Customer Lifetime Value

This regression model provides the value of each customer in a given window of time in your customer population.

How does it work?

We calculate lifetime value (LTV) using the following metrics:

• HISTORICAL_LTV: All spend prior to score date.

• SCORE: The predicted spend of a customer in the TRAINING_WINDOW.

• TOTAL_LTV: The sum of HISTORICAL_LTV and SCORE

HISTORICAL_LTV is the past spend, SCORE is the predicted future spend, and TOTAL_LTV is the combination of both. You can use all three for audience creation and segmentation, depending on your goal.

MessageGears' default value for the window of prediction (known as the PREDICTION_WINDOW) is 365 days. This means the model predicts the customer’s spend over the next 365 days. If necessary, we can assign a custom PREDICTION_WINDOW that fits a 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 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 365 days.

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

Results

The model produces four different columns, all of which have value for marketing efforts:

• HISTORICAL_LTV: All spend prior to score date.

• SCORE: The predicted spend of a customer in the TRAINING_WINDOW.

• SCORE_SCALED: The score transformed into a ranked scale – 100 is the most valuable customers during the window of prediction, 1 is the least valuable.

• TOTAL_LTV: The sum of HISTORICAL_LTV and SCORE.

Requirements

Email engagement data (clicks, opens, sends)

• Web / Pixel engagement data
  • Data Input Schemas for Transaction/conversion data

Schema