MS Companies is committed to providing a seamless experience between the manufacturing industry and the tech industry. The company uses predictive analytics on its database of over 200,000 talent profiles to best match candidates with their customers. In addition, company executives pride themselves on providing easy communication throughout the entire process. To maintain this goal, MS Companies sought a better forum to support communication and provide a better customer experience. With Einstein Discovery, MS Companies uncovers key patterns about its employees and clients to increase satisfaction and retention.
MS Companies is a long-time Salesforce customer. The company began its journey with Sales Cloud, Service Cloud, and the Salesforce Platform. With the Salesforce Platform, MS Companies built over 20 custom apps to learn more about its employees.
“We have been able to simplify our processes by transitioning all legacy systems to Salesforce, resulting in a 30% productivity gain.”
The employee engagement app measures and records retention, activities, and event participation across 14 states. Employees now have easier access to their own information and can rate their supervisors and easily submit an HR case. MS Companies also built an app that measures employee time and performance. Before this app, all time and performance processes were manual and included laborious tasks such as scanning and printing. With Salesforce, the company has improved its invoicing accuracy from 96% to 99%, which saves significant time and money for MS Companies employees.
“We utilize the power of the platform and achieve our needs with limited code.”
In addition, the company built an employee community app. Before this app, applicant conversion was low during the employee onboarding process. This app leverages Community Cloud and allows employees to interact easier, improving applicant conversion by 67%. Even though MS Companies was heavily using all of the Salesforce tools, company leaders sought a tool that would provide deeper analytical insights to business users.
Before Einstein Discovery, MS Companies had a full team of analysts trying to understand key insights from Excel spreadsheets. The analysis process was extremely manual and did not allow for scalability. Executives needed a way to make it easier to communicate key findings with nontechnical employees. With this goal in mind, MS Companies chose Einstein Discovery and was able to dig into its initial use case with an up-and-running time of two weeks. The first area MS Companies wanted to explore was which factors influence the success of an employee. Company leaders began by exploring education level, age, and employee tenure through data sources such as, custom built apps, ERP data, and more. They quickly discovered that the initial factors explored did not correlate with employee retention. Instead, they discovered that constant communication throughout the onboarding process highly correlated with retention. “This was a key finding for us. This technology is really innovative and helps make our jobs easier,” said Gray.
In addition to discovering employee retention behavior, MS Companies began exploring a new use case around manufacturing. MS Companies manufactures all types of automotive parts; however, it was difficult to identify auto-part malfunctions and provide assistance from a service standpoint. Previously, the company used Excel to understand issues occurring in the assembly line. With Einstein Discovery, quality engineering teams have access to part data and have the ability to escalate findings all the way up. To further explore what causes low quality parts to occur, MS Companies is analyzing warehouse location, supervisor tenure, and supervisor training.
“We could see what has already happened, but our goal was to look at tomorrow and next week and predict what was going to happen,”
Previously, the company used Excel to understand issues occurring in the assembly line. With Einstein Discovery, quality engineering teams have access to parts data and have the ability to escalate findings all the way up.
“To be able to identify problems and predict issues ahead of time is a game changer.” said Gray.
To further explore what causes low quality parts to occur, MS Companies is analyzing warehouse location, supervisor tenure, and supervisor training.
“The long-term goal is to be able to push these findings into Community Cloud and provide our customers with even more visibility and a more personalized experience,” said Vandergrift.