Demand pricing has been a common theme in the parking world in recent years. The goal, of course, is being able to adjust pricing in a particular zone, garage, or lot based on demand, i.e., availability of inventory or lack thereof, yielding the highest amount of revenue while not overly impacting the customer experience, think, pricing yourself out of the market.
Demand pricing is a strategy that adjusts prices based on real-time demand. It’s used in various industries, including parking, to maximize revenue. Rates change depending on factors like time of day, events, and occupancy levels, ensuring a balance between supply and demand.
However, it is a sticky subject at the owner level as it requires three aspects: First, legislation in place allowing the fluctuation of prices; second, a prior understanding of how pricing will impact utilization; and, finally, an application that will update pricing on demand based on current utilization.
Enter yield forecasting. The goal of yield forecasting is to help you get to demand pricing. How does that work? Yield forecasting looks at your historical data, occupancy by date and time, weather patterns, enforcement, or any other relevant piece of information, and builds a model out of your data set to identify how much you can charge at a given point in time with the available inventory based on the previously mentioned factors.
This is a bit subjective as every owner might have extenuating circumstances in their area, Hurricanes in Miami, snowstorms in the Northeast, etc., so models must consider each owner/operator’s unique conditions.
Fine-Tuning Parking Models: Balancing Reality and Optimization
These models must be constantly tweaked and worked on to make them as close to reality as possible. For example, we can build a potential yield forecasting model in a particular area based on owners/operators on the ground knowledge, feedback, and utilization.
However, unless you have actual cameras or sensors counting inventory, we do not know TRUE utilization, for example those that have not paid for parking. This differs in a garage or lot, thanks to LPR and/or Revenue Control Gates. It is neither economical nor realistic to expect a parking owner/operator to sit at an open lot/on-street zone and count vehicles every day for a year to check true availability. If anyone promises you that, run.
As mentioned earlier, Yield Forecasting aims to provide stakeholders with information on pricing changes that might impact utilization and revenue. With this information, stakeholders can present a use case scenario to implement demand pricing to increase revenue without negatively impacting supply.
That’s where In-Parking Sight comes in, using yield forecasting to bridge the path to demand pricing. We aim to refine parking models to reflect real conditions, empower stakeholders with insights, and enable effective demand pricing implementation. It’s all about maximizing revenue without compromising supply, and In-Parking Sight is committed to help owners and operators in this data-driven journey.
