What Superior Farms does to better analyze their customer scores
How Superior Farms is leveraging Einstein Analytics to better analyze their customer scores in Salesforce Sales Cloud
Superior Farms, Inc. engages in the processing and marketing of lamb and veal products for retailers and foodservice operators in the United States and Canada as well as international markets. In addition to its staple lamb and beef products, Superior Farms also provides value-added products, such as scallopini and osso buco. The company was founded in 1963 and is based in Davis, California with additional offices in Dixon and Vernon, California; Boston Massachusetts; Denver, Colorado; Chicago, Illinois; and Hawarden, Iowa. Superior Farms, Inc. operates as a subsidiary of Transhumance Holding Company, Inc.
Superior Farms wanted to implement customer scoring using data points from various disparate systems and be able to drill into the root causes of high/low customer scores. Accounts would be scored by measures across various categories: relationship, freight cost, service cases, etc. The biggest challenge was getting this customer data from the various systems and quickly visualizing it in a central location. This would give sales reps a deeper understanding of specific accounts when interacting with them, and more quickly than if they looked across systems at the various categories.
Traction implemented Einstein data flows and built out two primary components within Salesforce Sales Cloud: an Account View dashboard that was embedded on the Account page layout, and an Overview dashboard that was accessible within Einstein. The Account View showed the various category scores for the selected Account, plus visualized other measures like margin, sales totals, cases, etc. The Overview dashboard shows insights across the entire business and allows executives and sales managers to slice and dice across regions, by individual reps, and into specific account groups. Additional dashboards were built to display a deeper level of detail comparing sales against budget, margins, as well as white space analysis to find opportunities to improve product mix across accounts.
The project build was successfully completed and deployed to users in November of 2016. User training resulted in a lot of great conversations about the inability to access this level of data in the past, and how these insights will improve the business moving forward. Additionally, the ad hoc analysis capabilities in Einstein have empowered business users to start answering their own data questions, which they were previously unable to do without technical know-how and access to their customer data warehouse.