The Likelihood To Buy (LTB) model predicts near-future repeat purchasing behavior based on past transactions, email, and browsing behavior. This model helps marketers to predict the buying behavior of customers.
Use cases
Revenue or margin optimization
- Identify the customers that are likely to buy.
- Give lower discounts to the customers that are likely to buy. 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.
- Improve lift by targeting the customers with a higher likelihood of buying through expensive campaign mediums like direct mail.
Reactivation optimization and churn prevention
Identify customers who are not trending in likelihood to buy and create programs to avoid churn.
Customer comprehension
- Understand the customer by pivoting likelihood groups with other dimensions such as demographics.
- Understand how transactional, email, and web engagement variables are drivers of repeat purchases.
Target audience
The LTB 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 not bought an item in the last three years in this model.
How does the model work?
For each contact, the model calculates:
- The purchase behavior features such as lifetime value, recency, frequency, and Average Order Value (AOV)
- The email behavior features such as send, open, click frequency, volume, and recency
- The web behavior features such as browse or abandoned cart counts, session duration, session recency
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 LTB for each customer. This probability is segmented in 10 equal-sized deciles, 1 being the most likely decile to yield purchases:
| Decile | Description |
|---|
| 1, 2 | High |
| 3, 4, 5 | Medium |
| 6, 7, 8, 9, 10 | Low |
| NA | Non buyer |
| NA | Not qualified |
Using the LTB model
CDP displays the output from the LTB model in:
- 360 > 360 Profiles
- Actions > Campaigns+
- Analytics > Metrics
You can add the deciles: 1 - High and 2 - High as additional filters for:
- Pushing information to Facebook as it is an expensive channel. Hence, you can run daily campaigns that send information to Facebook to target high LTB customers.
- Sending direct mails to audience lists through ad-hoc campaigns. Thus, you can limit the audience to high LTB customers.
To review the continued effectiveness of the model, you can use a dashboard to compare the LTB deciles from one month earlier to the recent purchases made in the last one month.
To learn more about how the LTB model can enhance your marketing workflow, contact us.
Likelihood To Buy
The Likelihood To Buy (LTB) model predicts near-future repeat purchasing behavior based on past transactions, email, and browsing behavior. This model helps marketers to predict the buying behavior of customers.
Use cases
Revenue or margin optimization
- Identify the customers that are likely to buy.
- Give lower discounts to the customers that are likely to buy. 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.
- Improve lift by targeting the customers with a higher likelihood of buying through expensive campaign mediums like direct mail.
Reactivation optimization and churn prevention
Identify customers who are not trending in likelihood to buy and create programs to avoid churn.
Customer comprehension
- Understand the customer by pivoting likelihood groups with other dimensions such as demographics.
- Understand how transactional, email, and web engagement variables are drivers of repeat purchases.
Target audience
The LTB 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 not bought an item in the last three years in this model.
How does the model work?
For each contact, the model calculates:
- The purchase behavior features such as lifetime value, recency, frequency, and Average Order Value (AOV)
- The email behavior features such as send, open, click frequency, volume, and recency
- The web behavior features such as browse or abandoned cart counts, session duration, session recency
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 LTB for each customer. This probability is segmented in 10 equal-sized deciles, 1 being the most likely decile to yield purchases:
| Decile | Description |
|---|
| 1, 2 | High |
| 3, 4, 5 | Medium |
| 6, 7, 8, 9, 10 | Low |
| NA | Non buyer |
| NA | Not qualified |
Using the LTB model
CDP displays the output from the LTB model in:
- 360 > 360 Profiles
- Actions > Campaigns+
- Analytics > Metrics
You can add the deciles: 1 - High and 2 - High as additional filters for:
- Pushing information to Facebook as it is an expensive channel. Hence, you can run daily campaigns that send information to Facebook to target high LTB customers.
- Sending direct mails to audience lists through ad-hoc campaigns. Thus, you can limit the audience to high LTB customers.
To review the continued effectiveness of the model, you can use a dashboard to compare the LTB deciles from one month earlier to the recent purchases made in the last one month.
To learn more about how the LTB model can enhance your marketing workflow, contact us.
Acquia CDP