Kowee solutions

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-Analytics

K-Analytics is a series of enriched car park revenue management dashboards, helping you control the economic performance of your car parks.

Benefits fake

Thanks to K-Analytics, one can really move from intuition to data centric management of its car parks.

This module paves the way for the implementation of sound dynamic pricing policies.

Benefits

Thanks to K-Analytics, one can really move from intuition to data centric management of its car parks.

This module paves the way for the implementation of sound dynamic pricing policies.

Needed data

Data from the equipment system (the “gate system”) as well as from the parking e-commerce platform (if it exists):

  • Data are daily transfered when K-Yield is not put in place, otherwise a real-time connection with the system is required
  • In all cases, the content is limited to, per individual transaction: date and time of entry, payment amount, date and time of exit, customer type, etc.
  • APIs with the most important equipment system providers are available

Other sources of data can be used: forecasts of passenger traffics (for airports), competition information (if relevant, price tracking feauture is proposed).

Features
Specific parking revenue management KPIs are made available thanks to interactive business-driven dashboards: 

    • Car park occupancy curves, incl. customer type occupancy distribution (per day, time, etc.)
    • Entry and exit distribution patterns: per length of stay segment, etc
    • Multi-axis comparisons vs the year before, etc.
    • Lead time analysis for pre-bookers
    • Competition positionning
    • Correlation with passenger traffic (for airport car parks)

Around 70 reports are available (in case of specific needs, customization is possible as long the data exists). Secured 24/7 web access to the reports.

Implementation
The implementation of K-Analytics takes a few days, depending on data availability:

  • The first phase consists in the integration of 2 or 3 years of past data
  • In parallel, the daily API is put in place together with the third party team (gate system provider for ex.)
  • Controls and tests are performed (vs turnover and volume of transactions) to ensure 99.9% accuracy in the data processing aspects (lost tickets, manual counting processes, etc.)
  • Knowldege transfer is ensured by Kowee teams
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K-Pricing

K-Pricing is a pricing simulator software dedicated to the car park sector including multi price grids management, seasonality management, tariff grid structure evolutions, price elasticity hypothesis, etc.
Benefits

Thanks to K-Pricing, one can see in a few seconds the potential revenue impact of any pricing policy change.

Based upon the real time re-processing of past tickets that are automatically made available, it gives you all the elements on the relevancy of pricing policy changes.

This solution is a complement to K-Yield as it primarily addresses the optimization of the drive-up pricing policies.

Benefits
Thanks to K-Pricing, one can see in a few seconds the potential revenue impact of any pricing policy change.

Based upon the real time re-processing of past tickets that are automatically made available, it gives you all the elements on the relevancy of pricing policy changes.

This solution is a complement to K-Yield as it primarily addresses the optimization of the drive-up pricing policies.

Needed data

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

Data from the equipment system (the “gate system”) as well as from the parking e-commerce platform (if it exists):

  • Data are daily transfered when K-Yield is not put in place, otherwise a real-time connection with the system is required
  • In all cases, the content is limited to, per individual transaction: date and time of entry, payment amount, date and time of exit, customer type, etc.
  • APIs with the most important equipment system providers are available

One can easily retrieve a set of past data, ticket transactions as well as applicable price tables, and base its simulations on it.

Features

The real time simulation of pricing policy changes makes it a powerful decision-making tool for car park managers.

The following questions are most easily addressed:

  • Should we change the prices after 1 hour of stay? Or 45 minutes? Between 4 and 8 hours of stay in the parking?
  • Should we introduce seasonal rates? A weekend package?
  • What are the threshold effects of any change in the pricing structure?
  • How many vehicles are affected? Which customer segments will be proposed different tariffs than before?
  • What are the consequences on revenue?
  • What are the most likely hypothesis regarding client behavior?
  • Will the demand volume decrease if the price is raised by x%? In what proportions and with what effect on the revenues? What is the best trade-off between price and volume?

K-Pricing is for parking managers who do not want to spend hours in getting data sets and complicated excel sheets to simulate pricing changes.

Implementation
The implementation of K-Pricing is very similar to that of K-Analytics. It takes only a few days depending on data availability (from the gate system):

  • The first phase consists in the integration of 2 or 3 years of past data
  • In parallel, the daily API is put in place together with the third party team (gate system provider for ex.)
  • Controls and tests are performed (vs turnover and volume of transactions) to ensure 99.9% accuracy in the data processing aspects (lost tickets, manual counting processes, etc.)
  • Past pricing policies are entered in the system
  • Knowledge transfer is ensured by Kowee teams
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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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Solutions

K-Analytics

K-Pricing

K-Yield

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