Purpose:

The Python script identifies and tags the intent of marketing campaigns based on specific keywords found in campaign names.

To Elaborate

The script is designed to process a dataset of marketing campaigns and determine the intent of each campaign by analyzing its name. It uses a predefined list of keywords to identify whether a campaign is targeting the top, middle, or bottom of the sales funnel, commonly referred to as TOFU (Top of Funnel), MOFU (Middle of Funnel), and BOFU (Bottom of Funnel). The script extracts these intents from the campaign names and updates the dataset accordingly. It ensures that only campaigns with identifiable intents are retained in the final output, thereby providing a filtered view of campaigns with clear marketing objectives.

Walking Through the Code

1. Define Configurable Parameters
   • The script begins by defining a list of keywords (INTENT_KEYWORDS) that represent different stages of the sales funnel: ‘tofu’, ‘mofu’, and ‘bofu’.
   • It also sets up column names for both the report and bulk data, such as RPT_COL_CAMPAIGN and BULK_COL_INTENT.
2. Extract Intent Function
   • A function extract_intent is defined to take a campaign name, convert it to lowercase, and check for the presence of any keywords from INTENT_KEYWORDS.
   • If a keyword is found, it returns the keyword as the intent; otherwise, it returns an empty string.
3. Process Input Data
   • The script copies the input DataFrame (inputDf) to outputDf to preserve the original data.
   • It iterates over each row in inputDf, extracting the campaign name and using the extract_intent function to determine the intent.
4. Update and Filter Data
   • The identified intent is then assigned to the corresponding row in the outputDf under the BULK_COL_INTENT column.
   • Rows with missing intent values are dropped from outputDf, ensuring only campaigns with identified intents are included in the final output.
5. Output Results
   • The script checks if outputDf is empty and prints the results accordingly, providing a table view of the processed data if intents were successfully identified.

Vitals

• Script ID : 1019
• Client ID / Customer ID: 1306927457 / 60270313
• Action Type: Bulk Upload
• Item Changed: Campaign
• Output Columns: Account, Campaign, Intent
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: Autumn Archibald (aarchibald@marinsoftware.com)
• Created by Autumn Archibald on 2024-04-29 19:27
• Last Updated by Autumn Archibald on 2024-04-29 19:28

> See it in Action

Python Code

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# Define the configurable parameters for the script
INTENT_KEYWORDS = ['tofu', 'mofu', 'bofu']
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_ACCOUNT = 'Account'
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_INTENT = 'Intent'

# Function to extract intent from campaign name
def extract_intent(campaign_name):
    campaign_lower = campaign_name.lower()
    for intent in INTENT_KEYWORDS:
        if intent in campaign_lower:
            return intent
    return ''

# Copy input rows to output
outputDf = inputDf.copy()

# Loop through all rows
for index, row in inputDf.iterrows():
    campaign_name = row[RPT_COL_CAMPAIGN]

    # Extract intent from campaign name
    intent = extract_intent(campaign_name)
    print("Campaign [%s] => Intent [%s]" % (campaign_name, intent))

    # Update intent column
    outputDf.at[index, BULK_COL_INTENT] = intent

# Drop any rows with missing intent values
outputDf = outputDf.dropna(subset=[BULK_COL_INTENT])

if not outputDf.empty:
    print("outputDf", tableize(outputDf))
else:
    print("Empty outputDf")

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