Script 1199: Tag Performance of Meta Campaigns

Purpose:

The Python script tags Meta campaign dimensions based on performance metrics to determine if they are “winning” or “losing.”

To Elaborate

The script is designed to evaluate the performance of Meta campaigns by tagging them as “winning” or “losing” based on specific business rules. These rules are applied to three dimensions: Reach, Traffic, and Conversion. Each dimension has its own set of criteria based on campaign names and performance metrics such as eCPM (effective cost per thousand impressions) and CTR (click-through rate). The script processes campaign data, applies the rules, and updates the campaign tags accordingly. This helps in identifying which campaigns are performing well and which are not, allowing for better decision-making and optimization of marketing strategies.

Walking Through the Code

Local Mode Configuration:
- The script begins by setting up configurations for local execution, including paths for downloading and loading data.
- It checks if the script is running on a server or locally, and loads data from a pickle file if running locally.
Data Initialization:
- The script loads the primary data source into a DataFrame and sets up initial output columns for Reach, Traffic, and Conversion with placeholder values.
Business Rules Definition:
- Business rules are defined in a list of dictionaries, specifying keywords, metrics, thresholds, and dimension tags for determining campaign performance.
Data Processing:
- The script iterates over each business rule, applying conditions to filter campaigns based on keywords and performance metrics.
- It updates the DataFrame with “winning” or “losing” tags based on whether the campaign metrics meet the defined thresholds.
Output Generation:
- The script uses a utility function to select and compare changed data, preparing the final output DataFrame.
- If running locally, it writes the output and debug information to CSV files for further analysis.

Vitals

Script ID : 1199
Client ID / Customer ID: 1306920543 / 60268855
Action Type: Bulk Upload
Item Changed: Campaign
Output Columns: Account, Campaign, Reach, Conversion, Traffic
Linked Datasource: M1 Report
Reference Datasource: None
Owner: Michael Huang (mhuang@marinsoftware.com)
Created by Michael Huang on 2024-06-19 17:38
Last Updated by afarrokhi@marinsoftware.com on 2024-07-10 18:51

> See it in Action

Python Code

##
## name: Tag Performance of Meta Campaigns
## description: Tags the following Marin Dimensions based on performance
##  - Dimensions: Reach, Traffic, Conversion
##  
## 
## author: Michael S. Huang
## created: 2024-06-19
## 


########### START - Local Mode Config ###########
# Step 1: Uncomment download_preview_input flag and run Preview successfully with the Datasources you want
download_preview_input=False
# Step 2: In MarinOne, go to Scripts -> Preview -> Logs, download 'dataSourceDict' pickle file, and update pickle_path below
# pickle_path = ''
pickle_path = '/Users/mhuang/Downloads/pickle/windsor_fashions_meta_campaign_tagging_20240623.pkl'
# Step 3: Copy this script into local IDE with Python virtual env loaded with pandas and numpy.
# Step 4: Run locally with below code to init dataSourceDict

# determine if code is running on server or locally
def is_executing_on_server():
    try:
        # Attempt to access a known restricted builtin
        dict_items = dataSourceDict.items()
        return True
    except NameError:
        # NameError: dataSourceDict object is missing (indicating not on server)
        return False

local_dev = False

if is_executing_on_server():
    print("Code is executing on server. Skip init.")
elif len(pickle_path) > 3:
    print("Code is NOT executing on server. Doing init.")
    local_dev = True
    # load dataSourceDict via pickled file
    import pickle
    dataSourceDict = pickle.load(open(pickle_path, 'rb'))

    # print shape and first 5 rows for each entry in dataSourceDict
    for key, value in dataSourceDict.items():
        print(f"Shape of dataSourceDict[{key}]: {value.shape}")
        # print(f"First 5 rows of dataSourceDict[{key}]:\n{value.head(5)}")

    # set outputDf same as inputDf
    inputDf = dataSourceDict["1"]
    outputDf = inputDf.copy()

    # setup timezone
    import datetime
    # Chicago Timezone is GMT-5. Adjust as needed.
    CLIENT_TIMEZONE = datetime.timezone(datetime.timedelta(hours=-5))

    # import pandas
    import pandas as pd
    import numpy as np

    # other imports
    import re
    import urllib

    # import Marin util functions
    from marin_scripts_utils import tableize, select_changed

    # pandas settings
    pd.set_option('display.max_columns', None)  # Display all columns
    pd.set_option('display.max_colwidth', None)  # Display full content of each column

else:
    print("Running locally but no pickle path defined. dataSourceDict not loaded.")
    exit(1)
########### END - Local Mode Setup ###########

today = datetime.datetime.now(CLIENT_TIMEZONE).date()

# primary data source and columns
inputDf = dataSourceDict["1"]
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_PUBLISHER = 'Publisher'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_CAMPAIGN_TYPE = 'Campaign Type'
RPT_COL_CAMPAIGN_STATUS = 'Campaign Status'
RPT_COL_IMPR = 'Impr.'
RPT_COL_CLICKS = 'Clicks'
RPT_COL_PUB_COST = 'Pub. Cost $'
RPT_COL_ECPM = 'eCPM $'
RPT_COL_CTR = 'CTR %'
RPT_COL_REACH = 'Reach'
RPT_COL_TRAFFIC = 'Traffic'
RPT_COL_CONVERSION = 'Conversion'

# output columns and initial values
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_REACH = 'Reach'
BULK_COL_TRAFFIC = 'Traffic'
BULK_COL_CONVERSION = 'Conversion'
outputDf[BULK_COL_REACH] = "<<YOUR VALUE>>"
outputDf[BULK_COL_TRAFFIC] = "<<YOUR VALUE>>"
outputDf[BULK_COL_CONVERSION] = "<<YOUR VALUE>>"

# user code starts here

### Business Rules as Configurable Params

# Define the business rules in a dictionary
business_rules = [
    # 1. “Reach” dimension - campaign names containing “Evergreen” and “Reach” that perform below $2.21 CPM are “winning”, and if performing above $2.21 “losing”
    {
        "keywords": ["Evergreen", "Reach"],
        "metric": RPT_COL_ECPM,
        "threshold": 2.21,
        "dimension": BULK_COL_REACH,
        "value_high": "Losing",
        "value_low": "Winning"
    },
    # 2. “Reach” dimension - campaign names containing “GNO” and “Reach” that perform below $2.21 CPM are “winning”, and if performing above $2.21 “losing”
    {
        "keywords": ["GNO", "Reach"],
        "metric": RPT_COL_ECPM,
        "threshold": 2.21,
        "dimension": BULK_COL_REACH,
        "value_high": "Losing",
        "value_low": "Winning"
    },
    # 3. “Reach” dimension - campaign names containing “Formal” and “Reach” that perform below $2.36 CPM are “winning”, and if performing above $2.36 “losing”
    {
        "keywords": ["Formal", "Reach"],
        "metric": RPT_COL_ECPM,
        "threshold": 2.36,
        "dimension": BULK_COL_REACH,
        "value_high": "Losing",
        "value_low": "Winning"
    },
    # 4. “Traffic” dimension - campaign names containing “Evergreen” and “Traffic” that perform below 0.62% CTR are “losing”, and if performing above 0.62% “winning”
    {
        "keywords": ["Evergreen", "Traffic"],
        "metric": RPT_COL_CTR,
        "threshold": 0.0062,
        "dimension": BULK_COL_TRAFFIC,
        "value_high": "Winning",
        "value_low": "Losing"
    },
    # 5. “Traffic” dimension - campaign names containing “GNO” and “Traffic” that perform below 0.55% CTR are “losing”, and if performing above 0.55% CTR “winning”
    {
        "keywords": ["GNO", "Traffic"],
        "metric": RPT_COL_CTR,
        "threshold": 0.0055,
        "dimension": BULK_COL_TRAFFIC,
        "value_high": "Winning",
        "value_low": "Losing"
    },
    # 6. “Traffic” dimension - campaign names containing “Formal” and “Traffic” that perform below 0.77% CTR are “losing”, and if performing above 0.77% CTR “winning”
    {
        "keywords": ["Formal", "Traffic"],
        "metric": RPT_COL_CTR,
        "threshold": 0.0077,
        "dimension": BULK_COL_TRAFFIC,
        "value_high": "Winning",
        "value_low": "Losing"
    },
    # 7. “Conversion” dimension - campaign names containing “Evergreen” and “Conv” that perform below 0.33% CTR are “losing”, and if performing above 0.33% “winning”
    {
        "keywords": ["Evergreen", "Conv"],
        "metric": RPT_COL_CTR,
        "threshold": 0.0033,
        "dimension": BULK_COL_CONVERSION,
        "value_high": "Winning",
        "value_low": "Losing"
    },
    # 8. “Conversion” dimension - campaign names containing “GNO” and “Conv” that perform below 0.27% CTR are “losing”, and if performing above 0.27% “winning”
    {
        "keywords": ["GNO", "Conv"],
        "metric": RPT_COL_CTR,
        "threshold": 0.0027,
        "dimension": BULK_COL_CONVERSION,
        "value_high": "Winning",
        "value_low": "Losing"
    },
    # 9. “Conversion” dimension - campaign names containing “Formal” and “Conv” that perform below 0.45% CTR are “losing”, and if performing above 0.45% “winning”
    {
        "keywords": ["Formal", "Conv"],
        "metric": RPT_COL_CTR,
        "threshold": 0.0045,
        "dimension": BULK_COL_CONVERSION,
        "value_high": "Winning",
        "value_low": "Losing"
    }
]

print("inputDf.shape", inputDf.shape)
print("inputDf.dtypes", inputDf.dtypes)
print("inputDf sample", inputDf.head())

originalDf = inputDf.copy()

# Apply the business rules to set dimensions
for rule in business_rules:
    # Build the condition to filter DataFrame based on all keywords
    condition = inputDf[RPT_COL_CAMPAIGN].apply(lambda x: all(keyword in x for keyword in rule["keywords"]))

    # Apply the rule based on the metric threshold
    inputDf.loc[condition & (inputDf[rule["metric"]] > rule["threshold"]), rule["dimension"]] = rule["value_high"]
    inputDf.loc[condition & (inputDf[rule["metric"]] <= rule["threshold"]), rule["dimension"]] = rule["value_low"]

outputDf, debugDf = select_changed(inputDf, 
                                   originalDf,
                                   diff_cols = [RPT_COL_REACH, RPT_COL_TRAFFIC, RPT_COL_CONVERSION],
                                   select_cols = [RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN, RPT_COL_REACH, RPT_COL_TRAFFIC, RPT_COL_CONVERSION],
                                   merged_cols = [RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN]
                                   )

print("outputDf.shape", outputDf.shape)

### local debug

if local_dev:
    output_filename = 'outputDf.csv'
    outputDf.to_csv(output_filename, index=False)
    print(f"Local Dev: Output written to: {output_filename}")

    debug_filename = 'debugDf.csv'
    debugDf.to_csv(debug_filename, index=False)
    print(f"Local Dev: Debug written to: {debug_filename}")

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

19 Jun 2024

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