As we've learned throughout the past year, our industry is more than capable of incorporating creative and adaptable solutions to keep their business moving forward. Understanding customer behavior is a key component of an effective selling strategy. Implementing a successful business plan will rely on accurate projections from trusted sources.
These activities are reflected in quoting and selling performance seen across all products listed on AQ’s platform. Our team recently conducted a data comparison presentation between the National Restaurant Association’s (NRA) Restaurant Performance Index (RPI) and AQ quoting data. Statistical analyses like this one help to keep our customers informed on the latest trends and facilitate predictions of future quoting activity.
What is RPI and how does it work?
Launched in 2002, RPI is a monthly composite index that tracks the health of and the outlook for the U.S. restaurant industry. The index consists of two components: the Current Situation Index (CSI)—which measures current trends—and the Expectations Index (EI)—which measures operators’ 6-month projections. The CSI component yields high, positive correlation to AQ quoting figures. These data points include the total number of projects, users, quotes, and dollar volume.
RPI vs AQ Quoting Data
Trends in AQ activity can be accurately compared to those in RPI—especially CSI—to determine changes in the overall market vs changes in AQ-specific activity. At the 99% confidence level, there is a significant association between the RPI and, specifically, the number of AQ projects quoted, showing a strong positive linear relationship.
CSI shows a great correlation with projections for the previous month’s number of users, as is noted in the chart below. These findings provide an opportunity for our team to use quoting data to predict real restaurant performance. The consistency of 2020 quoting activity displayed is somewhat surprising given the volatility of the market.
The Expectations Index intends to measure operators’ 6-month outlook in the industry. However, the unpredictable nature of business, paired with the subjective nature of future measurements means that these values are not predictive. While EI is most highly correlated with quoting volume, there is not enough evidence to support a significant relationship between the EI component of RPI and AQ Quoting statistics. It is also important to note that the data used is defined by two clusters of pre-and post-COVID samples. While the data could be linear in the long run, further observations are necessary to support it in the long run.
As our industry continues moving toward a path to recovery, it is worth noting that activity is trending upwards, giving our customers the stability needed to pursue their selling goals. In the meantime, our team will continue to analyze performance results against projections to refine our internal predictive model.