QSR Franchisor Uses Wage Rate Data Set to Manage Bottom Line

Background

With razor-thin profit margins, franchisors across the United States are closely watching as momentum builds to raise the minimum wage to $15 an hour. Several cities, including Washington, D.C. and Seattle, have already increased minimum-wage requirements to $15 an hour. Drastic increases in the minimum wage have a significant impact on the way fast-food franchisees run their units. Thus, it is imperative franchise systems have real-time business intelligence tools to help them understand the effects that an increasing wage rate will have on their bottom line.

The Challenge

A fast-food franchisor with more than 1,000 locations across the United States wanted to study the impact that an increase in the minimum wage would have across its system. The franchisor was motivated to take a proactive approach with the goal of maintaining its financial health. Working with iLumen, the franchisor aimed to:

• Gain an understanding of how an increase in the wage rate affects unit profit margins.
• Segment units into categories based upon area wage rates and compare financial statements across each category to determine profitability.
• Provide unit operators with data in an easy-to-understand format that gives them insight about what they need to do maintain profit margins.

The Solution

The iLumen data aggregation and analysis platform has the capability to easily integrate a new data set into existing dashboards and forecasting models. An iLumen client for three years, the franchisor already had an existing data analysis model that allows sales increases, margins and costs to be adjusted to determine the overall impact of wage rate increases. However, iLumen first needed to determine the wage rates for each state and ZIP code in which the franchisor operates. While the franchisor could have utilized the iLumen survey engine to collect data from each franchisee individually, the iLumen team obtained current wage rates for each state and ZIP code. If a certain state or ZIP code was missing from the survey, data from adjacent DMAs was used to create a close approximation of that area’s wage rates. The iLumen system supports many data types including demographic values, financial reporting calculations and key metrics, among others.

Results

Using the wage rate data set, a new custom demographic variable was created, and the state and local wage rates were tagged to each individual franchise unit in the system. The franchisor is now able to use this new demographic field to segment each unit’s financial data into low, intermediate and high wage rate categories. Equipped with this data, the franchisor can compare the financial statements for these groups and evaluate how units in high wage rate areas are able to maintain similar profit margins to their counterparts in low wage rate areas. In addition, time periods where certain markets experienced an increase in minimum wage can be evaluated to determine which units handled the increase successfully and how they were able to do so.

Today, this franchisor can download an instance of this model that uses its real-world financial data to see the effects that an increasing wage rate will have on the bottom line. By integrating real-world financial data and local wage rate data into the model, franchisees are armed with the knowledge of what they must do in order to maintain profits in the face of increased labor costs. The iLumen platform provides franchisees with actionable insights to help them stay ahead of their competition.