Faraday model reports: Understanding feature importance and directionality

Faraday model reports: Understanding feature importance and directionality


3 min read

Predictions are complex, and consumer predictions for marketing aren’t an exception. In many cases, you might find yourself given the green or red light to go after a lead, prevent a churn, or approach a customer in another way that’s specific to a business goal your org has. Having access to that knowledge is great, but it’s a black box–simply knowing whether or not a lead will bite isn’t enough if you want to truly understand your customers and the actions they take.

In Faraday, every predictive model you describe for your organization via the recipe wizard or in Outcomes is scored based on how well you can expect the model to perform, and includes a full technical report describing how the model was built. While the report includes a swathe of info for data-minded users, for the sake of this guide we’re going to take a look at how features of importance and directionality impact a sample lead conversion model, and what they mean.

Features of importance

Image of features of importance in model report

In every model, you’ll find a section describing the top features of importance in each of Faraday’s feature categories: lifestyle, demography, and property. Reachability (do-not-call lists) and private (first-party data) may appear as well depending on the data used. Generally totaling 25, the features that had the most impact in creating the model are listed in the chart, sorted by the level of impact. In the above image, you can see that features such as age, gender, and length of residence had a major impact on the model. This means that when thinking about who you’re targeting with these predictions, you can tailor creative and messaging appropriately–but that still feels a little broad. Let’s take a look at directionality to see exactly how these features of importance break down.


Image of directionality in model report

When you take a deeper look at a feature of importance in directionality, you’ll find it broken down into ranges if the feature is numeric, or selections if not. In the above example, green indicates positive, and red negative. For age, it’s clear that consumers aged 18-42 rank higher on average (green), whereas all age ranges older than 42 rank lower (red). Similarly, consumers who have lived in their current residence between 1-7 years rank higher, and those of any longer residence rank lower in the length of residence chart.

That’s cool and all, but what’s the takeaway?

In short, these breakdowns tell you that in this lead conversion model, the people that are most likely to convert are largely in the age range of 18-41, and have lived in their current residence for no more than 7 years. Using this info, your marketing to them can confidently personalize toward the positive features so that your content is relevant.

The best of AI, right in your inbox