Set up predictive models

The actions for which you are creating a predictive score.

When a customer completes the action on the message that is sent using Blueshift, it is counted as conversion. 

You can select the following actions for the specific channels.

• Email: Open, Click
• Push: Click
• SMS: Click
• In-App: Open, Click

Predictions will be made and remain valid for this time period. The default time period is 7 days.

For example, predict channel engagements in the next 7 days.

Shorter time windows could lead to more accurate predictions, while longer windows could introduce noise/irregularities, which leads to lower predictive accuracy.

Consider setting this value based on the historical conversion rate. For example, n number of conversions in the prediction window/number of active customers in lookback days * 100 is at least 1%.

Audience for the predictive scores will include those customers who had at least one action on the channel in the lookback period. The default value is 30 days (monthly active customers).

Consider setting this value based on the historical conversion rate. For example, n number of conversions in the prediction window/number of active customers in lookback days * 100) is at least 1%.

Select the events for which you are creating a predictive model. You can choose any combination of events that you are tracking with Blueshift.  

The presence of at least one of these events will be counted as a conversion. For example: purchase, subscription_renewal, etc. 

For more information about events, see Events in Blueshift.

Predictions will be made and remain valid for this time period. The default time period is 7 days.

For example: predict purchase intent in the next 7 days or subscription renewal in the next 30 days.

Shorter time windows could lead to more accurate predictions, while longer windows could introduce noise/irregularities, which leads to lower predictive accuracy.

Consider setting this value based on the historical conversion rate. For example, n number of conversions in the prediction window/number of active customers in lookback days * 100 is at least 1%.

Audience for the predictive scores will include those customers who had at least one impression(event or campaign action) in the lookback period. The default value is 30 days (monthly active customers).

This input should be equal to the length of the conversion funnel. 

• In most use cases, the conversion is heavily correlated with the recent engagement, so 30 days (monthly) or 90 days (quarterly) is usually sufficient.
• E-commerce funnels are usually short, so you can set it to 7 to 14 days for most e-commerce purchase intent use cases. 
• For Subscription businesses, you can set this input based on the billing cycles to predict subscription churns. 

Consider setting this value based on the historical conversion rate. For example, n number of conversions in the prediction window/number of active customers in lookback days * 100) is at least 1%.

Funnel Events

(Advanced Options)

Add funnel events for the goal event.

Funnel events are a set of events the customer has completed and which help to predict the likelihood of the customer completing the goal event.

User attributes

(Advanced Options)

Include attributes like customer demographics (age, location), preferences (attributes of interest from the catalog), memberships (subscription type), lifetime attributes (lifetime orders, revenues), and so on. 

The latest value of the customer attributes is considered.  

For more information, see Customer attributes and types.

Warning: Do not include a customer attribute that is updated as a result of the conversion. Otherwise, this would cause leakage (overfitting) and the model will not generalize well on unseen data. An example of one such customer attribute is “total_orders” for predicting purchase conversions. If we increment total_orders after conversion, for one-time purchasers, the model could confidently learn a simple rule that customers who convert must have total_orders > 0, which is not what we want.