Consumer predictions are key to smart growth — but how do you get them in the first place? Go behind the scenes with Faraday data scientists as they break down the latest initiatives they're undertaking and challenges they're solving.
Google and Microsoft both allow for flexible geographic targeting up to a point, which means we can use AI to bundle groups of individuals, find the commonalities, and make a recommendation about how much a marketer should be willing to spend to engage with them.
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.
Journey mapping helps brands understand the sequence of actions a customer is likely to take – and it has strategic implications.
A fundamental part of location intelligence, spatial aggregation combines individuals into groups and generalizes information about them.
Our clients expect that their persona groups won't shift every time they convert a lead. This is why we are currently researching a new method for determining the optimal number of personas, which focuses on the stability of the persona group.