Mandy is a word analytics platform that empowers companies to understand how people feel and thus make better decisions in reaching them. Using proprietary artificial intelligence, we deliver instant feedback to leaders on engagement and retention risks for any group of people based on the words they use to describe their experiences. Our technology is versatile and can be equally applied to help managers understand how engaged their team members are, or to help companies understand how their customers are responding to their products.
The primary subject of our case study was an owner-operator of a Chick-fil-A franchise location located in a metropolitan area in the mid-south United States. The owner operator was an early adopter and an original user of our Mandy AI engagement-retention word analytics platform. The context of our relationship was a 45-day use case within a store of approximately 90 employees.
Our primary goal was to use Mandy AI on a weekly basis among the owner-operator’s employees to diagnose employee sentiments, engagement factors, retention risks, and to provide prescriptive details about ways to enhance employee and customer experience and maximize store performance. We determined to qualify and measure these items using several metrics: store profitability, employee productivity, and customer satisfaction.
The governing hypothesis of our case study was that as the owner-operator maintained consistent communication with his team using the Mandy AI platform, and as his team sent anonymous feedback on guided questions about employee experience and store performance, the proprietary analysis and prescriptive data we provided the store owner would empower him to make calculated decisions that positively augmented the governing performance metrics above over a 45-day period.
We delivered the Mandy AI platform to the store owner in several stages. First, we trained the store owner briefly on operating the platform, which included helping him onboard the team members with whom he wanted to communicate via text message into our database. We also trained the owner-operator in formulating and scheduling questions to send to his team. Second, we began sending once-per-week questions to his team. As data was received, our artificial intelligence interpreted this data and organized it in terms of keywords, themes among employees’ responses, sentiment scores, and risks for engagement and retention based on employee language. We also generated regular prescriptions for suggested actions based on this data for the owner-operator and maintained weekly phone calls to discuss progress.
We executed the items in the methodology above every week over the test period. The owner- operator and the Mandy team worked together to compose questions that would be unique to his store, and we deployed them according to a set schedule. During this period, we achieved a response rate of over three times the industry average, and we identified several core behavioral and logistical issues within the employee experience which we used as a basis for helping the owner-operator identify and execute new store strategies. The owner operator used Mandy’s analysis to supplement his leadership team meetings and mitigate employee issues while driving new initiatives.
In line with our hypothesis, the Chick-Fil-A store experienced increases in every key metric.