Generic automated lead ranking in dynamics CRM

We developed a generic framework which enables Customer Relationship Management (CRM) organizations to deploy an automated ranking system for leads (commonly known as 'lead scoring'). Leads are records that represent non-customers who might become customers. Lead ranking is a fundamental CRM problem with many flavors. Ranking serves as a prioritization management tool for CRM organizations, with many characteristics similar to those of recommender systems. We present the system with its most recent developments, emphasizing challenges that go beyond the core of the learning algorithm, and that have played an instrumental role in maturing the system into a trustable feature, robust to different types of organizations and datasets. Particularly, we present features which enable Human in the Loop [1], a dominant concept in both configuration and result consumption. Another type of features demonstrates the addition of domain knowledge into the machine learning based process. We present the concepts of feature selection, with and without human help, prediction explanations, insights on model inputs, data quality issues, training for UX consistency, and actionability for each individual prediction.