Now you're ready for the fun part: choosing the predictions you want Faraday to make.
The Faraday API makes it easy to generate predictions about people—specifically customer behavior. Here are the broad types of predictions we provide:
Propensity — Want to predict whether a given person will take a specific action? Declare a propensity objective to compute the probability that a prospect will make a purchase, a lead will convert, a customer will re-engage, or any other action.
Persona — Want to organize a group of people into interesting, coherent subgroups? Declare a persona objective to enable a wide array of personalization techniques.
Potential (coming soon) — During the lifetime of a customer's relationship with you, many things can happen. Brands everywhere wish they could a sense of how long the relationship may last, and how valuable it will be along the way. Faraday helps you forecast this relationship for each one of your customers—even before they become a customer. You can use this forecast to predict LTV, churn, and more. Contact support for an invite to the closed beta.
Most AI platforms require the user to manage "models" — technical AI artifacts that encode specific predictive patterns.
Faraday believes that models are like physical servers in a datacenter: users shouldn't have to worry about which specific box their website is running on; nor should they worry about specific models.
Instead, you will declare prediction objectives in an intuitive way that doesn't require data science experience. Faraday will assess your objectives, build candidate models for each of them, and select the highest performing models. Going forward, we continuously evaluate opportunities to "refresh" your models to take advantage of new data.
When you deploy predictions, you choose objectives rather than models for your payload — Faraday will use the best model at the time to compute the predictions for your deployment.
The machine-learning part of prediction is boring and messy—you don't have to worry about it with Faraday. (Of course, if you want to see model details, we're fully transparent.)
Updated 4 months ago