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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.

 

Schema

MG Field Name Type Range Description
RecipientId VARCHAR   The unique customer identifier, the key to join back to customer tables.
ML-PurchasePropensityScore INT 0-100 0-100 normalized output of scoring.
ML-PurchasePropensityLow BOOL 0 or 1
Predefined segments.
Our recommended audience definitions today break out high/medium/low, where the top quintile = high; mid three quintiles = medium; bottom quintile = low. However, we can customize this during implementation.
ML-PurchasePropensityMedium BOOL  
ML-PurchasePropensityHigh BOOL  
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