Purchase Propensity

This classification model scores all customers in the population on their likelihood to make a purchase within a given window of time.

How does it work?

The model predicts the probability that an individual will make a purchase in the next 30 days. If needed, we can assign a custom prediction window that fits your unique sales cycle. We transform each customer’s unique data into a custom feature set, with automated training and tuning to deliver the most performant model possible.

The model uses a historical window to determine who is eligible for scoring. For the Next Purchase model, the default historical window criteria is that a customer must have made a purchase, clicked an email, or triggered a web event within the last 90 days. After training and tuning, the model is ready to score the active population.

The model’s output is a normalized SCORE that ranks customers from 100 (most likely) to 1 (least likely) to make a purchase. The model then assigns the customer into a percentile, creating a (mostly) even distribution that ranks customers against each other. A customer with a score of 99 is considered more likely to make a purchase than a customer with a score of 75, who in turn more likely to make a purchase than a customer with a score of 25. We then make the score available to use in segmentation or personalization for a wide variety of marketing use cases.

Requirements

Behavioral data (such as clicks, opens, sends, and push interactions) from one or more channels (email, push, in-app, and so forth) and transaction data (purchases, conversions) needs to be available in the your data warehouse.

• Data Input Schemas for Transaction/conversion data

Schema