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Off-Peak Analysis

Off-Peak Period: Two Possible Approaches

1. Based on Peak Revenue (PR)

A time period is off-peak if its revenue is below a certain percentage of the peak revenue.

Formula:

OPRt<α⋅PROPR_t < \alpha \cdot PROPRt​<α⋅PR

where:

  • OPRtOPR_tOPRt​ = Off-Peak Revenue at time ( t )

  • PR = Peak Revenue

  • α\alphaα = Threshold percentage (e.g., 70% of PR)

2. Based on Average Revenue (AR)

A time period is off-peak if its revenue is significantly below the daily or weekly average revenue.

Formula:

OPRt<β⋅AROPR_t < \beta \cdot AROPRt​<β⋅AR

where:

  • β\betaβ is the threshold factor (e.g., 80% of AR)


Deriving Revenue Difference Between Off-Peak and Normal Days

To quantify the financial impact of off-peak periods, we need to determine the difference in revenue between off-peak and normal periods.

Revenue Difference Formula

ΔR=(AR−OPR)×Toff\Delta R = (AR - OPR) \times T_{off}ΔR=(AR−OPR)×Toff​

where:

  • ΔR\Delta RΔR = Lost revenue due to off-peak periods

  • AR = Average Revenue per unit time (hour, day, week)

  • OPR = Off-Peak Revenue per unit time

  • ToffT_{off}Toff​ = Total duration of off-peak periods

Percentage Revenue Loss Due to Off-Peak Periods

Loss Percentage=(AR−OPRAR)×100\text{Loss Percentage} = \left( \frac{AR - OPR}{AR} \right) \times 100Loss Percentage=(ARAR−OPR​)×100

This formula helps estimate how much revenue is lost during off-peak times.


Example Calculation

Let’s assume:

  • Peak Revenue (PR) = $10,000 per day

  • Average Revenue (AR) = $7,000 per day

  • Off-Peak Revenue (OPR) = $4,500 per day

  • Off-Peak Time Share (ToffT_offTo​ff) = 5 hours out of a 12-hour business day

Revenue Difference Calculation

ΔR=(7,000−4,500)×512=2,500×0.4167=1,041.67\Delta R = (7,000 - 4,500) \times \frac{5}{12} = 2,500 \times 0.4167 = 1,041.67ΔR=(7,000−4,500)×125​=2,500×0.4167=1,041.67

Thus, the revenue shortfall per off-peak period is $1,041.67.

Percentage Revenue Loss

(7,000−4,5007,000)×100=(2,5007,000)×100=35.71%\left( \frac{7,000 - 4,500}{7,000} \right) \times 100 = \left( \frac{2,500}{7,000} \right) \times 100 = 35.71\%(7,0007,000−4,500​)×100=(7,0002,500​)×100=35.71%

So, off-peak periods account for 35.71% revenue loss compared to normal periods.


Using This Formula for Forecasting

By adjusting the ( α\alphaα ) and ( β\betaβ ) values, a business can:

  • Predict revenue differences for different off-peak time scenarios.

  • Determine discount rates or incentives needed to reduce the impact of off-peak losses.

  • Optimize pricing strategies for subscription models based on expected revenue fluctuations.