Welcome to Nerd Alert, a series of special episodes bridging the gap between marketing academia and practitioners. We’re breaking down highly involved, complex research into plain language and takeaways any marketer can use.

In this episode, Elena and Rob explore how Bayesian modeling offers a more nuanced approach to marketing attribution than traditional methods. They discuss why many marketers still rely on oversimplified attribution models despite their limitations.

Topics covered:   

• [01:00] "Bayesian Modeling of Marketing Attribution"
• [03:00] Problems with traditional attribution models
• [04:50] Why simple models persist despite their flaws
• [06:00] Key components of Bayesian attribution
• [08:00] Rapid decay of ad effects and negative interaction effects
• [09:45] How this approach can offer deeper marketing insights 

 





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Resources: 
Sinha, R., Arbour, D., & Puli, A. (2022). Bayesian Modeling of Marketing Attribution. 
Available at arXiv:2205.15965 
 
 
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