How to set up holdout tests to evaluate prediction accuracy
Prediction systems finding silly patterns is a real problem—you could even say it's _the_ problem of machine learning. Luckily there are great ways to avoid it.
· Andy RossmeisslPredicting with Faraday
Churn prevention: How to reduce customer churn using machine learning
When it comes to churn, no business can ever completely eliminate it, but there are strategies to consider that can help your business with churn prevention and mitigation.
· Patrick Doherty
Direct mail marketing tactics you need to know about
Join Faraday's Directors of Accounts Alex Noyes and Nick Haggerty, as well as Director of Marketing Perry McDermott, where they discuss modern direct mail targeting and personalization tactics you can use to get better results from your campaigns.
· Faraday
Personalized direct mail: How can direct mail be personalized at scale?
Personalized direct mail can be accomplished through several different means that utilize your first-party data, such as merge tags and creative variations. With machine learning and third-party consumer data on top of your first-party data, you can accomplish it more effectively, and at scale.
· Patrick Doherty
Propensity vs probability: Understanding the difference between raw scores and probabilities
In the world of machine learning, we often encounter two important concepts: the raw propensity score and probability. Though they may sound similar, they represent distinct aspects of a machine learning model's predictions.
· Thibault Dody
Using churn predictions to improve retention rates
Join Faraday's Directors of Accounts Alex Noyes and Nick Haggerty, as well as Director of Marketing Perry McDermott, where they dive into why brands are investing heavily in retention, and how to do it effectively.
· Faraday
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