Maximize Parking Revenues with Smart Pricing Strategies
Based on forecasts and unique machine learning algorithms, it finds the optimal price to set for a given booking request, considering the alternatives and their degree of certainty.
Direct Impact on Revenues
By defining online pricing tactics (yield strategies), managers can have a direct influence on parking revenues.
Revenue Increase
A minimum of 5 to 10% additional revenues is typically observed after a 6-month transient period.
Features K-Yield
K-Yield has been designed with the most innovative algorithms to find the optimal trade-off to find between the various lengths of stay expected to enter the parking.
Innovative Algorithms for Optimal Trade-offs
K-Yield finds the perfect balance between various expected lengths of stay in the parking area.
Machine Learning Optimization
Using machine learning and advanced algorithms, K-Yield ensures superior demand prediction and inventory management.
Beyond Occupancy Forecasting
K-Yield focuses on revenue drivers, not occupancy forecasts, for more optimized pricing.
Comprehensive Predictive Models
K-Yield combines historical data with real-time demand to deliver precise forecasts for bookings and drive-ups.
Customizable Seasonal Models
Easily adapt models for events, maintenance, holidays, and other special situations.
User-friendly Interface
K-Yield’s intuitive interface makes price control effortless and aligns with your sales strategy.
How is K-Yield
is implemented?
The implementation of K-Yield takes a few days, depending on the availability of the data:
Understanding phase
Comprehension of the local parking demand drivers, market habits, marketing positioning of the various products, past pricing policies, occupancy constraints, etc.
Forecast fine tuning phase
Kowee data scientists adjuts the Kowee machine learning models, together with managing of the calendar matching rules and Yield expert parameters
Yield strategy definition
Including customer segmentation, dynamic pricing simulation over a future demand period (and related gain estimates), etc.
Needed data
The sources of data required for the implementation of our Parking Yield Management tool are equivalent to those used for the K-Analytics Business Intelligence and K-Pricing Revenue Optimization Softwares.
Historical Data Requirement
A minimum of 1 to 2 years of past individual transactions (tickets) is necessary.
This applies to both the gate system data and, ideally, the booking system.
Required APIs for Third-Party Integrations
Real-time connection with the gate system
Ensures the latest car park status (entries and exits) is always updated
Real-time connection with the e-commerce platform
Ensures instantaneous responses to any booking requests.
