Accurate measurement of performance indicators is essential for effective decision-making in restaurant operations, with revenue and yield management playing a crucial role. Kimes and Chase (1998), Kimes (1999) and Kimes et al. (1999) proposed the use of the revenue per available seat hour (RevPASH) metric which reflects revenue accrued in each time interval or service period divided by the number of seats available during that time for restaurant revenue management.

RevPASH indicates the rate at which capacity utilisation generates revenue. The Revenue Per Available Seat Hour (RevPASH) formula, has been widely used to assess restaurant profitability. However, the conventional version of the RevPASH formula, which combines the variables of seat count (number of seats) and service hours, presents limitations that could hinder analytical insights. This article critically examines these limitations and proposes an alternative presentation of the formula that enhances the understanding of the metrics' purpose as a baseline unit of measure and maintains a clear focus on two key drivers that can influence revenue and profit generation.

Analytical Limitations of the Common RevPASH Formula

The conventional RevPASH formula is often expressed as:
REVPASH = Total Service Revenue / (Number of Seats x Service Hours)

This formula calculates revenue per available seat over a unit of measure of one hour. While this formula provides a straightforward metric and yields accurate results, it combines two strategic levers that can both vary, namely the number of seats and service hours. Consequently, when comparing data over time, the intertwined nature of the number of seats and service hours can make it difficult to discern the specific driver of revenue changes, should they be driven by either capacity changes or service hours changes.

Drawing a comparison to the airline industry's revenue per available seat kilometre (RASK), which calculates revenue per available seat over a unit of measure of one kilometre (as a baseline unit of measure), the RevPASH formula considers time and uses a baseline unit of measure of one hour. RASK considers distance flown (the kilometres the capacity travels) and RevPASH considers time (the service period hours of the capacity). Similarly, when comparing with the accommodation industry's revenue per available room (RevPAR) metric, the baseline unit of measure is one night of capacity.

One key difference restaurants have is their ability to utilise their capacity multiple times within the same service period. Once a plane door shuts or guests check into their room, the capacity is not used again on that route or for that night by any other customers. However, a restaurant can quickly adjust its seating capacity to accommodate demand. Therefore, the two strategic levers of service hours and the number of seats (capacity) for restaurants are variables that can change more readily as part of a strategy. Hence, it is ideal for analysts to closely monitor these two strategic levers.

To ensure a consistent focus and a clear identification of any changes the proposed presentation of the RevPASH formula keeps the two variables that can potentially change separated. This enables quick identification of any changes when conducting data comparisons and trend observations. A capacity change, for example, will also be immediately apparent, as this number remains the same and reflects the physical seat capacity. Reflecting the capacity in the formula as the physical number of seats, rather than (number of seats x service hours), is also a recommended approach.

A More Effective Analytical Approach: Disaggregating Seats and Service Hours

To address the potential limitations of the common RevPASH formula, it is crucial to disaggregate the seat count (number of seats) and service hours for comprehensive analysis. By examining these variables separately, analysts can maintain a focused perspective on two of the drivers behind revenue fluctuations and make informed decisions regarding these two strategic levers.

To enhance the analytical perspective further, it is recommended to present the RevPASH formula as follows:
REVPASH = (Total Service Revenue / Number of Service Hours) / Number of Seats

This revised formula presentation (Kalan, 2023) first breaks down revenue by service hours, establishing a baseline unit of measure, and then divides it by the physical number of seats to accurately reflect the capacity of the restaurant. This formula presentation also eliminates potential misleading thinking and facilitates meaningful interpretations of the metric. For instance, a restaurant with 50 seats open for 3 hours does not have a capacity of 150 seats but rather has 50 seats available over a 3-hour service period, with the ability to utilise these seats multiple times by different customers during the same service period.

From a revenue and yield management perspective, examining service hours and seating capacity allows for an assessment of operational efficiency and the identification of potential areas for optimisation. By tracking service hours in relation to revenue and consumer demand patterns, analysts can evaluate staffing levels, opening hours, and the impact of service efficiency on customer satisfaction. This approach enables targeted adjustments in resource allocation and operational practices, leading to improved revenue and profit performance.

Additionally, alongside the preferred RevPASH formula presentation, analysts should also consider incorporating seat utilisation or seat churn ratios for the service period to further enhance their decision-making capabilities. Analysing seat utilisation, which measures the number of times a seat is turned over or occupied by different customers during a defined service period, provides valuable insights into demand patterns, customer behaviour, and operational efficiencies. A decline in seat utilisation may indicate decreased customer demand, service quality issues, or inefficiencies in meal preparation times and/or table turnover times. Conversely, an increase in seat utilisation could suggest opportunities for expansion, further optimised seating arrangements, or menu price adjustments. By focusing on seat utilisation in conjunction with RevPASH, analysts can identify and address areas of opportunity for improvement that directly impact revenue and profit performance.

The seat utilisation ratio is calculated as follows:
Seat Utilisation Ratio: (Total Customers over the service period / Capacity): (Capacity / Capacity)

This provides insights into the utilisation of individual seats during a service period, offering a comprehensive understanding of demand patterns, menu design and operational efficiencies. There are other influences and strategic levers that impact restaurant revenue and profit generation, such as, but not limited to, menu design, menu pricing (including psychological pricing strategies and dynamic menu pricing), food cost ratios, menu item contribution margins, and data collection quality.

As a first step towards data collection and building up comparison data, it is recommended to disaggregate the number of seats and service hours in the RevPASH formula, so analysts can gain a deeper understanding of the impact of these two drivers and strategic levers, while keeping a close eye on any changes, anomalies, or errors.

Both the common formula presentation and the preferred formula presentation suggested in the article result in the same answer, and all good analysts should be able to drill down and detect any underlying changes regardless of which version they use. However, the preferred approach highlighted in this article allows for a more targeted and consistent focus on two changing variables and increases the usefulness of the metric when comparing data trends over time, consequently aiding more informed decision-making, and reducing the risk of oversight.

By presenting the RevPASH formula as REVPASH = (Total Service Revenue / Number of Service Hours) / Number of Seats and incorporating seat utilisation or seat churn ratios, analysts can effectively make data-driven decisions to improve restaurant revenue and profit performance.