Predict if Current Customers will Open a New Account (Cross-Sell Opportunities)
In this use case example, we will be walking through how to predict the future behavior of users using a Machine Learning Pipeline. Specifically, we’ll cover how to predict if a current customer will open a new account or use a new service. This prediction is best for businesses that offer multiple services, and are looking for ways to Cross Sell one of those services to current customers. Examples of this could be financial services or insurance industries where customers can have different accounts or policies with the same company.
What data do I need for this prediction?
The actions available for you to predict with a Future Events pipeline are based on the Event data that you are sending into Cortex. This sample prediction requires two pieces of information:
- Open Account Event Type: an event representing that a user has opened an account.
- Current Customer Attribute: for this prediction, we are looking to cross sell a specific account type to current customers. We will use this Current Customer attribute to have the prediction be made for only those users who are tagged as current.
While the above are the only pieces of information required to set up our sample prediction, more data will typically lead to better predictive performance. Other information that can be used to build features for our pipeline include:’
- Additional User Behaviors (e.g. logins, clicks, pageviews, adds to cart, etc.) with additional metadata (e.g. What device is the user on? Where was the user referred from? etc.) which give more detail to the event. As an example of additional metadata for an event, an item category can be passed along with the event in order to differentiate which purchases occurred specifically for the category of interest. Alternatively, item details can be sent in a separate feed as long as the item is identified within your purchase events with a shared ID.
- User Attributes (e.g. demographics, loyalty status, etc.)
How do I predict if Current Customers will Open a New Account?
Step 1: Choose Pipeline Type
Select ‘Create New Pipeline’ from within your Cortex account, and choose the Future Events pipeline type.
Step 2: Define Event
Future Events pipelines are used to predict the probability that some event happens in the future for each of your data points. Any future events prediction can be stated as a sentence as follows:
(A) happens (B) or more times where (C) within (D) days.
For our prediction, that reads:
An Open Account event happens once where Current Customer = True within 30 days.
Here is how that will look in Cortex
Step 3: Define Groups
Sometimes you are looking to make a prediction for every user in your system, while other times you want a prediction only for a certain group of users. This prediction will be made for only Current Customers, so we will have an Included Group filter users where Current Customer is True.
Step 4: Specify Settings
Settings is where you give a Name to your pipeline as well as the option to add Tags. Name and Tags are the two main ways to find your pipeline within Cortex after it has been created, so best practice is to use descriptive Names and Tags specific to each prediction.
In this example, the pipeline name is “Open New Account Prediction” and the Tag “Open Account” has been added as well.
Additionally, you can choose to run this pipeline only once, or have it rerun weekly. In this example, the pipeline is set to re-run every Sunday. This means that your pipeline will use the latest available data to retrain and re-generate up-to-date predictions on a weekly basis.
Step 5: Review
The final step is to review your pipeline and ensure all settings look accurate! If anything needs updated, simply go ‘Back’ in the workflow and update any step. Otherwise, click ‘Start Training’ and sit back while Cortex generates the predictions.
Those are all the steps necessary to create a future events pipeline that predicts if current customers will open a new account.
- Future Events Performance
- How to Build a Look Alike Pipeline
- How to Build a Classification Pipeline
- How to Build a Regression Pipeline
Still have questions? Reach out to email@example.com for more info!