In this article, we describe an approach that builds one Markov model for each feature (e.g. region, segment, time to conversion), and combines them into a final, context-aware attribution score.

🧭 Why Context Matters in Attribution​

A typical attribution model might give you this insight:

“Email drives 18% of conversions.”

But is that true across all users? Maybe it’s 30% for returning customers, and just 5% for new users.
If we don’t consider context, we miss important patterns.

🧍 Comparison of Two User Journeys​

User A (New, from UK):
→ Paid Search → Social → Conversion

User B (Returning, from Italy):
→ Paid Search → Social → Conversion

🟡 Both users took the same path—but their behavior and background are different. Treating them the same reduces model accuracy.

🧮 Step-by-Step: How Context-Aware Attribution Works​

1. Start with Enriched Path Data​

Your journey data includes more than just channels:

You might have:

• Categorical features: region, segment, position
• Numerical features: seconds to last touch

2. Discretize Numerical Features​

Before modeling, numerical variables like seconds_to_last_touch are binned to turn them into categorical values:

3. Create Artificial Channels from Each Feature​

We now create “channel + feature” combinations to isolate feature-specific behavior.
Example using region:

Original Path:
Email → Social → Conversion

Transformed Paths:
Email_UK → Social_UK → Conversion

🔁 We now have multiple transformed views of the same path, customized per feature.

4. Fit One Markov Model per Feature​

Each artificial channel is modeled independently.
For example, for the feature region, we fit a Markov model using artificial channels:

• Email_UK → Social_UK → Conversion
• PaidSearch_Italy → Direct_Italy → Conversion
• Email_FR → Social_FR → Conversion
  ...

From each model, we compute the **odds ** for each channel:

These odds quantify how strongly a channels contribute to conversion, and are used to assign attribution credit along the path.

5. Evaluate Model Performance​

Each feature-specific model is evaluated using AUC-PR (Area Under the Precision-Recall Curve)—a better choice than ROC-AUC for imbalanced datasets (few conversions vs. many non-conversions).

We assign a weight to each model based on how much it improves prediction compared to a baseline (the overall conversion rate):

Where:

• AUC-PR is the precision-recall performance of the model
• CR is the global conversion rate in the dataset

Only models that outperform the baseline are retained.

6. Final Attribution = Weighted Mean​

The final attribution score is computed as a weighted average of the feature-specific models.

Each model contributes in proportion to its performance:

Example:

• weight(Region) = 0.25
• weight(Segment) = 0.38
• weight(Seconds to Last Touch) = 0.12
• weight(Position) = 0.22

The final attribution score for a channel aggregates contributions across all relevant features.

✅ Why This Works​

• 👁️ You gain multiple perspectives on customer behavior
• 🎯 You only keep models that improve predictive accuracy
• ⚖️ You combine models in a data-driven way—not based on guesswork or static rules

💡 When to Use This Approach​

This method is especially useful when:

• You have rich journey data with structured features
• You need transaction-level attribution
• You want interpretable results
• You want to understand how attribution shifts across audience segments

📚 Learn More​

If you'd like to see how this approach works in practice, visit our documentation or contact us to discuss your use case.