Purpose:

The Python script adjusts the “FullFunnel” strategy for marketing campaigns based on specific ratio criteria.

To Elaborate

The script is designed to update the “FullFunnel” strategy for marketing campaigns by evaluating a specific ratio associated with each campaign. It applies a set of business rules to determine how the “FullFunnel” value should be adjusted. The script processes input data, applies conditions to modify the “FullFunnel” strategy based on the ratio, and outputs only those campaigns where the strategy has changed. This ensures that only campaigns requiring updates are included in the final output, optimizing the allocation of marketing resources.

Walking Through the Code

1. Initialization and Setup
   • The script begins by defining constants for column names used in the input and output data frames.
   • It initializes a temporary field in the input data frame to store new “FullFunnel” values.
2. Applying Business Rules
   • The script applies several conditions to the input data:
     • If the ratio is 0, the “FullFunnel” value is set to -10.
     • If the ratio is greater than 0.81, it is set to -5.
     • If the ratio is less than 0.72 and not 0, it is set to 5.
     • If the ratio is between 0.72 and 0.81, the existing “FullFunnel” value is retained.
3. Updating and Filtering Output
   • The new “FullFunnel” values are copied to the output data frame.
   • The output data frame is filtered to include only those campaigns where the “FullFunnel” strategy has changed, ensuring efficient updates.

Vitals

• Script ID : 229
• Client ID / Customer ID: 1306922915 / 60269133
• Action Type: Bulk Upload
• Item Changed: Keyword
• Output Columns: Account, Campaign, Group, Keyword, Match Type, FullFunnel
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: Jonathan Reichl (jreichl@marinsoftware.com)
• Created by Jonathan Reichl on 2023-06-30 09:39
• Last Updated by Anton Antonov on 2024-01-05 11:27

> See it in Action

Python Code

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RPT_COL_KEYWORD = 'Keyword'
RPT_COL_MATCH_TYPE = 'Match Type'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_GROUP = 'Group'
RPT_COL_RATIO = 'Ratio'
RPT_COL_FULLFUNNEL = 'FullFunnel'
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_GROUP = 'Group'
BULK_COL_KEYWORD = 'Keyword'
BULK_COL_MATCH_TYPE = 'Match Type'
BULK_COL_FULLFUNNEL = 'FullFunnel'

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

today = datetime.datetime.now(CLIENT_TIMEZONE).date()
print(tableize(inputDf))

TMP_FIELD = BULK_COL_FULLFUNNEL + '_new'
# blank out tmp field
inputDf[TMP_FIELD] = numpy.nan

inputDf.loc[ (inputDf[RPT_COL_RATIO] == 0 ) , TMP_FIELD ] = '-10'
inputDf.loc[ (inputDf[RPT_COL_RATIO] > 0.81 ) , TMP_FIELD ] = '-5'
inputDf.loc[ ((inputDf[RPT_COL_RATIO] < 0.72 )  & (inputDf[RPT_COL_RATIO] != 0 ) ), TMP_FIELD ] = '5'
inputDf.loc[ ((inputDf[RPT_COL_RATIO] >= 0.72 ) & (inputDf[RPT_COL_RATIO] <= 0.81 )  ), TMP_FIELD ] = inputDf[RPT_COL_FULLFUNNEL]
# copy new strategy to output
outputDf.loc[:,BULK_COL_FULLFUNNEL] = inputDf.loc[:, TMP_FIELD]

# only include campaigns with changed strategy in bulk file
outputDf = outputDf[  (inputDf[RPT_COL_FULLFUNNEL] != inputDf[TMP_FIELD]) ]

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