Car Park Revene Managment
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Transcript of Car Park Revene Managment
© 2015 GrayMatter All Rights Reserved
AA+ (CPRM) Car Park Revenue Management
© 2015 GrayMatter All Rights Reserved 2
• AA+ CPRM Implement s a demand driven dynamic pricing system for all the car parks in Airport– Car park prices will be updated every day for next X Days in advance
• Dynamic pricing will result in – Increase in the car parking revenue
– Increase in occupancy levels in all the car parks managed by Airport
• Pricing decisions should adhere to business rules and constraints
• System should monitor demand and decision accuracies and recalibrate when demand predications deviate significantly from actuals– Recalibration can be a mix of automatic and manual tuning of models and
algorithms
AA+ CPRM Business Objectives
© 2015 GrayMatter All Rights Reserved 3
• AA+ CPRM is an extension to AA+ car park analytics framework for revenue optimization
• AA+ CPRM is built on advanced statistical models with several linear and non linear variables for occupancy predictions
• AA+ CPRM system predict and recommends future price every day for few days ahead based on historical data analytics, algorithms and business rules
• AA+ CPRM provides user interface to control and override recommendation as needed
• AA+ CPRM provides ongoing basis monitoring and remodeling of algorithms for model optimization
Introduction to AA+ CPRM
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• Changing demand (supply and demand curves)– Competitor pricing and other actions
– External factors (weather, etc.)
• Demand management– Trying to make as much money from selling a limited capacity as possible
– Stimulate demand when it is low
• Hedging– Customers are rewarded with discounts for committing early
– Limit the effect of, e.g., bad weather
• Willingness to pay of different market segments– Customer differentiation and price discrimination
AA+ CPRM built on following Concepts
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• A demand model is a mathematical or pattern based function of independent attributes
• Several algorithms are used to infer this function– Triple Exponential Series
– Auto Regressive Integrated Moving Average
– Seasonality & Growth Models
– Linear and Non-Linear Regression
– Pattern Based Models
• Selection of the algorithm is done as part of model validation using “accuracy” of prediction and “uncertainty” in predictions– Accuracy measures how close predicted demand is with actuals
– Uncertainty measures spread in the prediction error
• Sometime ensemble models are also deployed. Model selection is driven by the data
AA+ CPRM Model Overview
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CRRM Solution Architecture Overview
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Data Requirements
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Data Needed
PURPOSE MODELLING DECISION MONITORING
Transactions Completed Transactions for past 24 months
Incremental Completed TransactionsandIn-Park TransactionsCars which have arrived in the carpark but not yet departed
Both Completed and In-Park Transactionsof Recent past weeks (2-4)
Reservations Completed Reservationsfor past 24 monthsCars have either arrived and departed or booking cancelled or Noshow
Incremental Completed Reservationsand On-Books ReservationsThese are reservations where arrival date is in future
Both Completed and On-Books Reservationsof Recent past weeks (2-4)
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• Transaction ID• Carpark• Date and Time of Arrival• Date and Time of Departure• Type of Arrival: PreBook or DriveIn• Parking Charges
– Split charges based on components like Parking, Tax, Commissions
• Discount/Promotion Coupon, if any– Employee Discount – Free Parking
• Customer Type– Employee/Staff Vehicle– Maintenance Vehicle– General Vehicle
• Booked Date and Time of Arrival• Booked Date and Time of Departure• Booked parking rate
Completed Transaction Data
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• Transaction ID
• Carpark
• Date and Time of Arrival
• Type of Arrival: PreBook or DriveIn
• Prorated Parking Charges– Split charges based on components like Parking, Tax, Commissions
• Discount/Promotion Coupon, if any– Employee Discount
– Free Parking
• Customer Type– Employee/Staff Vehicle
– Maintenance Vehicle
– General Vehicle
• Booked Date and Time of Arrival
• Booked Date and Time of Departure
• Booked parking rate
In-Park Transaction Data
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• Reservation ID• Carpark• Date and Time of Reservation• Date and Time of Cancellation• Planned Date and Time of Arrival• Planned Date and Time of Departure• Discount/Promotion Coupon, if any
– Employee Discount – Free Parking
• Customer Type– Employee/Staff Vehicle– Maintenance Vehicle– General Vehicle
• Number of parking slots needed• Parking Charges, split if any• Status
– No Show, Used as planned, Early Exit, Early Arrival, Delayed Departure, Delayed Arrival
Completed Reservation Data
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• Reservation ID
• Carpark
• Date and Time of Reservation
• Planned Date and Time of Arrival
• Planned Date and Time of Departure
• Discount/Promotion Coupon, if any– Employee Discount
– Free Parking
• Customer Type– Employee/Staff Vehicle
– Maintenance Vehicle
– General Vehicle
• Number of parking slots needed
• Parking Charges, split if any
On-Books Reservation Data
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• In addition to the transaction and reservation data, we will also need carpark meta data which includes– Carp park name
– Type of carpark
• Multilevel
• Valet/Drop off
• Park & Ride
– Capacity
– Number of hours of stay classified as Short Stay
• This data will be captured once and an interface will be provided to add/delete/modify information
Meta Data
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High Level Solution Approach
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High Level Solution ApproachA
A+
War
eho
use
Res
erva
tio
ns
Historical DataExtraction Descriptive
AnalyticsData
CleaningModelling
Use
r In
terf
ace
and
Das
hb
oar
ds
IncrementalData - Daily
MODEL
Prediction Optimization
Pre
dic
tio
n
&
Dec
isio
ns
Decision upload to reservation system(s)
Are Predictions
and Decisions “Good”
Recalibrate Model
No Yes
BusinessRules
Modelling, Daily Decisions, Monitoring
Patterns –booking / Arrival
Special Events
ConstrainingOutliers
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• Three stage solution
• Historical data processing– Uses past 2+ years of historical transactions and reservations
– Data cleaning – outlier detection, identifying constrained periods & special events
– Customize demand prediction model for ADM
• Daily Decision Optimization– Uses recent transactions & reservations
– Using business constraints, predictions generates pricing decisions
• Monitoring Process– Monitors patterns and predictions in a predefined frequency
– Significant deviations results in alerts and re-modelling activities
High Level Solution Approach
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• Data cleaning and imputation of missing data
• Identify outliers and associate reasons with outliers– Low occupancy with maintenance of car park
– Low occupancy with airport closure due to weather
– High occupancy with holidays
• Extract of useful insights/ patterns from the Car Park data, like– Special Events, Price Elasticity, Seasonality, Cancellation rates
Historical Data Analysis
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• Car park demand is a function of several attributes like,– Seasonality (Week of Year (WOY), Day of Week (DOW), Special Events- Holidays, Extended
Weekends, Conference, ...– Length Of Stay (LOS)– Type of carpark, Price– PAX volume , Competition
• Using statistical testing techniques, extent of the impact of each of the attributes on the demand is inferred– Time Series– Pattern based– Regression
• Historical price changes are used to extract price elasticity and cross elasticity functions
• In order to reduce uncertainty in the demand predictions, demand groups having similar demand functions are made– Clustering algorithms are used to find these demand groups
• Demand models are build for each demand group. Group demand is the distributed to lower granular element
Prediction – Car park Demand Model
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• Dynamic pricing comes in two scenarios– Demand exceeds the carpark capacity
• Optimizer should not allow lower rates/discounts to be offered
– Demands are very low compared to capacity
• Optimizer should attract more demand by reducing the rates/offering more discount
• Optimizer will scan price and demand space and select a price for each carpark & LOS to achieve the objectives within the business constraints
• Objectives for the optimizer can be to maximize revenues and occupancy levels across all the carparks
Car Park Optimizer
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• Quality of decisions depends on the forecasts which in turn is a function of various patterns and models
• System will monitor Occupancy Levels, Revenue per slot and predictions v/s actual arrivals by LOS on a weekly basis
• Alerts are raised when monitored metrics crosses a threshold
• Pattern monitoring and model re-validations are carried out once a quarter
Monitoring
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• User Interface to evaluate the decision provided by system– Workflow approval process to override any decision
• Configuration master data management, capture competition and other external data
• Business Rule Configuration UI
• System Monitoring UI
User Interfaces
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• Data Connector to upload data from decision database to car park reservation system
Connector to reservation Systems
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Thank You