Purpose:

The Python script tags campaigns based on their performance metrics compared to predefined benchmarks, assigning tags like “Over Target,” “Under Target,” and “On Target.”

To Elaborate

The Python script is designed to evaluate the performance of marketing campaigns by comparing specific metrics against benchmark values. It processes input data containing various performance metrics such as conversion rate, cost per conversion, click-through rate, and average cost per click. The script then assigns a performance tag to each campaign based on how these metrics compare to the benchmarks. The tags indicate whether a campaign is performing above, below, or exactly at the target level. Additionally, the script calculates the performance ratio for each metric relative to its benchmark, providing a quantitative measure of performance. This helps businesses quickly identify which campaigns are meeting expectations and which require attention or adjustment.

Walking Through the Code

1. Data Preparation
   • The script begins by defining the primary data source and relevant columns from the input data, which include campaign details and performance metrics.
   • It sets up the output DataFrame to store the results, including account and campaign identifiers.
2. Benchmark Definition
   • A benchmark value for the cost per conversion is defined, which serves as the standard against which campaign performance is measured.
3. Performance Tagging Function
   • A function named tag_performance is created to determine the performance tag based on the comparison of a metric to its benchmark. It returns “On Target,” “Over Target,” or “Under Target” depending on the comparison result.
4. Performance Calculation and Tagging
   • The script calculates the performance ratio for the cost per conversion metric by dividing the actual cost by the benchmark value.
   • It applies the tag_performance function to assign a performance tag to each campaign based on the cost per conversion metric.
5. Output Generation
   • The script assigns the performance tag to the output DataFrame and prints the first few rows for verification, allowing users to review the tagged performance of campaigns.

Vitals

• Script ID : 1111
• Client ID / Customer ID: 1306926629 / 60270083
• Action Type: Bulk Upload
• Item Changed: Campaign
• Output Columns: Account, Campaign, Performance Tag
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: dwaidhas@marinsoftware.com (dwaidhas@marinsoftware.com)
• Created by dwaidhas@marinsoftware.com on 2024-05-17 12:11
• Last Updated by dwaidhas@marinsoftware.com on 2024-05-23 20:25

> See it in Action

Python Code

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##
## name: Campaign Benchmarks
## description:
##  This Python script is designed to tag campaigns based on their performance metrics compared to benchmarks. The script takes input data with metrics such as conversion rate, cost per conversion, click-through rate, and average cost per click. It then compares these metrics to benchmark values and assigns a performance tag to each campaign based on the comparison. The performance tags include “Over Target,” “Under Target,” and “On Target.” The script also calculates the performance ratio of each metric to its benchmark.
## 
## author: Dana Waidhas 
## created: 2024-05-17
## 


# Assume CLIENT_TIMEZONE is defined somewhere in your environment
today = datetime.datetime.now(CLIENT_TIMEZONE).date()

# primary data source and columns
inputDf = dataSourceDict["1"]
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_PUBLISHER = 'Publisher'
RPT_COL_CAMPAIGN_TYPE = 'Campaign Type'
RPT_COL_CAMPAIGN_STATUS = 'Campaign Status'
RPT_COL_CONV_RATE = 'Conv. Rate %'
RPT_COL_COST_PER_CONV = 'Cost/Conv. $'
RPT_COL_CTR = 'CTR %'
RPT_COL_AVG_CPC = 'Avg. CPC $'
RPT_COL_IMPR = 'Impr.'
RPT_COL_CLICKS = 'Clicks'
RPT_COL_PUB_COST = 'Pub. Cost $'
RPT_COL_CONV = 'Conv.'
RPT_COL_IMPR_SHARE = 'Impr. share %'

# Output Columns
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_PERFORMANCE_TAG = 'Performance Tag'


# Define benchmark values
benchmark_cost_per_conv = 90.0

# Function to tag performance
def tag_performance(metric, benchmark):
    if metric == benchmark:
        return 'On Target'
    elif metric > benchmark:
        return 'Over Target'
    else:
        return 'Under Target'

# Create output DataFrame
outputDf = pd.DataFrame()
outputDf[BULK_COL_ACCOUNT] = inputDf[RPT_COL_ACCOUNT]
outputDf[BULK_COL_CAMPAIGN] = inputDf[RPT_COL_CAMPAIGN]

# Calculate performance ratio and tag
outputDf['Cost/Conv. Ratio'] = inputDf[RPT_COL_COST_PER_CONV] / benchmark_cost_per_conv
outputDf['Cost/Conv. Tag'] = inputDf[RPT_COL_COST_PER_CONV].apply(tag_performance, args=(benchmark_cost_per_conv,))

# Assign the performance tag based only on Cost/Conv
outputDf[BULK_COL_PERFORMANCE_TAG] = outputDf['Cost/Conv. Tag']

# Print the first few rows of the output DataFrame for verification
print(outputDf.head())

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