Articles related to predictive modeling from the team that helps B2C companies build and defend revenue with AI.
Happy Money is a fintech company on a mission to turn borrowers into savers, specializing in helping people pay off their credit card debt and save up for the future.
We like cohorts because they are only able to grow, retaining each individual customer that enters. The fact that someone can’t be removed from a cohort means that, when modeling, we can expect results from our historical models to be consistent.
While there are various types of propensity models, the one we use most at Faraday is the random decision forest. Like real forests, this one is made of trees — decision trees. Decision trees are classifier algorithms that look like flow charts, showing the choices made to reach a certain outcome.
An algorithm contains the biases of its builder. At Faraday, we have a handful of approaches we use to minimize these effects at each level of our machine learning pipeline.