The Challenge
Goosehead’s data existed at multiple levels and in multiple Salesforce objects. They needed a way to assess model performance and create a long-term data-driven business plan in order to diagnose data quality issues and optimize their data strategy. Goosehead also needed to discover what factors drove an account’s propensity to remain active vs. cancel a policy.
The Solution
Goosehead partnered with Atrium to leverage Salesforce Analytics Data Sync/Dataflow tools to join data from multiple sources and analyze a single policy-oriented dataset. This eliminated the confusion of data living in several places in Salesforce and created a more streamlined view of accounts. With SAQL-powered dashboards in Analytics, model performance metrics (AUC) on scored records can be tracked over time. Goosehead is also using Einstein Discovery to successfully predict policy attrition for customers.
The Results
Einstein Discovery provides the predictions that Goosehead needed to identify policies that are highly likely to renew vs. those that may need additional attention from agents and has increased Goosehead’s understanding of factors that separate high-retention policies vs low-retention policies. With Einstein, Goosehead is now developing better thinking around company partnerships, particularly towards low-touchpoint insurance carriers. They are also improving their data strategy by highlighting features/objects where data is less well-defined.
