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 evaluates each campaign based on its name, creation date, and the number of clicks it has accumulated. The script uses specific tokens in the campaign name to determine if a campaign is exclusive or non-exclusive. It then applies business rules to assign a strategy: campaigns with certain tokens are assigned to specific strategies, campaigns older than 14 days with sufficient clicks are reassigned to a different strategy, and campaigns older than 60 days are moved to an evergreen strategy. This structured approach ensures that campaigns are managed efficiently and aligned with business objectives.

Walking Through the Code

  1. Initialization and Setup
    • The script begins by defining constants for column names and initializes the strategy column in the output DataFrame with a placeholder value.
    • It sets up strategy values and campaign name tokens that will be used for matching and assignment.
  2. Rule-Based Strategy Assignment
    • Rule 1: Campaigns containing specific tokens in their names are assigned to either an exclusive or non-exclusive strategy. This is done using case-insensitive string matching.
    • Rule 2: Campaigns that are either exclusive or non-exclusive, created more than 14 days ago, and have accumulated at least 100 clicks are reassigned to a “CPA BAU” strategy.
    • Rule 3: Campaigns created more than 60 days ago are assigned to a “CPA Evergreen” strategy.
  3. Identifying and Processing Changes
    • The script identifies campaigns whose strategy has changed based on the new assignments.
    • It prepares an output DataFrame containing only the campaigns with changed strategies, ensuring that only relevant updates are processed. 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 2024-11-27 06:58:46 GMT

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