Propensity to Churn

This classification model scores all customers on their likelihood to churn out of your active customer population. Churn is defined as a customer not making a purchase within a specified window.

How does it work?

MessageGears' default value for the window of prediction (known as the PREDICTION_WINDOW) is 180 days. This means the model predicts the probability that an individual will churn within the next 180 days. If necessary, 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 HISTORICAL_WINDOW determines who is eligible to be scored. For the Churn model, the default HISTORICAL_WINDOW criteria is that a customer must have made a purchase within the last 365 days.

After training and tuning, the model is ready to score the active population.

Results

The model’s output is a normalized SCORE that ranks customers from 100 (most likely) to 1 (least likely) to churn out of your active customer population. It 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 churn than a customer with a score of 75, who in turn more likely to churn than a customer with a score of 25. You can then use score in segmentation or personalization for a wide variety of marketing use cases.

Requirements

• Email engagement data (clicks, opens, sends)
• Web/Pixel engagement data
  • Data Input Schemas for Transaction/conversion data

Schema