rule 2: Strategy Adjustment for Older Campaigns with Clicks:
    • Campaigns with “_ Exc” or “_ NonExc” in their name, created more than 14 days ago, and with at least 100 clicks are reassigned to the “SVOD - VN - Title-BAU - CPS” strategy.
  4. Rule 3: Evergreen Strategy for Very Old Campaigns:
    • Campaigns created more than 60 days ago are assigned the “SVOD - VN - Title-Evergreen - CPS” strategy.
  5. Output Preparation:
    • The script identifies campaigns with changed strategies and prepares an output DataFrame containing only these campaigns.
    • If no changes are detected, an empty output DataFrame is prepared.

Vitals

  • Script ID : 121
  • Client ID / Customer ID: 1306924441 / 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 11:13
  • Last Updated by Jonathan Reichl on 2023-12-13 11:21
> 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 - VN - Title_Exc-Launch - TIS"
VAL_STRATEGY_IS_NON_EXC = "SVOD - VN - Title_NonExc-Launch - TIS"
VAL_STRATEGY_CPA_BAU = "SVOD - VN - Title-BAU - CPS"
VAL_STRATEGY_CPA_EVERGREEN = "SVOD - VN - 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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