Scoring & Propensity models

Intelligence workbench module allows you create ready-to-use data science models to gain insights from the unified customer data and make informed decisions.

Model	Sample use cases
Predictive Contact Lead Scoring models	Identify leads at a product level within an account for efficient follow up Filter out low-quality leads so sales can prioritize follow-ups
Predictive Account Scoring models	Rank accounts or contacts based on probability to convert Route accounts to sales or marketing depending on their likelihood to purchase
Product Propensity models Repurchase propensity models	Predict which product category (e.g., electronics, apparel, services) a customer is likely to buy next Suggest complementary items for cross-sell Identify customers likely to buy again soon and nudges with reminders.
Churn Propensity models	Flag high-risk customers and trigger retention offers Predict which segment is likely to unsubscribe, enabling preventive engagement
Engagement Propensity models	Identify customers likely to open/click campaigns and refines targeting Find dormant customers with high potential to re-engage.

The use cases listed above are illustrative scenarios. The models can support a larger variety of use cases depending on the business objectives, and the data availability.