Check out this Python script for comparing first-touch vs. linear model outputs.

I've been conducting an internal analysis for a client to quantify the practical impact of attribution model selection on channel budget allocation. While most platforms offer a toggle between models, they often lack the transparency needed to understand the exact algorithmic transformation. To address this, I built a Python script that simulates a basic attribution scenario, allowing for a side-by-side comparison of first-touch and linear model outputs from the same raw journey data.

The core value lies in the reproducibility and the explicit mapping of touchpoints to credit. The script ingests a simple dataset of user journeys, where each journey is a time-ordered list of marketing channels leading to a conversion. The key is that the fractional attribution is calculated from the ground up, so you can trace exactly how a dollar of conversion credit gets distributed.

```python
import pandas as pd
from collections import defaultdict

def calculate_attribution(journeys_df, model='linear'):
"""
Calculate channel attribution for a list of user journeys.
journeys_df expects columns: ['user_id', 'journey', 'conversion_value']
where 'journey' is a list of channel strings (e.g., ['Organic', 'Direct', 'Email'])
"""
channel_credit = defaultdict(float)

for _, row in journeys_df.iterrows():
channels = row['journey']
value = row['conversion_value']
num_touchpoints = len(channels)

if model == 'first_touch':
# All credit to the first channel in the journey
channel_credit[channels[0]] += value
elif model == 'linear':
# Distribute credit equally across all touchpoints
credit_per_channel = value / num_touchpoints
for channel in channels:
channel_credit[channel] += credit_per_channel
# Additional models (last_touch, time_decay, position_based) can be added here.

# Convert to a sorted DataFrame for comparison
attribution_df = pd.DataFrame(list(channel_credit.items()), columns=['channel', 'attributed_value'])
return attribution_df.sort_values('attributed_value', ascending=False)

# Example Data: Simulated user journeys
example_data = pd.DataFrame({
'user_id': [1, 2, 3, 4],
'journey': [
['Paid Search', 'Direct'],
['Organic Social', 'Email', 'Paid Search', 'Direct'],
['Direct'],
['Organic Search', 'Email', 'Direct']
],
'conversion_value': [100.0, 150.0, 50.0, 75.0]
})

# Calculate and compare
first_touch_results = calculate_attribution(example_data, model='first_touch')
linear_results = calculate_attribution(example_data, model='linear')

print("First-Touch Attribution:")
print(first_touch_results)
print("nLinear Attribution:")
print(linear_results)
```

Running this on even a small sample set reveals significant disparities. In my example data, "Direct" traffic often appears late in the journey. Under a first-touch model, it receives no credit in any journey where another channel initiated the session, making it seem far less valuable. The linear model, by assigning it a fraction in those multi-touch journeys, surfaces a more substantial contribution. This directly impacts ROI calculations; a channel like "Email" that frequently appears in a nurturing, mid-journey position is systematically undervalued by first-touch.

**Next Steps & Considerations for Production Use:**

* **Data Input:** In a real scenario, you would replace the example data with a feed from your analytics pipeline, likely requiring session-level data with a user ID, timestamp, channel grouping, and conversion flag/value.
* **Model Expansion:** The script is structured to easily incorporate other models. Adding `last_touch` or a `time_decay` function (which requires timestamps) follows the same pattern.
* **Scalability:** For large-scale analysis (millions of journeys), you would optimize using vectorized Pandas operations or PySpark, but this logic remains the conceptual foundation.
* **Cookie-less Context:** This simulation approach is inherently based on a user ID. To simulate a cookieless environment, you would need to work with aggregated, probabilistic paths or focus on modeled, panel-based data, which is more complex and beyond this script's deterministic scope.

The primary takeaway is not that one model is superior, but that the choice is a material business decision with concrete numerical outcomes. This script provides a starting point to demystify the "black box" and have data-driven discussions about model suitability for your specific customer journey patterns.

-ck