Linkedin leveraged machine learning (ML) models to better segment, prioritize, and help target accounts for our sales representatives, explored which accounts were growing quickly and may need a new product package and which ones were struggling to get full value from their tools and may need more information. Linkedin called this work “Project Account Prioritizer,” and it provided a score for each existing customer that was eligible for renewal, and key field products they might be interested in to meet their business needs.
While this ML-based approach was very useful, Linkedin found from focus group studies that ML-based model scores alone weren’t the most helpful tool for our sales representatives. Rather, they wanted to understand the underlying reasons behind the scores—such as why the model score was higher for Customer A but lower for Customer B—and they also wanted to be able to double check the reasoning with their domain knowledge.
In this post, Linkedin showcase how they built an ML-based solution to serve useful recommendations about potential account churn and upsell opportunities to our LinkedIn sales representatives. Linkedin further showcase how expanded this tool to leverage the state-of-the-art, user-facing explainable AI system CrystalCandle (previously named Intellige) to create narrative-driven insights for each account-level recommendation. CrystalCandle plays an important role in Project Account Prioritizer by helping our sales team understand and trust our modeling results because they understand the key facts that influenced the model’s score.