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Florent Buisson holds a PhD in behavioral economics, with a strong foundation in econometrics. He transitioned from academic economics to business, starting in management consulting before moving to analytics and experimentation in the U.S. He has led behavioral science and experimentation teams at companies like Cars.com and Allstate, working with A/B testing, behavioral data, and product management.
Some Key Takeaways:
1. The discussed Causal Behavioral Framework combines behavioral science, data analysis, and causal inference to drive better business decision-making at AllState and Cars.com.
2. The outputs of the causal framework often include intermediary work products like causal diagrams, which clarify the relationships between customer characteristics, behaviors, and outcomes. Implementing these frameworks require collaboration across teams, ensuring stakeholders align on goals and understand the relationships between variables before data collection.
3. Causal diagrams are a powerful starting point for experimentation, helping teams visualize cause-and-effect relationships and avoid confusion with correlational data.
4. Distinguishing between causal and correlational metrics is critical. Florent emphasizes that relying on correlations, such as increased website logins correlating with higher purchases, can lead to false assumptions. The causal framework can help identify whether one action actually causes the other, helping to avoid misguided business decisions.
5. NPS (Net Promoter Score), as Florent suggests, should be viewed as just one piece of the puzzle. It is not always a strong predictor of actual customer behavior and works best when combined with other data points.
In this episode, we cover:
(00:04) Introduction To Florent Buisson And His Background
(04:10) Overview Of The Causal Behavioral Framework
(09:22) Understanding Outputs of the Causal Framework
(15:21) Considerations for Implementing a Causal framework
(19:06) Getting Started with Causal Diagrams
(26:01) Data Traits and Their Impact on User Behaviors
(32:37) Causal vs Correlational Metrics
(37:19) Trends in Experimentation
(39:24) Resources, Recommendations and Final Thoughts
Referenced:
Florent’s O’Reilly book, which was the basis for much of today’s discussion (particularly Chapters 1 and 2): Behavioral Data Analysis with R and Python: Customer-Driven Data for Real Business Results
Florent’s Adobe Target Book: Getting Started with Adobe Target: a Gentle Introduction for Web Analysts
Whenever you're ready, there are 4 ways I can help you:
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4. EXO Field Notes Participation: Take your thought leadership to the next level with one of three formats: 1:1 interviews with leaders of top experimentation programs, micro-interviews for those beginning their thought leadership journey, and roundtable discussions reserved for groups of 2 or 3 established in-house brands seeking peer-to-peer insights. Interested? Send me a message on LinkedIn to inquire about a future opportunity.
About the Host
I’m Brian Poe, Founder and Chief Optimization Officer of Mammoth Insights, where we specialize in helping companies enhance their digital strategies through Conversion Rate Optimization (CRO) and experimentation. With over 15 years of personal experience in the marketing industry, we have a deep passion for uncovering hidden opportunities in data to drive significant improvements in overall business performance. At Mammoth Insights, we offer affordable CRO services, including quant/qual research, market analysis, UX and copy research, A/B testing, and more, tailored specifically for mid-sized to enterprise companies. Our mission is to make complex CRO concepts easy to understand and implement at an affordable price without sacrificing expertise or quality.
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