The Situation
FARE, a thriving fast-casual restaurant brand co-founded by female entrepreneurs Rhiannon Bader and Kasia Bednarz, is taking Chicago by storm with its healthy ingredient-first philosophy and innovative seasonal menu. Now in high growth mode with three locations (and three more in the works), FARE partnered with MRGN to bring clarity to their complex data and get answers to high-stakes questions—like which menu items should stay, which should go, and how each dish performed across different sales channels. Using MRGN’s Machine Learning Insights Engine, FARE gained clear, data-backed answers to their questions.
The Challenge
Operational Complexity: Speed Not Spreadsheets
Despite having access to extensive data through their existing systems, the FARE team needed a more effective way to evaluate individual menu item performance across dine-in, takeout, delivery, and catering. Answering critical questions about menu performance would have taken months of manual analysis. With three new locations on the horizon, including their first expansion outside their home market, FARE turned to MRGN with urgent questions about menu performance across both current and upcoming locations and channels.
In short, FARE didn’t need more numbers—they needed fast, clear answers.
The Solution
Timesaving, Scalable, Tech-Agnostic Insights
MRGN created a scalable, tech-agnostic workflow equipped to handle FARE’s existing systems and data complexity. In less than two weeks, the team focused on analyzing product mix (PMIX) data and collaborated closely with FARE to align on how the data should be interpreted to ensure relevance and clarity. Leveraging its proprietary Machine Learning Insights Engine, MRGN surfaced meaningful patterns and delivered actionable recommendations that directly addressed FARE’s most pressing operational and menu-related questions.
