Data-driven from the start, the Mixt data team wanted to better understand the buying behaviors of their most loyal customers – many of whom visited over 20 times every month – and see how they could further accelerate their mission to deliver fast, healthy, and great-tasting food to everyone.
The team at Mixt had a rich set of dashboards monitoring everything from same-store-sales to key store metrics like throughput and order volume. However, none of these descriptive tools could effectively showcase why performance in certain locations wasn’t keeping pace. The team needed a faster, more comprehensive way to diagnose their operations.
"We were capturing transaction and customer data from every point of sale,” said Celia Stockwell, Director of Finance at Mixt, “but the volume of information was too complex to do more than the most basic investigations. With Sisu, we can use all of the operational data we capture to diagnose changes in same-store sales, the impact of external factors like weather and season, and get regular updates on individual store performance.”
Read Mixt's case study to learn why they rely on Sisu to find ways to improve store operations by analyzing 15,000 hypotheses in seconds.