Overview
Last updated
Last updated
In marketing, you want to predict future customer behaviors and send messages that are as specific as possible. Knowing which customers are likely to churn or spend more can help you target them effectively.
AI predictions are now available on DinMo, enabling you to enrich your customer data with churn likelihood and expected lifetime value. We've developed custom machine learning models based on neural networks, tailored specifically for retailers.
This guide provides end-to-end instructions for computing AI predictions on DinMo and using them as filters to address specific customer groups. Follow these steps to compute AI predictions and build your audiences around them.
Before starting, ensure you have the following:
Access to a DinMo workspace
A user model linked to an events model with a transaction value field.
In the sidebar, click on "Predictions" and request access. Access will be granted based on your subscription conditions. Once approved, click on "Predict your first attribute."
Choose Customer Lifetime Value to predict LTV and churn predictions.
Select the following fields:
User Model: Contains user data. Ensure there is a relationship with an events model containing transaction data.
Events Model: Contains transaction data linked to the user model you selected.
Transaction Value Field: This represents the amount a customer spends per transaction.
Click on Continue.
Configure the AI attributes:
Selecting the AI attributes you wish to compute, such as churn likelihood and/or LTV.
Choose the time window for the prediction. For example, selecting a three-month window will compute the likelihood of churn within the next three months.
Name the prediction.
Click on Save.
You can now check your predictions in the tab Predictions:
For an overview of the output, click on a prediction and then "Go to model" at the bottom. An associated predictions model has been created, pointing to a predictions table in your data warehouse.
To apply AI attributes as filters for your segments:
Create a new segment and select the user model you used for predictions.
The AI attributes will appear in the additional model filters. For example, you can choose customers with a churn likelihood above 80% in the next three months.
Privilege Errors: Ensure that DinMo has the necessary privileges on the predictions schemas and tables in your data warehouse. You can find the relevant SQL scripts for each warehouse at the following links:
