Car park yield management

K-Yield

Solutions

From Business Intelligence to Dynamic Pricing

The implementation of Kowee solutions helps you gain a minimum of 5 to 10% additional parking revenues

K-Yield 

*To understand what a “true” car park dynamic pricing solution is, please have a look at this: Parking dynamic pricing: how to not get it wrong

K-Yield is “a true”* car park Dynamic Pricing solution that helps you increase your revenues immediatly.

Its classic version is backed by a pre-booking system and each time a request is received, the K-Yield engine responds with an optimal price.

Another version of K-Yield exists which allows to change prices also optimally but without the need for a pre-booking system: prices evolve dynamically at the parking entrance, displayed on digital panels. This is the so-called K-Yield version for drive-ups (“drive-ups” are vehicles without reservation).

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Benefits

K- Yield is a dynamic pricing (or yield management) software solution specifically designed for the parking sector.

  • By defining on-line pricing tactics (also called “yield strategies“), the manager can have a direct impact on the on-line parking revenues
  • A minimum of 5 to 10% additional revenues are usually observed after a 6-month transient period

 

K-Yield is a sophisticated dynamic pricing solution giving full control on the parking revenues. It is based on the most advanced revenue optimization techniques for drive-ups and pre-bookings at the same time.

Benefits

K- Yield is a dynamic pricing (or yield management) software solution specifically designed for the parking sector.

  • By defining on-line pricing tactics (also called “yield strategies“), the manager can have a direct impact on the on-line parking revenues
  • A minimum of 5 to 10% additional revenues are usually observed after a 6-month transient period

 

K-Yield is a sophisticated dynamic pricing solution giving full control on the parking revenues. It is based on the most advanced revenue optimization techniques for drive-ups and pre-bookings at the same time.

Needed data
The sources of data are equivalent to that of K-Analytics and K-Pricing.

A minimum of 1 to 2 years of past invidual transactions (tickets) is necessary. This holds for the gate system data as well as, ideally, for the booking system (but it is well possible to launch a brand new e-commerce platform, including parking space pre-booking features, together the implementation of K-Yield).

The APIs to build with the third party systems are the following:

  • A real-time connection with the gate system to make sure the latest information about the car parks are “known” by the system: any new entry or new exit may well have an impact on the price proposed for a later booking request
  • A real-time connection with the e-commerce platform (or “parking pre-booking system”), to ensure an instantaneous answer to any booking request
Features

K-Yield has been designed with the most innovative algorithms to address the question of the optimal trade-off to find between the various lengths of stay expected to enter the parking.

  • K-Yield uses innovative machine learning methods combined with cutting-edge optimization algorithms to create superior demand predictions and inventory management.
  • The comprehensive predictive models combine detailed historical demand along with current booking and drive-up demands (K-Yield does not only forecast the bookings but also the drive-ups)
  • K-Yield does not rely on occupancy forecasts to produce optimal prices (because this is not a driver of parking revenue optimization)
  • The seasonal models are easily customizable to properly address special events, car park maintenance periods, holidays, etc.
  • K-Yield is all but a black box: it offers an extremely simplified user interface making it immediate to control / fine tune the proposed dynamic prices in the various so-called Yield situations.
  • The applied Yield policies are therefore always 100% aligned with the sales and marketing strategy of the parking operator
Implementation

The implementation of K-Yield follows follows a specific path to ensure 100% efficiency as for the Yield strategies that will be put in place:

  • An understanding phase: comprehension of the  local parking demand drivers, market habits, marketing positioning of the various products, past pricing policies, occupancy constraints, etc. ; all this is made possible by a preliminary one-off extract of 2 years of past data (bookings and tickets)
  • A forecast fine tuning phase: Kowee data scientists adjuts the Kowee machine learning models, together with managing of the calendar matching rules (past vs future) and Yield expert parameters
  • In parallel, the APIs are put in place between the various third party systems and Kowee’s
  • A phase dedicated to the Yield strategy definition, including customer segmentation, dynamic pricing simulation over a future demand period (and related gain estimates), etc.

All this is ran over a period of 10 to 12 weeks, where Kowee project team will propose recommendations while also ensuring knowldege transfer.

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