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Freddy’s Serves Up Its Data Science Initiative With Domo
Technology Category
- Analytics & Modeling - Data-as-a-Service
Applicable Industries
- Retail
Applicable Functions
- Business Operation
Use Cases
- Predictive Quality Analytics
- Supply Chain Visibility
Services
- Data Science Services
The Challenge
Freddy’s Frozen Custard & Steakburgers® has grown from a single location in the heart of the Midwest to nearly 400 locations across the US. The company has a modern approach to data science. However, when Freddy’s first began its data science journey, it struggled with a lack of technology and perspective. After a failed pilot engagement, Freddy’s needed a different partner and a different approach to sell the initiative to its leadership team. The company had to deal with 18 different data sets spanning over 100 different columns of information created for each of the Freddy’s locations at multiple points in time. With so many individual columns of data to consider, store quality was difficult to understand and assess.
About The Customer
Freddy’s Frozen Custard & Steakburgers® is a fast-growing restaurant chain that has expanded from a single location in the heart of the Midwest to nearly 400 locations across the United States in less than two decades. The company is known for its classic fast food fare, but it has a decidedly modern approach to data science. Despite its rapid growth and modern approach, Freddy’s initially struggled with its data science journey due to a lack of technology and perspective. The company had to deal with 18 different data sets spanning over 100 different columns of information created for each of the Freddy’s locations at multiple points in time, making store quality difficult to understand and assess.
The Solution
As a long-time Domo customer, Freddy’s believed that Domo would be the right partner for the restaurant’s next attempt at data science. Working with Domo’s experienced data science consultants, Freddy’s set out to understand their restaurant quality data and make it easily consumable. With Domo, Freddy’s created a statistically sound data reduction process to automate and make their store quality data easy to understand. Domo’s data science team worked with Freddy’s to fully understand business practices, their restaurant quality indicators, incentives for restaurants in managing restaurant quality, etc. After comprehensive discussions about both business practices and data, Domo worked with Freddy’s to develop an exploratory factor analysis to identify the complex interrelationships among the 100+ columns of restaurant quality data. After understanding these relationships, Freddy’s and Domo moved forward to develop a custom confirmatory factor analysis pipeline explicitly accounting for the unique attributes in Freddy’s data and business model. This process was then automated in a data science production pipeline that facilitates full automation of Freddy’s data science solution.
Operational Impact
Quantitative Benefit
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