Gulmammadova & Mukherjee - RNNs for data-driven multi-touch attribution at Booking.com

Booking.com uses Recurrent Neural Networks (RNNs), specifically LSTMs, for data-driven multi-touch attribution (DDA) modeling to understand marketing channel effectiveness

The model treats marketing customer journeys (MCJs) like sentences, with touchpoints as tokens and uses attention mechanisms to determine attribution weights

Key goals of DDA implementation:

• Move attribution credit from lower funnel to upper funnel channels
• Increase total conversions without harming ROI
• Better understand marketing channel contributions
• Make more efficient bidding decisions

Model architecture:

• Embeds touchpoint features into vectors
• Uses LSTM layers to process sequential touchpoint data
• Applies attention mechanism to calculate attribution weights
• Outputs conversion probability through sigmoid layer

Experimentation strategy:

• Two-level experimentation approach
• Geo-level experiments to measure total market impact
• A/B tests to validate model performance
• Control groups use business-as-usual attribution rules

Implementation challenges:

• Lack of ground truth for attribution
• Data quality issues with partner reporting
• Need to balance statistical power with dilution effects
• Complex bidding landscape across marketing channels

Benefits of the approach:

• Operational simplicity
• Easy interpretation through attention weights
• Flexible implementation across channels
• Ability to incorporate user characteristics
• Compatible with existing bidding systems