Can data scientists foresee the future? – Wrong predictions & their benefits
Can data scientists predict the future? As a data scientist, I know that most predictions are wrong. However, there is some truth to this myth. In “Torch”, our new data science series, I will show you how wrong predictions can still be useful for statistical forecasts.
How you can calculate customer churn using probabilities.
Why these probabilities, which are false at the individual level, are nevertheless valuable overall.
How these probabilities help your company target customers.
Want to see this on a real-life example? In my takeaway, I show you how we used “wrong” assumptions to forecast a company’s customer churn.
Hi there, my name is Sacha, and I am a data scientist at Trivadis – Part of Accenture. I come from the realms of statistics – that is the subject I got my Master’s degree in. Thus, I am interested in inference, optimization methods and business analytics. My heart does not only beat for data science, however: for 9 years, I have played professional tennis, first in France and then in the US. Originally from Paris, I am now based at Lausanne. PS: Did you enjoy my “Torch” episode? Then let’s get in touch – I would love to pass the torch and discuss the world of data science further!
What I share with you
Using «wrong» assumptions to make predictions about the future – that is what my team and I did for a client from the public sector. How we estimated their future customer churn using machine learning and implemented this into a business intelligence solution? In this takeaway, you will find concrete tips for your own customer churn prediction use case.
