The Predictive Lifetime Value (PLTV) model predicts the revenue or margin of a customer in the next 12 months. The model predicts potential future value of customers and allows you to build campaigns around the predicted buying behavior.
Limit direct mails to the top tiers to maximize ROI and cut losses on lower tiers.
Target high PLTV customers with discount bundle deals to increase ticket size.
Understand your new customers by pivoting PLTV groups with other dimensions such as demographics, acquisition source or machine learning dimensions like Likelihood To Buy, Likelihood To Pay Full Price.
The PLTV model predicts the behavior of buyers. A buyer is a customer with a transaction in the past three years. Hence, anyone who hasn’t bought an item in the last three years isn’t included in this model.
For each contact, the model calculates:
CDP trains and tests the model on historical data where the outcome is known. Post training, the model is deployed to predict future revenue.
This model ouputs the following sets of values:
By default, predictive value decile is split into 22 buckets:
Decile | Description |
---|---|
01, 02 | High |
03, 04, 05, 06, 07, 08, 09 | Medium |
10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 | Low |
NA | Non buyer |
NA | Not qualified |
Predictive value revenue group has the following default intervals:
Deciles higher than 10 (for example, 11, 12) have low predictive value.
CDP displays the output from the PLTV model in:
You can use the top five revenue groups as additional filters for:
To review the continued effectiveness of the model, you can use a dashboard to compare the six-month older predictive lifetime revenue groups with the actual purchases in the last six months.
To learn more about how the Predictive Lifetime Value (PLTV) model can enhance your marketing workflow, contact us.