Script 109: 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 campaign name, the date the campaign was created, and the number of accumulated clicks to determine the appropriate strategy. The script applies a set of business rules to categorize campaigns into different strategies, ensuring that each campaign is aligned with the most suitable marketing approach. This process helps in optimizing campaign management by systematically updating strategies based on the latest campaign data.

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 values.
    • Strategies are predefined and include options like “SVOD - ID - Title_Exc-Launch - TIS” and “SVOD - ID - Title-Evergreen - CPS”.
  2. Temporary Strategy Assignment
    • A temporary column is created in the input DataFrame to store new strategy assignments. Initially, this column is set to empty.
  3. Rule 1: Strategy Assignment Based on Campaign Name
    • Campaigns are assigned to either an “Exc” or “NonExc” strategy based on tokens found in their names. This assignment is case-insensitive.
  4. Rule 2: Strategy Assignment Based on Creation Date and Clicks
    • Campaigns that are either “Exc” or “NonExc” and were created more than 14 days ago with at least 100 clicks are reassigned to the “CPA BAU” strategy.
  5. Rule 3: Evergreen Strategy Assignment
    • Campaigns created more than 60 days ago are assigned to the “CPA Evergreen” strategy.
  6. Identifying and Outputting Changes
    • The script identifies campaigns whose strategies have changed and prepares an output DataFrame containing only these campaigns. If no changes are detected, an empty DataFrame is prepared.

Vitals

  • Script ID : 109
  • Client ID / Customer ID: 1306924343 / 69058
  • Action Type: Bulk Upload
  • Item Changed: Campaign
  • Output Columns: Account, Campaign, Strategy
  • Linked Datasource: M1 Report
  • Reference Datasource: None
  • Owner: Michael Huang (mhuang@marinsoftware.com)
  • Created by Michael Huang on 2023-05-17 22:18
  • Last Updated by Jonathan Reichl on 2023-12-13 11:18
> 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 - ID - Title_Exc-Launch - TIS"
VAL_STRATEGY_IS_NON_EXC = "SVOD - ID - Title_NonExc-Launch - TIS"
VAL_STRATEGY_CPA_BAU = "SVOD - ID - Title-BAU - CPS"
VAL_STRATEGY_CPA_EVERGREEN = "SVOD - ID - 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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