The behaviour model of fuel retail consumer
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Transcript of The behaviour model of fuel retail consumer
Department of Marketing
www.buseco.monash.edu
Modelling The Behaviour of Fuel Retail Consumers
Ari Pramono
Department of MarketingMonash University
Structure of the Presentation
Fuel Retailing: Overview
Topic- 1 : Accessibility as a determinant of consideration sets
Topic – 2: Modeling gas station choice as an “on-the-fly” choice process
Fuel Retailing : Overview
• Product– Homogeneity (product specification, price)– No direct substitutes.
• Industry– Horizontal and vertical integration– Importance to economy (Ma et.al, 2011)
• Spatial Economic– Spatial correlation– Firm’s spatial competitive behaviour (Hotelling’s law)– Spatial Interaction.
Fuel Retailing : Overview
• Consumer Behaviour– Mental Map (Dingemans et.al 1986)– Spatio – temporal bounded rationality (Kitamura and Sperling
1987)– Asymmetric competition ( Cooper and Jones 2007)– Choice or Search ? (Dingemans et.al 1986) (Dellaert & Haubl,
2012)
• Modelling :“ spatial choice conducted “while in motion”
On-The-Fly (OTF) Choice ModelOn-The-Fly (OTF) Choice Model
Several complexities in modeling OTF choice….
1. The choice (shopping location) is NOT the destination of the trip!• Distance ‘decay’ definitions?• Accounting of consumer path trajectory
1. Choice set endogeneity• Numerous alternatives• The applicability of stage wise process • Spatio-temporal limitation
1. Accounting for Spatial Structure • Relativity to Consumer Movement
Research Objectives
• Investigating the choice set generation process
• Developing a choice model for the planned choice and ad-hoc search situation.
• Investigate the nature of spatial competition among gas stations.
Topic- 1 : Accessibility as a determinant of consideration sets (*)
• Research questions :– Role of consideration set in spatio-temporally limited situation
• Method : Monte Carlo simulation (estimation: MNL and CMNL)
• Findings :– The role of the consideration set depends on level of accessibility.
The more restrictions are imposed on access, the more important the consideration set becomes in setting the outcome of the choice process.
– Two stage model only performs better in a limited accessibility condition, otherwise it is at par with a one stage model.
– The importance of non-spatial attribute is moderated by spatio-temporal limitation
(*) Has been published in the Journal of Choice Modeling, 5(1), 22 - 45.
Topic- 2 : Modeling consumer behavior during refueling as an “on-the-fly” choice process
Planned Refueling Ad-hoc Search
Behaviour mode Choice mode Search mode
Decision making Compensatory binary
Hierarchical Sequential
Modelling Two-stage One-stage
Trade off betwen spatial & non spatial
Importance of spatial attribute over non-spatial
Model : Fixed Effect Conditional Logit (McFadden 1977) or also referred as Multinomial Logit
Probability of consumer j, characterized by attribute Gj, choosing gas station i, with attributes Xi,j , is calculated as :
Xi,j is vector of spatial and non spatial attributes of i in regards to j Gj is vector of contextual attributes of individual jIi,j is dummy indicating whether i is inside or outside j’s Feasibility Set
n = the total number of gas stations in the set
Regular model OTF model
Spatial Separation effect(distance decay effect)
Distance (or travel cost) between the shopper and the store (or gas station)
Detour distance : the length of deviation from the shopper’s original O-D route needed to reach the store (or fuel station).
Spatial Structure effect(agglomeration effect)
Spatial correlation (or interdependence) among stores (or fuel stations) i.e. clustering or spatial differentiation.
Spatial dominance: The relative intervening sequence of the stores (or fuel stations) in regard to the shopper’s O-D path. (Cascetta & Papola, 2009)
Accounting for Spatial Attributes : OTF vs Regular spatial model
Methodology
Field Survey
Choice
Station Data
Trip Data Quality Index
StationCoordinates
Centroid coordinate
Distance matrices calculation
Detour distanceSpatial Dominance
OTF Model Development
Regional Digital Map
O-D Data
Tank levelTime of day
RevealedRevealed
StatedStated
Model Estimation : Sensitivity Method
origin Destination
MNL Choice Search
Coeff t-stat Coeff t-stat OTF t-stat
Cons 1.447837 5.73 1.536715 5.98 -0.1018341 -0.2
cons2 -12.72077 -0.02 -15.99286 -0.02 -1.544937 -0.6
Detour -0.0006438 -9.24 -0.0005171 -6.14 -0.0002847 -4.84
Quality 0.0622467 1.82 0.2163645 3.59 0.001141 0.04
log (dominance) -0.7883585 -5.03 -0.7952468 -5.07 -1.202707 -4.61
detour x midday -0.0003127 -2.25 -0.0002737 -2.01 -0.0001101 -1.17
quality x midday -0.0362741 -0.66 -0.0110604 -0.2 0.0186984 2.03
dominance x mid day 0.1240502 0.49 0.12658 0.5 -0.1208099 -0.29
detour x low tank 0.0001003 0.92 0.0000681 0.66 -0.0000653 -0.59
quality x low tank 0.0161144 0.3 0.0009624 0.2 -0.023841 -3.54
dominance x low tank 0.3653381 1.52 0.3698403 1.53 0.650738 1.44
threshold (�) 18.08971 18.09 1.076813 1.82 � x quality -0.1988119 -0.2 -0.0045611 -0.69
FOS 1.092104 3.82
FOS x tank 0.2120251 0.44
FOS x off peak -0.885995 -2.03
Importance of Station Quality only in two -stage
Importance of Station Quality only in two -stage
Location is more important in searchLocation is more important in search
In off-peak hours motorists are less concern about location
In off-peak hours motorists are less concern about location
In off-peak hours Quality becomes slightly important in search mode
In off-peak hours Quality becomes slightly important in search mode
But if the tank is low ... they don’t care about quality
But if the tank is low ... they don’t care about quality
Evidence of Feasibility Set Evidence of Feasibility Set
Motorists stick to their “mental map”Motorists stick to their “mental map”
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0 250 500 750 1000 1250 1500 1750 2000 2250 2500 2750 3000
Probability to be choosen
distance (meters)
Quality=92
Quality=87
Quality=85
Quality=83
Quality=81
Quality=93
Quality=91
The impact of distance for different Station Quality level
1. Consumer is first concern about distance (only choose stations within < 1.5 Km) .2. Except for best quality stations , they allow for additional 500 – 750 meters
Willingness To Travel
Consumers in search are more affected by spatio-temporal limitations, compare to consumers in regular planned choice
Choice mode
Search mode
Role of Spatial Dominance
Consumers in search mode will most likely to stop in station 1, regardless the quality of station-2.
Consumers in choice mode will most Likely to continue to station-2 if It has at least 2 quality score higher than station1
SSM = Spatial Structure Model (Timmermans & Borgers (1985)CDM = Competing Destinatin Model (Fotheringham, 1988)
Our model outperforms the previous model
Our model outperforms the previous model
Comparison with existing Model
0.00%
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60.00%
70.00%
80.00%
90.00%
100.00%
1 2 3 4 5 6 7 8 9 10 > 10
Cumulative Probability
The maximum predicted Rank of the actual choice
OTF
SCM
CDM
Predictive Capability
Our model can predict the consumer’s preffered choice alternatives with 80% accuracy
Map of sales in gas station (kiloliters/day)
The origin location of demand (kiloliters/day)
Our model can be usedTo predict the origin ofdemand based on the salesin gas station
Example : city of Bandung, Indonesia
Conclusions• Both modes of Refueling activity (Choice and Search) can be effectively modeled as “on the
fly” spatial choice model based on typical MNL framework.
• Choice mode is more aligned with planned behaviour. – compensatory trade offs between both spatial and non spatial attributes – 2 stage processes (Consideration stage + Choice stage).
• Search mode tends to be a sequential decision behaviour,– Importance of spatio temporal limitations – Saptial over non-spatial attractiveness – stage wise choice set development become less relevant.
• The study introduce the methodology to account for complexity casued by decision maker’s movement in the spatial choice model.– Spatial cognition (Mental Map)– Spatio-temporal limittaion
Implication & Further ResearchThe On-The Fly model could potentially be applied for study about :
– Perceptual (behavioral) based spatial competition modelling.– Multipurpose shopping (activity) modelling.– Studies involving analysis of path movement (Hui, Fader & Bradlow
2009):• Supermarket (Grocery) shopping• Eye tracking movement• Web browsing• Pedestrian movement