Script 149: Pause Keywords Automation

Purpose:

The Python script automates the process of pausing keywords and labeling them with the ‘AutoPause’ dimension based on specific criteria related to creation date, accumulated clicks, and spend.

To Elaborate

The script is designed to manage keyword statuses in a marketing campaign by automatically pausing certain keywords and labeling them with the ‘AutoPause’ dimension. The criteria for pausing a keyword include being created more than 60 days ago, having accumulated at least 100 clicks, and incurring a spend of at least 200 SGD. This automation helps in optimizing the campaign by identifying and pausing underperforming keywords, thereby potentially improving the overall efficiency and effectiveness of the marketing strategy.

Walking Through the Code

  1. Initialization and Setup
    • The script begins by defining constants for various report and bulk column names, which are used throughout the script to reference specific data fields.
    • It sets up placeholders for the new status and ‘AutoPause’ dimension values in the output data frame.
  2. Date Calculation
    • The script calculates the date 60 days prior to the current date to use as a threshold for determining which keywords are eligible for pausing.
  3. Temporary Columns Creation
    • Temporary columns are created in the input data frame to store the new status and ‘AutoPause’ dimension values for keywords that meet the criteria.
  4. Keyword Evaluation
    • The script converts the ‘Creation Date’ column to a date type and evaluates each keyword against the criteria: created more than 60 days ago, clicks greater than or equal to 100, and spend greater than or equal to 200 SGD.
    • Keywords meeting these criteria are marked with a new status of “Paused” and labeled with the current date in the ‘AutoPause’ dimension.
  5. Output Preparation
    • The script identifies keywords whose status has changed and prepares an output data frame containing only these keywords, along with relevant columns.
    • If no keywords meet the criteria, an empty output data frame is prepared.

Vitals

  • Script ID : 149
  • Client ID / Customer ID: 1306924343 / 69058
  • Action Type: Bulk Upload (Preview)
  • Item Changed: Keyword
  • Output Columns: Account, Campaign, Group, Keyword, Match Type, Status, AutoPause
  • Linked Datasource: M1 Report
  • Reference Datasource: None
  • Owner: Jeremy Brown (jbrown@marinsoftware.com)
  • Created by Jeremy Brown on 2023-05-30 10:25
  • Last Updated by Michael Huang on 2023-12-06 04:01
> See it in Action

Python Code

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#
# Pause Keyword and label ONLY paused keywords with the Dimension 'AutoPause' with todays date according to:
#  - Creation Date
#  - Accumulated Clicks
#  - Accumulated Spend
#
# Author: Jeremy Brown
# Date: 2023-05-30

RPT_COL_KEYWORD = 'Keyword'
RPT_COL_MATCHTYPE = 'Match Type'
RPT_COL_GROUP = 'Group'
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_STATUS = 'Status'
RPT_COL_DIM_AUTOPAUSE = 'AutoPause'
RPT_COL_CREATIONDATE = 'Creation Date'
RPT_COL_CLICKS = 'Clicks'
RPT_COL_SPEND = 'Pub. Cost SGD'
RPT_COL_CONVERSIONS = 'Conv.'
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_GROUP = 'Group'
BULK_COL_KEYWORD = 'Keyword'
BULK_COL_MATCHTYPE = 'Match Type'
BULK_COL_STATUS = 'Status'
BULK_COL_DIM_AUTOPAUSE = 'AutoPause'

outputDf[BULK_COL_STATUS] = "<<YOUR VALUE>>"
outputDf[BULK_COL_DIM_AUTOPAUSE] = "<<YOUR VALUE>>"

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

# define new status of keyword
VAL_STATUS_MATCH = "Paused"

# define 'AutoPause' Dimension value to set (todays date)
VAL_DIMENSION_AUTOPAUSE = today

# define tmp column for new Status and set to empty
TMP_STATUS = RPT_COL_STATUS + '_'
inputDf[TMP_STATUS] = np.nan

# define tmp column for dimension 'AutoPause' and set to empty
TMP_AUTOPAUSE = RPT_COL_DIM_AUTOPAUSE + '_'
inputDf[TMP_AUTOPAUSE] = np.nan

# convert Creation Date into Date type
# example: 12/24/2022 5:04 PM
inputDf.loc[:, RPT_COL_CREATIONDATE] = pd.to_datetime(inputDf[RPT_COL_CREATIONDATE], \
                                       format='%m/%d/%Y %H:%M %p')

### Rule: if keyword created more than 60 days ago AND clicks >= 100 AND spend >= 200,
###    pause keyword and label dimension 'AutoPause' with todays date

matched_keywords = ( inputDf[RPT_COL_CREATIONDATE] <= date_60_days_ago) & \
             (inputDf[RPT_COL_SPEND] >= 200) & \
             (inputDf[RPT_COL_CLICKS] >= 100)

inputDf.loc[ matched_keywords, \
             TMP_STATUS \
           ] = VAL_STATUS_MATCH

inputDf.loc[ matched_keywords, \
             TMP_AUTOPAUSE \
           ] = VAL_DIMENSION_AUTOPAUSE


# find changed campaigns
changed = inputDf[TMP_STATUS].notnull() & (inputDf[RPT_COL_STATUS] != inputDf[TMP_STATUS])

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

  # only select changed rows
  cols = [RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN, RPT_COL_GROUP, RPT_COL_KEYWORD, RPT_COL_MATCHTYPE, TMP_AUTOPAUSE, TMP_STATUS]
  outputDf = inputDf.loc[ changed, cols ].copy() \
                    .rename(columns = { \
                      TMP_STATUS: BULK_COL_STATUS, \
                      TMP_AUTOPAUSE: BULK_COL_DIM_AUTOPAUSE \
                    })
  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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