Faraday Core — our AI engine

With historical examples, rich data, and key business outcomes in hand, Core continuously optimizes your customer lifecycle. Important opportunities are promoted, and likely dead-ends are deprioritized automatically.

Machine learning that works

  1. Faraday Core employs the most accurate, flexible ML techniques to optimize your outcomes. For a quick tour of the Random Decision Forest (RDF) algorithm, use the controls above.
  2. RDF starts with your known historical examples, both good and bad. For example, if we're optimizing lead conversion, we collect your leads that converted and your leads that didn't.
  3. Because these leads are synced to the Faraday Identity Graph, they include hundreds of rich attributes. To start, the algorithm picks one attribute at random.
  4. Here, household income was chosen. Now, the algorithm chooses a random "split" in the range of values. In this case, $53,439 was chosen.
  5. Next, the algorithm checks to see if this random selection has led to a better sorting of the converted and non-converted leads. It doesn't have to be perfect, just better than before.
  6. In this case, yes! The resulting subgroups of leads are better sorted than before. If that weren't the case, the algorithm would toss out this particular random selection and try again.
  7. Now we just continue to build our "tree" of random attributes and splits. Here, education level (college or below versus graduate degree) and football interest (yes versus no) were selected.
  8. In practice, the algorithm continues in this way until it reaches a decent stopping point. As you can see, each of the "leaf" circles on the tree are all mostly either good or bad. This is a successful tree!
  9. Now, when we receive a new lead, we can simply follow it through the tree to find the predicted outcome. For example, a lead with high income but no interest in football is unlikely to convert.
  10. On the other hand, a new lead with lower income that never went to grad school probably will!

The best models, automatically

Core always maintains a "best model" for each configured business outcome. When new data emerges, new candidate models are continuously built, cross-validated, and evaluated to see if a "new best" is available.