Attribution Modeling Advanced Techniques

Introduction

Attribution modeling is a critical aspect of user behavior analytics. It helps marketers understand how different touchpoints contribute to conversions. This lesson will explore advanced techniques in attribution modeling that provide deeper insights into user behavior.

Key Concepts

• Attribution: The process of assigning credit for conversions to various marketing channels.
• Last Click Attribution: Giving all credit to the last touchpoint before conversion.
• Multi-Touch Attribution: Distributing credit across multiple touchpoints in the customer journey.
• Linear Attribution: Equal credit to all touchpoints.
• Time Decay Attribution: More credit to touchpoints closer to the conversion.

Advanced Techniques

1. Algorithmic Attribution

Algorithmic attribution uses statistical models to analyze user interactions and allocate credit based on impact on conversion.

Example Code for Algorithmic Attribution

# Python Example for Algorithmic Attribution
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression

# Load your data
data = pd.read_csv('touchpoints.csv')

# Prepare your features and target variable
X = data[['touchpoint1', 'touchpoint2', 'touchpoint3']]
y = data['conversion']

# Create and train the model
model = LinearRegression()
model.fit(X, y)

# Predict contributions
data['contribution'] = model.predict(X)
print(data[['touchpoint1', 'touchpoint2', 'touchpoint3', 'contribution']])
                

2. Shapley Value Attribution

Shapley value attribution is based on cooperative game theory, distributing credit based on the contribution of each channel in various combinations.

Flowchart of Shapley Value Calculation

graph TB
    A[Start] --> B[Identify touchpoints]
    B --> C[Calculate contributions]
    C --> D{Is combination complete?}
    D -- Yes --> E[Distribute credit]
    D -- No --> B
    E --> F[End]
                

Best Practices

• Utilize a mix of attribution models for comprehensive insights.
• Continuously test and validate your models against actual conversion data.
• Incorporate qualitative data from user surveys to complement quantitative findings.
• Regularly update your models to reflect changes in user behavior and marketing strategies.