228: The Machine Learning Reality Check: When AI Makes Sense for Marketing Attribution with Lew Dawson of Momentum Consulting

This week on The Data Stack Show, Eric and John welcome back Lewis Dawson from Momentum Consulting for the third part of this conversation on data attribution. In this episode, the group talks about the intricacies of machine learning (ML) and its business applications, particularly focusing on customer lifetime value (CLV) prediction. They discuss the challenges of implementing ML models, emphasizing the necessity of having the right data and team. The conversation highlights the importance of simplicity in modeling and understanding the limitations of data-driven approaches. They also explore the concept of diminishing returns in marketing channels and the potential of AI in optimizing advertising strategies, while acknowledging the complexities involved.

Highlights from this week’s conversation include:

• Previewing Today’s Topic (0:14

• Machine Learning Discussion (1:44)

• Conditions for Machine Learning Worthiness (6:59)

• Simplicity in Modeling (8:43)

• Predictive Analytics vs. Machine Learning (10:42)

• Optimization and Investment Problems (13:03)

• Practical vs. Theoretical Saturation (15:16)

• Building Relationships with Marketing Teams (19:52)

• Reporting Metrics and Attribution Models (22:49)

• Tracking Common Metrics (25:40)

• Understanding Profit Generation (27:46)

• ROI Calculation Challenges (29:49)

• AI in Customer Data Analysis (31:23)

• Execution Challenges in Marketing Automation (35:23)

• API Limitations in Marketing Tools (36:50)

• Optimizing Ads with AI (37:50)

• Challenges of Real-Time Data Integration (40:01)

• Final thoughts and takeaways (41:13)

The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we’ll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.

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