The Likelihood To Pay Full Price (LTPFP) model predicts near-future discounting behavior based on past transaction, email, and browsing behavior. This model helps marketers to know the probability of a customer buying a certain product in the near future with zero or low discount.
Give lower discounts to the customers that are likely to pay full price. Alternatively, you can give higher discounts to the customers that are unlikely to purchase without a high discount. Thus, you can stop cannibalizing margin on the former while earning incremental revenue on the later.
Recommend full price items to the customers with high LTPFP while you can recommend sale items to the customers with a lower LTPFP.
You can combine LTPFP with other machine learning models, such as Predictive Lifetime Value, to design bundle campaigns for the high predictive lifetime value customers that also have high LTPFP.
While promoting a “global” discount coupon for all customers, exclude the customers with high LTPFP.
Identify the customers with high LTPFP and merge them with other machine learning predictions, such as Likelihood To Buy, Likelihood to Convert, to build new segments.
The LTPFP model predicts the behavior of buyers. A buyer is a customer with a transaction in the past three years. Hence, CDP does not include anyone who has bought earlier than three years 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 engagement.
The output of the model is the LTPFP for each customer. This probability is segmented in 10 equal-sized deciles, 1 being the most likely decile to yield full price buyers:
| Decile | Description |
|---|---|
| 1, 2 | High |
| 3, 4, 5 | Medium |
| 6, 7, 8, 9, 10 | Low |
| NA | Non buyer |
| NA | Not qualified |
CDP displays the output from the LTPFP model in:
To learn more about how the LTPFP model can enhance your marketing workflow, contact us.
If this content did not answer your questions, try searching or contacting our support team for further assistance.
The Likelihood To Pay Full Price (LTPFP) model predicts near-future discounting behavior based on past transaction, email, and browsing behavior. This model helps marketers to know the probability of a customer buying a certain product in the near future with zero or low discount.
Give lower discounts to the customers that are likely to pay full price. Alternatively, you can give higher discounts to the customers that are unlikely to purchase without a high discount. Thus, you can stop cannibalizing margin on the former while earning incremental revenue on the later.
Recommend full price items to the customers with high LTPFP while you can recommend sale items to the customers with a lower LTPFP.
You can combine LTPFP with other machine learning models, such as Predictive Lifetime Value, to design bundle campaigns for the high predictive lifetime value customers that also have high LTPFP.
While promoting a “global” discount coupon for all customers, exclude the customers with high LTPFP.
Identify the customers with high LTPFP and merge them with other machine learning predictions, such as Likelihood To Buy, Likelihood to Convert, to build new segments.
The LTPFP model predicts the behavior of buyers. A buyer is a customer with a transaction in the past three years. Hence, CDP does not include anyone who has bought earlier than three years 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 engagement.
The output of the model is the LTPFP for each customer. This probability is segmented in 10 equal-sized deciles, 1 being the most likely decile to yield full price buyers:
| Decile | Description |
|---|---|
| 1, 2 | High |
| 3, 4, 5 | Medium |
| 6, 7, 8, 9, 10 | Low |
| NA | Non buyer |
| NA | Not qualified |
CDP displays the output from the LTPFP model in:
To learn more about how the LTPFP model can enhance your marketing workflow, contact us.
If this content did not answer your questions, try searching or contacting our support team for further assistance.
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