Show notes
Suresh Pillai is a theoretical physicist by training and the Vice President of Data at Beat. Beat is an information and technology services company that created a ride-hailing and taxi mobile app. Beat claims to be the fastest growing app in Latin America (p.s. they’re hiring).Questions Suresh Answered in this Episode:How do you approach mobile data science from your theoretical physics perspective?How have you used uplift modeling or incrementality?Define propensity in the context of uplift modeling.Can you explain in more detail the marketing settings you never turn off?What is the difference between how people use uplift modeling, incrementally, and other causal machine learning?Do you have any tips for people to make sense of attribution in the complex setting of multi-touch marketing?We’re losing unique identifiers for users with the change to iOS14. What does this change for you? Has it been a problem? And do you think there’s a role for marketing mix models here?What are the most interesting insights you’ve seen from incrementality models? What really surprised you? What changed your view on how customers are acting?Timestamp:0:41 Suresh’s background & complexity science2:14 A physicist’s view of complex systems in mobile data science5:03 The granularity of incrementality and uplift modeling6:05 Sure things, persuadables, lost causes, and sleeping dogs11:31 Uplift modeling when there is no baseline13:31 Uplift vs causal vs attribution models16:48 What people get wrong with multi-touch attribution25:44 Dealing with the challenge of the iOS14 update27:50 The role of marketing mix modeling33:51 Validation: Engaging customers after conversionQuotes:(2:25-2:52) “When you’re thinking about any system, especially a complex system, and you’re given a problem, you need to decide which level of granularity you choose to model and understand that system. So different levels enable different insights, but it’s also a practical thing. If it’s a really complex system it may be too much to understand at the atomic level. What I say is you can’t predict anything at the atomic level because there’s too much going on. And we know this in physics, too.”(24:23-24:35) “When I come to a website, I don’t care what channel I came through. I don’t think about it consciously. There’s no reason to organize how you measure incrementality based on channels. Channels don’t exist. Customers exist.”Mentioned in this Episode:Suresh Pillai’s LinkedInBeat (Psst Beat is hiring)
