Advanced Modeling for Enhanced Campaign Targeting: Attributing Advertising Impact on Revenue Through AI
How a leading technology brand utilized advanced AI modeling to uncover revenue-driving marketing efforts and enhance overall campaign performance
Our Client’s Challenge
A globally recognized leader in consumer electronics, our client is committed to driving new revenue streams through digital transformation. However, their service monetization team found it difficult to accurately measure the causal impact of specific marketing segments on revenue.
Traditional measurement methods made it difficult to isolate the true effect of marketing interventions from other influencing factors, limiting their ability to effectively allocate budgets and optimize campaign performance.
Key Objectives
- Enhance Visibility into Marketing Segment Attributions: Provide deeper insight into which marketing segments and interventions genuinely drive revenue.
- Improve Revenue Contributions and Overall Campaign Performance: Accurately quantify the causal impact of marketing initiatives, thereby informing budget allocation and strategic planning.
- Establish a Comprehensive Causal Framework: Develop a structured approach to causal analysis that can be extended to various treatments and segments across the organization.
The AI COLLABORATOR Solution
AI Collaborator addressed these challenges with a holistic causal inference strategy and refined machine learning techniques, delivering a robust framework for improved targeting and attribution:
Propensity Scoring Refinement
- Upgraded the approach for identifying and prioritizing likely responders by moving beyond traditional methods and using more sophisticated AI-based techniques.
- Significantly improved the accuracy of identifying high-probability responders, allowing for better audience segmentation.
Holistic AI Approach
- Built a flexible structure that can be used to analyze different types of marketing interventions and results.
- Packaged the refined model in a reusable format, ensuring consistent and scalable adoption for various marketing analyses.
Adjusting for Large Spenders
- Introduced steps to prevent big spenders from skewing the results, ensuring findings accurately reflect the broader audience.
- Mitigated skewed results caused by small subsets of users with unusually large spending patterns.
Time-Weighted Causal Effect Measurement
- Incorporated time as a variable to observe how post-treatment effects evolve and their correlation with revenue-based treatment effects.
- Enabled more nuanced performance insights and better targeting decisions by analyzing short-term vs. long-term impact.
Our Client’s Results
By leveraging advanced causal inference methods and a refined machine learning infrastructure, the client significantly enhanced its ability to measure and optimize advertising impact on revenue.
Key Achievements
- Improved Propensity Scoring: Achieved a 2.66x improvement in classification scores compared to the original logistic regression approach, leading to more accurate segmentation and targeting.
- Enhanced ATT Estimation: Adjusting for outliers reduced the average treatment effect by 50% for top spenders, uncovering the diverse spending behaviors within the audience.
- Streamlined Code Packaging: Documented and packaged the causal ML framework for future application, enabling faster, broader deployment across various marketing efforts.
Through this data-driven transformation, the client gained a deeper understanding of how different treatments influence revenue, empowering more strategic and cost-effective decision-making.
Client Testimonial
“The AIC team has done an outstanding job supporting us. Their team displayed the technical expertise, strong collaboration ethos, and thought leadership that have been invaluable to the success of this pioneering project.”
— Global Head of Marketing Insights & Analytics
