Travel

ML-Driven Marketing Optimization

Challenge

A travel platform needed to optimize marketing spend across channels to improve ROI in a highly competitive market.

Solution

We developed a predictive marketing attribution model that analyzed customer journey data across multiple touchpoints to determine the true impact of each marketing channel on conversions.

Results

The platform achieved significant improvement in marketing ROI by reallocating budget to high-performing channels and optimizing campaign timing. Conversion rates increased substantially while maintaining the same overall marketing budget.

Key Metrics

15%
ROI Improvement
22%
Conversion Increase
8
Channels Analyzed
3M+
Customer Journeys

Project Overview

This marketing optimization project helps a major travel platform allocate its marketing budget more effectively across channels, improving return on investment while growing conversions.

Technical Solution

Approach

Our solution addressed several key challenges in marketing attribution:

  1. Multi-touch attribution model that considers all customer touchpoints
  2. Time-decay modeling that properly weighs the importance of each interaction
  3. Cross-device tracking to follow customers across different platforms
  4. Incrementality testing to measure true causal impact

Data Integration

We integrated data from multiple sources including:

  • Google Analytics and Google Ads
  • Facebook Ads Manager
  • Email marketing platforms
  • Mobile app analytics
  • CRM data
  • Website behavior tracking

ML Model Development

We built several complementary models:

  • Markov chain models to understand customer journey paths
  • Gradient boosting models to predict conversion probability
  • Uplift modeling to determine incremental impact
  • Budget allocation optimization using reinforcement learning

Implementation Challenges

Key challenges we overcame included:

  • Integrating disparate data sources with inconsistent identifiers
  • Handling attribution across long customer journeys (up to 90 days)
  • Accounting for seasonality and external market factors
  • Creating actionable dashboards for non-technical marketing teams

Business Impact

The solution delivered:

  • Double-digit improvement in overall marketing ROI
  • Significant increase in conversion rates
  • More effective budget allocation across channels
  • Data-driven insights for content optimization
  • Automated reporting that saved significant time each week

Technology Stack

  • Python with scikit-learn and TensorFlow
  • Google BigQuery for data warehousing
  • Airflow for orchestration
  • Custom dashboards built with Plotly and Dash
  • Automated A/B testing framework

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