Advanced Airport Commercial Analytics for Commercial Function from GrayMatter Airport Analytics...

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Advanced Analytics for Airports Commercial Function

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• Business Challenges

• Problem Statement

• GrayMatter’s Airport Analytics (AA+) Solution Explained Retail Revenue

Car Parking Revenue

• Data Modelling Process

• GrayMatter Centre of Excellence for Data Science

Agenda


Business Challenges


Decreasing aeronautical

revenues

Pressure on Profitability

Need for increase in non-aeronautical

revenues

Pressure on Profitability


Problem Statement


How to maximize Non-Aero Revenue?

Ads & Others

Parking

Retail (Revenue Comm. & Rentals)

How to increase per customer retail revenue?

How to determine optimal rentals that can be obtained from stores?

How to maximize car parking utilization?

Key Sources of Non-Aero Revenue


GrayMatter’s Airport Analytics Solution (AA+) Explained


Retail Revenue


Key Business Drivers

Passenger Profile:• Income• Nationality• Purpose of flight• Demographics

Commercial Offer:• Shop Profiles• Variety• Price

Architecture:• Layout• Store Location• Traffic flow

Passenger Spending Rate

Passenger Footfall

Passenger Conversion Rate

Revenue


• Retail surface density to maximize performance

Store right sizing

Store location

• Increase footfall and time spent in shopping

Aligning stores with the gate

Gate reallocation to direct traffic flow

• Increase conversions

Product mix and display adapted to passenger profiles and liking

Airport Retail Revenue Optimization Approaches


• Store location is one of the most important factor influencing performance of the store

• Depending on the class of store and segment of customers it attracts, it is important to locate the store at the right traffic flow

• Trend and correlation analysis of store size and revenue per departing passenger allows resizing store or commercial contracts

Store Size and Location


• Another important factor to influence revenue is to make the stores attractive to the customers

• Understanding customers and their likes enables

Correct store mix at the airport

Product mix and display in the store

• This enables to understand whether customer is price sensitive, their buying preferences, based on their demographics, purchase history

Customer Segment Profiling


• Airports have unique advantage of controlling the footfall

• Based on the profile of passengers, reallocation of gates can result in increased footfall to a store, which are likely to convert

• Correlating profiles of passengers (both departing and arriving) with land side store performance can help in making land side stores more attractive for passengers

Increasing footfall


Detailed Use Case: Store Size and Location


Use Case: Store Size and Location - Attributes

• Store Attributes– Store Name

– Store Classification

• Fast Food, Beverages, F&B, Fashion, Large Format,…..

– Store Size / Area

– Location

• Closest Gate, Security, Air-Side, Land-Side

– Days in Operation

• Departure Gate Attributes– Gate

– Flight Details

• Airline, Destination, #Passengers (Total Aircraft Capacity), Allocated Date Time, Departure Date Time (Scheduled and Revised), Passenger break by nationality would be ideal

– Neighboring gates


• Arrival Flight Attributes– Arrived from, Airline, #Passengers (total capacity), Arrival date and

time, Late minutes

• Product Attributes– Purchase Date/Time

– Store

– Product (and Product Classification)

– Price / Qty.

– Nationality (DFS if available)

• Performance Measures– Revenue

– #Transactions

Use Case: Store Size and Location - Attributes


• Analysis– Study correlation between various attributes with Revenue and

#Transactions

– Correlation between total passengers near store and revenue and #Transactions• Study map of departing passenger traffic with heat map of store sales

• Total arriving/departing passengers and land side store performance

– Store Model• Additional revenue with change in size and/or location

• Recommendation– Under performing Stores

• Actions required to improve their performance– Terminate, Resize, Relocate

– Other Stores which will benefit resizing and relocation

Use Case: Store Size and Location


Car Parking Revenue


• Industry Analysis Industry growth/de-growth (airport passenger volumes)

Competitor pricing

• Demand Management Fluctuating demand with seasonality, unplanned events further skewing

the predictability

Optimal pricing to reduce vacancy and maximize per slot revenue

• Customer Segmentation Identification of different customer segments and adopting differential

pricing

• Hedging Risks Mitigating revenue losses due to “force majeure” incidents

Promotional schemes to ensure committed revenues in advance

Key Business Drivers


• Airport Analytics (AA+) CPRM Implements 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

• User interface to control and override recommendation as needed; ability to do what-if analysis

Solution Outline


• 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 according to specific business needs

• Decision Optimization Factors affecting demand analysed

Historical data as well as recent trends included in analysis

Demand models generated for identified demand groups

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

3 Staged Solution Approach


• 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


• 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

• Recent trends in transactions and bookings factored to include recency effect and enable daily optimization

• 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

Decision Optimization


• 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 to achieve the objectives within the business constraints

Decision Optimization


• 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


DATA MODELLING PROCESS


Typical Approach

Data Preparations and understand Business process

Data Analysis

REPORTS

Predictive Models

Prediction ValidationPredict /

Optimize/Simulate/What-if

Data Cleaning

Patterns and

Clusters

Descriptive Predictive Decisions

Historical Data

Current Data

Granularity DecisionsOutliers Missing Integrity


GrayMatter Center of Excellence

for

Decision Science


Data Science – Service Offerings

• Horizontal

• Vertical

• Retail & CPG

• Airport

• Insurance

• BPO

• Data Mining Process

• Tools

• R, Weka

• KXEN, SAP PA

• Big Data

• Predictions

• Decisions

• Reports

• Discovery Workshop

• Data Models

• Data Cleaning and analysis

• Predictive and decision Modelling

• Team HandholdingConsulting

Analytics As A

Service

SolutionsTrainings


Vertical Solutions

• Workforce planning

• Agent’s Productivity

• Recruitment• Persistency / Lapse

• Agent performance

• Fraudulent Claims

• Optimum Store size and location

• Customer Segment Profiling

• Gate Allocation

• Car Parking Revenue Optimization

• Price Elasticity

• Price Optimization

• Allocation and Replenishment

Retail and CPG

Airport

BPOInsurance


Horizontal Solutions

• Customer/Product Segmentation

• Promotion Effectiveness

• Social Media AnalysisMarketing

• Sales forecastsSales

• Personalized content

• Social Network AnalysisDigital


• Depth in Technology and Data Science capabilities– Dedicated team of data scientists

– Experienced in Machine Learning

• Data Cleaning, Outlier Detection, Statistical Testing

• Classification, Regression, Attribute Selection, Segmentation/Clustering, Forecasting, Association Rule Mining

• Pattern Based Modelling

Team Capability

• Deployed over 30+ predictive and decision models across domains– Planning (Forecasting)

– Price Optimization (Demand Prediction)

– Allocation and Replenishment (Demand Prediction)

– Churn and Persistency (Likelihood Prediction)

• Strategic Partnership with SAP and Revolution Analytics


Technologies


Case StudyStore Performance at Airports

Business ProblemAirports are interested in getting a good estimate of revenues in next 12-18 months from various stores. This helps them in their yearly financial planning (a) Identify potential non-performing stores to take proactive actions (b) Consider prospect of new stores

Solution ApproachA custom growth-seasonality model was build using weekly sales by store

Company and Store level special events are identified using outlier detection methods. A separate special event model is developed and overlaid on the growth-seasonality model.

Stores with similar seasonality were grouped together and their seasonal patters were extracted. Similarly Store with similar growth/decay trends were grouped together and combined parameters were extracted.

Another model to detect recent inflexion points was build and data after inflexion point was used for growth/decay trends.

Solution is deployed as a web application integrated with the clients planning system.

BenefitsAutomated , integrated financial projection system with what-if analysis. Since it uses both long term and short term effects, accuracy of predictions are better.Accuracy Achieved: Weekly incremental: 10% , Cumulative 12 months: 2% - 5%

Inflection Point


Case StudyDemand Forecasting for Retailers

Business ProblemPredicting accurate demand for each item in a store is a challenge for all the retailers for three reasons (1) Large number of item and Store combinations (2) Low volumes of sales at item/store level (like fashion apparels) and (3) Short shelf life (perishable items). An automated and reliable demand predictions are needed to assist in planning and operational decisions.

Solution ApproachUsed recent 3 years of historical data (1) POS Transactions – sales, returns, audit, (2) Price changes, Inventory movements & PO and (3) Product and Store masters

Developed a demand model as a collection of several patterns – Store & Product Seasonality, Impact of time on Sales & Sell-Thru, Effect of Special Events, Impact of inventory and price elasticity.

These patterns are extracted using segmentation & clustering, regression, outlier detection and time series algorithms.

Demand is computed as a mathematical combination of these patterns with uncertainties.

This model has been deployed in several retail solutions like Price optimization, Allocation, Order and Replenishment

AccuraciesCompany level weekly item forecast ranges between 10% to 15% while season level accuracies are in 5% - 8% range


Thank You

Advanced Airport Commercial Analytics for Commercial Function from GrayMatter Airport Analytics...

Data & Analytics

Transcript of Advanced Airport Commercial Analytics for Commercial Function from GrayMatter Airport Analytics...