Carl is a former Wall Street Quant turned data scientist who is leading the battle against churn, using data as his weapon.
A data scientist, he uses a variety of tools and techniques to analyze data around online systems, and his expertise has led to the creation of the Subscription Economy Index.
Currently, he’s the Chief Data Scientist at Zuora - a comprehensive subscription management platform and newly public Silicon Valley “unicorn” with more than 1,000 customers worldwide.
FIND CARL ONLINE
Website: https://fightchurnwithdata.com/
LinkedIn: https://www.linkedin.com/in/carlgold/
Twitter: https://twitter.com/carl24k
GitHub: https://github.com/carl24k
WHAT YOU'LL LEARN
[00:16:01] What is churn?
[00:21:48] Metrics for understanding churn
[00:24:01] Feature engineering for churn
[00:27:22] Why ratio metrics are the best best in your battle against churn
[00:33:09] Dealing with outliers
[00:39:34] More feature engineering tips
QUOTES
[09:06] "When I started out, of course, people thought machine learning was trash...No one was that interested in machine learning back in the early 2000s. It wasn't until after Google essentially had showed how much they could do with machine learning in a production environment with big data."
[12:22] "It should enable better decisions, too. Not just faster decisions by getting the right data to the right people and giving them the right tools. We really should see companies making more optimal decisions."
[13:30] "There should be like a Hippocratic Oath for Data scientists, which means that goes beyond just you don't want to make mistakes. It means that you shouldn't be working on those, you know, on those dangerous applications. "
[22:04] "the features that you choose in my mind are really the main part of solving any data science problem and not the algorithm. I show actually in my book that if you do a good job on your feature engineering, the algorithm that you choose is not that important for your accuracy. So feature engineering always has number one importance in Data science"
SHOW NOTES
[00:01:31] Introduction for our guest
[00:02:54] Carl’s path into data science
[00:04:30] The fascination with churn
[00:08:04] How much more hyped do you think the field has become since you first broke into it?
[00:09:41] Where do you see the field headed in the next two to five years?
[00:11:20] What do you think would be the biggest positive impact that Data science will have on society in the next two to five years?
[00:12:36] What do you think would be the scariest application of machine learning and data science in the next two to five years?
[00:13:17] As practitioners of machine learning, what do you think would be some of our biggest concerns when we're out there doing our work?
[00:16:01] What is Churn? Is that what we do we make butter.
[00:17:27] So why is churn so hard to fight?
[00:21:48] The importance of metrics in our battle against churn
[00:24:01] How do we go from raw event data to metrics?
[00:24:45] How do cohorts help us analyze, predict, and understand churn?
[00:27:22] What are ratio metrics and why are they so powerful?
[00:33:09] Why are outliers so problematic to deal with?
model and get information from them, but without them ruining your numbers.
[00:34:57] What are some common mistakes that you've seen Data scientists make when it comes to dealing with outliers?
[00:39:14] How to be more thoughtful when it comes to feature engineering?
[00:42:31] Debunking the common misconception that the choice of algorithm is the most important thing that contributes to model performance.
[00:43:56] Your features don’t need to be the most creative
[00:45:28] Your job isn’t over once you deploy the model
[00:49:05] What are some things that we need to monitor and track - the context of churn - to make sure that our model is doing what it should be, that is performing as we've designed it?
[00:50:26] How COVID is messing up everyone’s churn models
[00:53:14] Is data science an art or sc