Batch AI Predict
Batch AI Predict calculates predictive scores for each of your profiles using machine learning models trained on your data: purchase behavior, browsing events, campaign reactions, subscription status, and more.
Scores are refreshed on a schedule you define and are available across the Batch interface for targeting, personalization, orchestration, and analytics.
How it works
The Scoring Engine processes your customer and event data to generate scores at profile level. Each score is stored as a profile attribute and can be used anywhere you can apply a targeting condition or a personalization variable.
For product affinity scores (cross-sell, upsell, replenishment, product recommendation), you can define the categories or products to include in the model.
Predictive Scores
Batch AI Predict uses predictive AI to create personalized scores for each of your customers. Scores are refreshed on your preferred schedule and are available throughout the Batch interface for targeting, personalization, orchestration, and analytics.
Each score is stored as a profile attribute and can be used anywhere you can apply a targeting condition or a personalization variable.
Available scores
Products Recommendation (Parameterized score)
Identify, for each customer, the top N products or product categories to promote in order to maximize repeat purchase rate and immediate conversion.
Output format: List of N product IDs
Cross-sell Propensity (Parameterized score)
Identify, for each customer who has never purchased in a given product category, their propensity to make a first purchase in that category (i.e., the probability that the customer will buy from this category for the first time in the coming months).
Output format: Decimal between 0 and 1
Product Propensity (Parameterized score)
Identify each customer's propensity for a given product or product category (i.e., the probability that the customer will purchase this product or category in the coming months).
Output format: Decimal between 0 and 1
Churn Decisive Moment
Identify customers at risk of becoming inactive in the coming months and the optimal past date after which they are highly likely to become inactive.
Output format: Date
Second Purchase Date
Identify the date after which a one-time buyer is most likely to return and make a second purchase.
Output format: Date
Replenishment Date
Identify, for each customer, the date after which they are most likely to have finished a "consumable" product.
Output format: List of SKUs × date
Promotion Sensitivity
Identify each customer's sensitivity to promotions (i.e., the ratio between the probability that the customer makes a purchase when not exposed to a promotion vs. the probability that they make a purchase overall).
Output format: Decimal between 0 and 1
Discount Recommendation
Identify, for each customer, the optimal promotion level in order to maximize both conversion rate and the associated gross margin.
Output format: Promotion ID
Subscription Churn
Identify, for each customer, their risk of unsubscribing before the next subscription renewal (i.e., the probability that the customer churns).
Output format: Decimal between 0 and 1
Top Client at Risk
Identify in advance the top historical customers who are at risk of reducing their spending in the coming months.
Output format: Boolean
Future Lifetime Value
Identify, for each customer, the total future amount they are likely to spend in the coming months or years.
Output format: Amount in €
High Potential Prospect
Identify, for each prospect, their likelihood of being converted soon (i.e., the probability that the prospect will make their first purchase soon).
Output format: Decimal between 0 and 1
High Potentiel Customer
Identify, for each customer, their likelihood of becoming a high-value customer soon (i.e., the probability that the customer will generate significant revenue or place high-value orders in the near future).
Output format: Decimal between 0 and 1
Best Send Time
Identify, for each recipient, what is the best time to send a message to him or her within a given time window.
Output format: Score only usable in Automation Builder, through a Best send time step.
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