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CASE STUDY

Automating and Enhancing Multi-Channel Attribution for Optimized Customer Journey Insights

AT A GLANCE

The Markov Chain Attribution Model (MAM) was enhanced to support an extended list of channels, automate monthly processing, and integrate seamlessly with Snowflake. These updates reduced manual data handling by 50%, improved processing speed by 20%, and ensured 100% accuracy, providing real-time attribution insights to optimize campaigns and drive better decisions.

Client information

Company Name
Media Publishing Company
location
Canada
SIZE
Enterprise
INDUSTRY
Media & Publishing
Services Provided
Attribution Modeling Optimization
50%
Reduction in time-to-insight, empowering the marketing team to optimize campaigns dynamically
20%
Improvement in monthly processing speed, significantly enhancing operational efficiency, ensuring timely analysis of all conversion types and historical periods
50%
Reduction in manual data handling time, enabling the analytics team to easily create Tableau visualizations and focus on deriving actionable insights.

CHALLENGE

The client needed to enhance their Markov Chain Attribution Model to efficiently analyze customer journeys, predict channel-specific conversions, and assess channel removal impacts, addressing inefficiencies in the existing manual process.

SOLUTION

The Markov Chain Attribution Model (MAM) was enhanced to support an extended list of channels, including detailed breakdowns for email, paid search, and internal content, with data extracted from Snowflake to build customer journeys. Automated scripts enabled monthly execution across conversion types and historical data ranges on an EC2 server. Predictions were formatted for direct upload into Snowflake, ensuring seamless integration and efficient processing.

IMPACT

  • Extended Attribution Modeling: The enhanced model processed predictions for an extended list of marketing channels, adding granularity and improving attribution insights across a broader range of touchpoints, empowering the marketing team to optimize campaigns dynamically and reducing time-to-insight by 50%.
  • Automated Monthly Process: Automation improved monthly processing speed by 20%, ensuring all combinations of conversion types and historical periods were processed efficiently. Data validation ensured 100% accuracy in initial outputs, providing reliable results for analysis.
  • Improved Data Flow: Integration with Snowflake reduced manual data handling time by 50% and streamlined workflows, enabling the analytics team to easily create Tableau visualizations and focus on deriving actionable insights.

TOOLS

Snowflake
AWS EC2
Tableau
Apache Airflow
Pandas

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