Multichannel Management - Gottfried Gruber - Peter Lang Publishing
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FORSCHUNGSERGEBNISSE DER
W I R T S C H A F T S U N I V E R S I TÄT W I E N
Gottfried Gruber
Multichannel
Management
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via free accessFORSCHUNGSERGEBNISSE DER
W I R T S C H A F T S U N I V E R S I TÄT W I E N
Gottfried Gruber
Multichannel Management
A company operating various sales channels, e.g. the Internet and a traditional
shop, inevitably faces a tricky coordination problem. As prevalent approaches
often do not lead to a satisfying solution, the author suggests a normative model
to offer directions for the optimal channel coordination. The model is based on
stochastic purchase and switching probabilities, given certain conditions like prices
and supportive marketing activities (like delivery time or shop environment). A
company can fit its consumer base to the model and simulate various effects on
its earnings by altering prices or marketing activities. The model is a market-based
playground to develop new holistic strategies for a multichannel company without
affecting the market.
Gottfried Gruber studied economics and computer science (Wirtschaftsinformatik)
at the Vienna University of Technology from 1998–2003. Until 2006 he successfully
completed the Center for Central European Financial Markets Program as well as a
portfolio management program. From 2006 until 2009 he earned a doctor’s degree
at the Vienna University of Economics and Business. During his studies he was
employed at software companies, telecommunication companies, and banks.
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via free accessForschungsergebnisse der
Wirtschaftsuniversitat Wien
Band 36
•
PETER LANG
Frankfurt am Main · Berlin • Bern · Bruxelles• New York· Oxford · Wien
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Multichannel Management
A Normative Model Towards Optimality
4
PETER LANG
lnternationaler Verlag der Wissenschaften
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of ZBW – Leibniz-Informationszentrum Wirtschaft.
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ISSN 1613-3056
ISBN 978-3-631-59445-2
ISBN 978-3-631-75387-3 (eBook)
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via free accessAbstract
The thesis deals with pricing strategies for multichannel retailers, especially traditional stores
which additionally manage an online shop. The problem of integrating two sales channels and ap-
plying a well-suited pricing strategy is still an emergent question. This work develops a stochastic
model to represent consumer behavior on pricing. On the one hand the model contains two prob-
ability functions which render consumers' reservation prices for each individual channel. On the
other hand the stochastic model is based on numerous distributions which represent switching
probabilities from and to each separate channel. The various distribution functions will be esti-
mated from the results of a survey. To highlight differences of pricing strategies due to several
product categories a cross comparisons of books, clothes and digital cameras will be presented.
The results show that there are differences in multichannel pricing of the various products. These
inequalities stern from consumers' perceptions of the sales channels. For each product a separate
sales channel is preferred by consumers. Therefore, one channel exhibits some advantage versus
the alternative channels. This advantage is reflected in different pricing strategies. Further appro-
priate marketing strategies could help a firm to counter discounting by its competitors. So firms
should keep an eye on the reservation price structure of its consumers as well as their demanded
marketing activities.
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Diese Arbeit beschiiftigt sich mit der Preispolitik im Mehrkanalvertrieb, im Speziellen wer-
den traditionelle Ladengeschiifte die auch einen Onlineshop betreiben untersucht. Die Inte-
gration mehrerer Vertriebskaniile und die Realisierung einer entsprechenden Preisstrategie stellt
noch immer eine kritische Frage dar. In dieser Arbeit wird ein stochastisches Modell en-
twickelt, dass das Einkaufsverhalten der Konsumenten darstellt. Das Model! besteht aus zwei
Wahrscheinlichkeitsfunktionen, die die Reservationspreise der Konsumenten in jedem Vertrieb-
skanal repriisentieren. Ferner basiert das Modell auf mehreren Wahrscheinlichkeitsfunktionen,
die die Wechselwahrscheinlichkeiten zwischen den verschiedenen Kaniilen darstellen. Die un-
terschiedlichen Wahrscheinlichkeiten werden mithilfe einer Umfrage geschiitzt. Differenzen in
der Preispolitik werden anhand von Biichern, Kleidung und Digitalkameras erschlossen.
Die Unterschiede in der Preispolitik starnmen von unterschiedlichen Wahrnehmungen der Ver-
triebskaniile durch die Konsumenten. Fiir jedes Produkt wird ein anderer Kanai von den Kon-
sumenten bevorzugt. Diese Vorliebe ermoglicht unterschiedliche Preisstrategien. Des Weit-
eren kann durch eine angepasste Marketingstrategie besser auf Preisattacken von Mitbewer-
bern reagiert werden. Daher sollten Unternehrnen sowohl die Reservationspreise ihrer Kunden
beobachten als auch deren geforderten Marketinghandlungen anbieten.
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Firstly, I like to take this opportunity to thank the people who provided scientific support to make
this work possible.
I would like to express profound gratitude to my advisors Prof. Hans Robert Hansen and Prof.
Kurt Hornik for their invaluable support, encouragement, supervision and useful suggestions
throughout this research work.
I am grateful for the cooperation of two companies by allowing me to publish my survey on
their web presence. I really appreciate the kindness of Mag. Anja Sickinger (thalia.at) and Mag.
Evelyn Thumb (geizhals.al) for their support in advertising my survey. Moreover, I would like to
acknowledge all of my respondents who answered my survey.
My sincere thanks go to many friends and colleagues from the Institute for Management Infor-
mation Systems, Vienna University of Business Administration, for scientific discussion, advice
and continuous support always so greatly appreciated.
Finally, I want to thank my family for their love and support. My parents raised me to believe
that I could achieve anything I set my mind to.
Gottfried Gruber, Fall 2008
Its not that I am so smart.
Its just that I stay with problems longer.
(Albert Einstein)
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1 Introduction 19
2 Literature Review 23
2.1 Price Levels . 24
2.2 Price Elasticity 25
2.3 Price Dispersion . 26
2.4 Search Costs . . . 27
2.5 Transactions Costs 27
2.6 Pricing Strategies . 28
2.7 Task Definitions . . 31
2.7.1 Physical Surroundings 32
2.7.2 Social Surroundings 33
2.7.3 Temporal Aspects . 33
2.7.4 Task Definitions 34
2.7.5 Antecedent States . 34
2.8 Reservation Prices . 35
3 Research Scope 37
3.1 Discussion of Conditions 38
3.2 Discussion of Strategic Decisions 39
3.3 Market Model . . . . . . . 40
4 Conceptual Consumer Model 43
4.1 Demographics . 44
4.2 Product .... 44
4.3 Shopping Goal 46
4.4 Latent Demand 47
4.5 Formation of Reservation Prices 49
4.6 The Product-Shopping Goal Link. 50
s Basic Model 51
5.1 Reservation Prices . 52
5.2 Intra-Firm Switching 55
5.3 Inter-Firm Switching 58
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5.4 Assembling the Model 59
5.5 Expected Value 63
5.6 Price Elasticity . 68
6 Basic Model - Refined 73
6.1 Influence of Marketing 73
6.1.1 Strategic Scope . 75
6.1.2 Product Policy . 77
6.1.3 Communication Policy 81
6.1.4 Distribution Policy 83
7 Simulation Model 85
7.1 The Firm Side . 86
7 .2 The Consumer Side 87
7 .3 Scenarios . . . . . 87
7.4 The Beta Distribution 88
7 .5 Estimating Beta Distributions with Maximum Likelihood . 89
8 Methodology 91
8.1 Survey . . . . . . . . 91
8.2 Sample Constitution . 92
9 Results Books 95
9.1 Marketing Strategies 97
9.2 Elasticity . . . . . . 100
9.3 Mean and Variance . 102
9.4 Consumer Drift Dynamics 103
9.5 Pricing . . . . . . . . . . . 107
10 Results Clothes 111
10.1 Marketing Strategies 113
10.2 Elasticity . . . . . . 117
10.3 Mean and Variance . 118
10.4 Consumer Drift Dynamics 118
10.5 Pricing . . . . . . . . 122
11 Results Digital Cameras 125
11.1 Marketing Strategies 128
11.2 Elasticity . . . . . . 130
11.3 Mean and Variance . 132
11.4 Consumer Drift Dynamics 132
11.5 Pricing . . . . . . . . . . . 136
12 Cross-Comparison 139
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13 Conclusion and Limitations 143
13.1 Conclusion 143
13 .2 Limitations . . . . . . . 144
A Detailed Tables 159
A.I Books . . . 159
A.2 Clothes .. 171
A.3 Digital Cameras . 183
B Questionnaire 195
8.1 Internet Usage . 195
8.2 Shopping Behaviour 195
8.3 Personality Traits . 197
8.4 Purchase Behavior 197
8.5 Demographic ... 203
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2.1 Influences on the Basic Model 24
2.2 S-O-R Model (Belk 1975) .. 31
2.3 Parameters of Task Definitions 32
3.1 Elements of the E-Commerce Business Model (Hansen 1998) . 37
3.2 Market Model . . . . . . . . . 40
4.1 Conceptual Consumer Model . 43
5.1 Probability Density Function of Offline Reservation Prices 54
5.2 Probability Density Function of the Switching Probability from the On-
line Channel to the Offline Channel ................... 57
5.3 Switching Probabilities of the Online Channel Consumer Base of Firm I 59
6.1 Additional Dimensions of the Refined Model (Hansen 2005) 75
8.1 Age Structure . . . . 92
8.2 Educational Structure 93
9.1 Frequency of Relative Price Statements 96
9.2 Importance of Marketing Attributes Offline 98
9.3 Importance of Marketing Attributes Online 99
9.4 Price Elasticity versus the Offline Price of Firm 1 IOI
9.5 Consumer Drift Dynamics ....... 104
10.1 Frequency of Relative Price Statements 112
10.2 Importance of Marketing Attributes Offline 114
10.3 Importance of Marketing Attributes Online 115
10.4 Price Elasticity versus the Offline Price of Firm 1 117
10.5 Consumer Drift Dynamics .......... 120
11.1 Frequency of Relative Price Price Statements 126
11.2 Importance of Marketing Attributes Offline 128
11.3 Importance of Marketing Attributes Online 129
11.4 Price Elasticity versus the Offline Price of Firm 1 131
11.5 Consumer Drift Dynamics . . . . . . . . . . . . 134
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7. l Simulation Scenarios . . . . . . . . . 88
9. l Parameters for the Beta Distributions . 97
9.2 Optimal Marketing Investments JOO
9 .3 Structure of the Reservation Prices 102
9.4 Initial Offset Values . 104
9.5 Results Summary . . . . . . . . . 108
l 0.1 Parameter for the Beta Distribution . I 13
10.2 Optimal Marketing Spending . . . I 16
10.3 Structure of the Reservation Prices 118
l 0.4 Initial Offset Values . 119
10.5 Results Summary . . . . . . . . . 123
11.1 Parameter for the Beta Distribution . 127
11.2 Optimal Marketing Spending . . . 130
11.3 Structure of the Reservation Prices 132
11.4 Initial Offset Values . 133
11.5 Results Summary . . . . . . . . . 137
A.I Elasticity Scenario a (Poi = 0.5,pn2 = 0.7,po2 = 0.6) 159
A.2 Elasticity Scenario /3 (Poi = 0.4,pn2 = 0.2,po2 = 0.3) 160
A.3 Consumer Decomposition Scenario a (Poi= 0.5,pn2 = 0.7,po2 = 0.6) 161
A.4 Consumer Decomposition Scenario /3 (Poi = 0.4,pn2 = 0.2,po2 = 0.3) 162
A.5 Scenario l (pn2 = 0.10001, po2 = Po2 , Sn2 = l, So2 = 0) 163
A.6 Scenario 2 (pn 2 = p13i, po2 = 0.1000 I, SB2 = l, So2 = 0) 164
A.7 Scenario 3 (pn2 = 0.10001,po2 = po2 ,Sn2 = 0,So2 = l) 165
A.8 Scenario 4 (pn2 = ps2,po2 = 0.10001,Sn2 = 0,So 2 = 1) 166
A.9 Consumer Drift Scenario 1 (pn 2 = 0.10001,po2 = po2,Sn2 = l,So2 = 0) 167
A.IO Consumer Drift Scenario 2 (pn2 = PBi ,po2 = 0.10001,Sn2 = l, So2 = 0) 168
A.11 Consumer Drift Scenario 3 (pn2 = 0.1000 I, po2 = Po 2 , Sn2 = 0, So2 = l) 169
A.12 Consumer Drift Scenario 4 (PB2 = PB2,P02 = 0.10001,SBi = 0,S02 = l) 170
A.13 Elasticity Scenario a (Poi = 0.5, PBi = 0. 7, po2 = 0.6) . . . . . . . . . 171
A.14 Elasticity Scenario /3 (Poi= 0.4,pn2 = 0.2,po2 = 0.3) . . . . . . . . . 172
A.15 Consumer Decomposition Scenario a (po 1 = 0.5,p82 = 0.7,po2 = 0.6) 173
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A.16 Consumer Decomposition Scenario /3 (po, = 0.4,pn2 = 0.2,po2 = 0.3) 174
A.17 Scenario 1 (pn 2 = 0. 10001 ,Po2 = po2,;n2 = 1, ;02 = 0) 175
A.18 Scenario 2 (pn2 = p'"n2,po2 = 0.10001,;B2 = l,;02 = 0) 176
A.19 Scenario 3 (PB2 = 0.10001,Poi = P02,;B2 = o,;02 = 1) 177
A.20 Scenario 4 (PB2 = PB2,P02 = 0.10001,;B2 = o,;02 = 1) 178
A.21 Consumer Drift Scenario 1 (PBi = 0.10001,po2 = po2,;B2 = l,;02 = 0) 179
A.22 Consumer Drift Scenario 2 (PBi = PB2,po2 = 0.10001,;B2 = l,;02 = 0) 180
A.23 Consumer Drift Scenario 3 (PBi = 0.10001,po2 = pa2,;B2 = 0,;02 = 1) 181
A.24 Consumer Drift Scenario4 (PBi = PB2,po2 = 0.10001,;B2 = 0,;02 = 1) 182
A.25 Elasticity Scenario a (po, = 0.5,PBi = 0.1,po2 = 0.6) . . . . . . . . . 183
A.26 Elasticity Scenario /3 (po, = 0.4,PBi = 0.2,po2 = 0.3) . . . . . . . . . 184
A.27 Consumer Decomposition Scenario a (po, = 0.5,pn2 = 0.1,po2 = 0.6) 185
A.28 Consumer Decomposition Scenario /3 (po, = 0.4,PBi = 0.2,po2 = 0.3) 186
A.29 Scenario 1 (PBi = 0.1000l,po2 = po2,;B2 = l ,;02 = 0) 187
A.30 Scenario 2 (pn 2 = PB2,po2 = 0.10001,;B2 = l,;02 = 0) 188
A.31 Scenario 3 (PBi = 0.l0001,po2 = Po2,;B2 = 0,;02 = 1) 189
A.32 Scenario 4 (PB 2 = Pih, Po2 = 0.10001, ;n2 = 0, ;02 = 1) 190
A.33 Consumer Drift Scenario 1 (pn 2 = 0.10001,po2 = po2,;B2 = l,;02 = 0) 191
A.34 Consumer Drift Scenario 2 (pB2 = PB2,po2 = 0.10001,;B2 = l,;02 = 0) 192
A.35 Consumer Drift Scenario 3 (PBi = 0.10001,po2 = po2,;B2 = 0, ;02 = 1) 193
A.36 Consumer Drift Scenario 4 (PBi = PB2,po2 = 0.10001,;B2 = 0,;02 = 1) 194
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