Purpose:

The Python script tags campaigns based on their performance metrics compared to predefined benchmarks.

To Elaborate

The 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, categorizing them as “Over Target,” “Under Target,” or “On Target” based on how their metrics compare to the benchmarks. Additionally, it calculates the performance ratio of each metric to its corresponding benchmark, providing a quantitative measure of performance. This analysis helps in identifying campaigns that are performing well or need improvement, thereby aiding in strategic decision-making for marketing efforts.

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 results, specifically the account, campaign, and performance tag columns.
2. Benchmark Definition
   • A benchmark value for the cost per conversion is defined, which is a user-changeable parameter. This value is used to evaluate campaign performance.
3. Performance Tagging Function
   • A function tag_performance is defined to compare a given metric against its benchmark. It returns a tag indicating whether the metric is “On Target,” “Over Target,” or “Under Target.”
4. Performance Calculation and Tagging
   • The script calculates the performance ratio for the cost per conversion by dividing the actual metric by the benchmark.
   • 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 performance tag is assigned to the output DataFrame, which is then printed to verify the results. This output helps in understanding which campaigns meet, exceed, or fall short of the benchmarks.

Vitals

• Script ID : 1505
• Client ID / Customer ID: 1306928469 / 60270543
• Action Type: Bulk Upload (Preview)
• Item Changed: Campaign
• Output Columns: Account, Campaign, Performance Tag
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: emerryfield@marinsoftware.com (emerryfield@marinsoftware.com)
• Created by emerryfield@marinsoftware.com on 2024-11-08 23:57
• Last Updated by emerryfield@marinsoftware.com on 2024-11-08 23:57

> 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