This document summarizes a presentation given by Dr. Steven D'Alessandro on using agent-based modelling (ABM) in marketing. ABM involves simulating the interactions of individual agents to assess emergence in complex systems. The presentation defines ABM and discusses its goal of understanding phenomena from micro-level interactions. An example ABM uses NetLogo to simulate adoption of 4G technology over 3G based on factors like price and network capacity. The model results suggest planning capacity while not needing full coverage, the old technology's price is more important than the new, and relationship marketing reduces churn. Validation compares the model to a mobile switching study, finding some similarities in key drivers of satisfaction and switching.

1. Agent based modelling (ABM) :
Contributions to marketing theory
and practice
Dr. Steven D’Alessandro
Presentation at Charles Sturt University 27th
of March 2013

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Goal of ABM: Emergence from chaotic
systems
Definitions
• Pepinsky (2005 373-4): “those phenomena that appear at an
aggregate level, based not on specific micro-level interactions of
agents but rather on the complex and often unpredicted effects of
many such interactions”
Gilbert and Troitzsch (2005)
• Irreducible phenomena of systems that paradoxically arise from
micro-level interactions
GOAL OF ABM: simulate emergence from “the bottom up” (micro-
level interactions)

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Why Use ABM?
Gilbert, Jager, Deffuant, & Adjali, (2007). Introduction to special issue of JBR on complex
systems:
• Many consumer markets display complex behaviour, meaning that traditional
forecasting models perform at a level that excludes practical use, for instance
when predicting the market shares of new products or the effects of marketing
strategies. Interaction among consumers, comprising normative influences and
word-of-mouth, is one of the key processes behind this complex market behaviour
Gilbert & Troitzsch’s (2005) seven uses:
1. Gain a better understanding
2. Prediction
3. Substitute for human expertise
4. Training
5. Entertainment
6. Discovery
7. Formalization

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The two way epistemology of ABM

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EXAMPLE: IF WE BUILD IT WILL THEY COME? OR IS THIS ONLY IF
THE PRICE RIGHT? ADOPTION AND USE OF 4G VERSUS 3G
TECHNOLOGY. RESULTS FROM A NETLOGO SIMULATION
SPECIAL SESSION ON AGENT BASED MODELLING ANZMAC
2011

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Overview of the Research Problem
How do managers of networks (cell and broadband) introduce new technology?
• Sale of Motorola Network see Crockett (2008)
New technology may be more expensive and limited in capacity.
• Mah (2008)
Consumers may move to another provider (Churn) if they are not happy with
service.
• Dierkes et al. (2011)
Consumers may make decisions on price and access to new technology.
• Jack(2008) and Poynter (2006).

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Background research.
Access to better technology.
• Chang Hyun, J. and J. Villegas (2008) and Colwell, S. R., M.
Aung, et al. (2008).
Price and Value
• Deng, Z., Lu, Yaobin, Wei, Kwok Kee and Zhang, Jinlong (2010).
Both factors
• Goode, Davies, Moutinho, and Jamal, (2005). Iyengar, et al.
(2008).

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The Netlogo Model: Moving from a 3G to 4G
World
Network
• 4G- Number of 4G cell access points (0-100)
• 3G- Remaining cell access points
• Capacity of each cell (0-100).
Price
• Price of 4G (0-$50)
• Price 3G (0-$50)
Consumers
• Tolerance (of bad service) – (0-5).

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Behaviour of Agents
1. Agents are set with a random allocation of bandwidth requirement
and price acceptance.
2. Agents seek to maximise bandwidth at an access point.
a) Green patch 3G
b) Black patch 4G.
3. Agents seek to minimise price of each offering.
Agents who do 1 and 2 are happy (RED) and don’t move.
Agents who cannot find a combination of 1 and 2 are not happy
(WHITE) and after 10 turns, leave for another provider (they die).
This decided by Churn which can be reduced by Tolerance.

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The Netlogo Model

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Experimental Design
• Independent variables
• ["FourG" 5 10 units]
• ["capacity" 5 10 units]
• ["Price4G" $10 $20]
• ["Price3G" $10 $20]
• ["tolerance" 0 1]
• ["Customers" 500]
• 32 Runs
Dependent Variables
% Happy customers
Loss of customers
Mean use of 3G
Mean use of 4G

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Netlogo: Results and implications for
providers
Planning for capacity is important
but you don’t have to provide
access to all consumers.
The price of the old technology is
more important than the price
of the new technology.
Relationship marketing is
important to increase tolerance
and prevent churn.
Source
Dependent
Variable df F Sig.
Partial Eta
Squared
FourG Happy 1 53.16 0.00 0.05
Mean3G 1 188.75 0.00 0.16
Mean4G 1 881.73 0.00 0.47
Price3G Happy 1 66.24 0.00 0.06
Mean3G 1 55.29 0.00 0.05
Mean4G 1 39.68 0.00 0.04
tolerance Happy 1 118.94 0.00 0.11
Loss 1 69.57 0.00 0.07
Mean3G 1 278.10 0.00 0.22
Mean4G 1 15.46 0.00 0.02
capacity
Mean3G 1 494.02 0.00 0.34
Mean4G 1 559.66 0.00 0.36
FourG * tolerance
Mean3G 1 49.37 0.00 0.05
FourG * capacity
Mean3G 1 5.00 0.03 0.01
Mean4G 1 89.68 0.00 0.08
Price4G * Price3G
Mean4G 1 14.32 0.00 0.01
Price4G * capacity
Mean4G 1 3.83 0.05 0.00
Price3G * tolerance Happy 1 3.97 0.05 0.00
Price3G * capacity
Mean3G 1 4.47 0.03 0.00
Mean4G 1 7.97 0.00 0.01
tolerance * capacity
Mean3G 1 18.43 0.00 0.02
FourG * Price4G *
Price3G
Mean4G 1 9.37 0.00 0.01
FourG * Price4G *
tolerance Mean4G 1 3.15 0.08 0.00

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VALIDATING SIMULATION
RESULTS WITH REAL WORLD
DATA

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Australian mobile phone switching study with amaysim (n=1600). The Likelihood of
switching when you are very dissatisfied with...

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Netlogo results compared to an Australian mobile phone switching
study with amaysim (n=1600): Satisfaction
Netlogo standardised
Beta
Amaysim study
standardised Beta
Coverage .08 .25
Monthly cost -.32 .23
Customer service .30 .34
Overall contract terms n/a .21
Dependent variable “Happy” R2=.25 R2=.39 Satisfaction with
current supplier

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Netlogo results compared to an Australian mobile phone switching
study with amaysim (n=1600): Switching: follow up study
Netlogo std
Beta 3G
Netlogo std
Beta 4G
Loglinear std Beta
of switching:
amaysim study
Coverage -.06 -.58 .26
Monthly cost -.19 .15 .10
Contract terms n/a n/a .68
Customer service .47 -.14 .57
Dependent variable 3G use 4G use Switched to another
provider
R-square and Pseudo R-square .64 .64 .13

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Netlogo results compared to an Australian mobile phone switching study with
amaysim (n=1600): Switching: follow up study
Netlogo standardised
Beta
Log-linear
standardised beta of
switching: amaysim
study
Coverage .43 .26
Monthly cost .01 .10
Contract terms n/a .68
Customer service -26 .57
Dependent variable Churn = Loss of turtles Switched to another
provider
R-square and Pseudo R-square .06 .13

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References
Chang Hyun, J. and J. Villegas (2008). “Mobile phone user’s behaviors: The motivation factors of the
mobile phone user” International Journal of Mobile Marketing 3(2): 4-14.
Colwell, S. R., M. Aung, et al. (2008). "Toward a measure of service convenience: multiple-item scale
development and empirical test." Journal of Services Marketing 22(2/3): 160-169
Crockett, R. O. (2008). “Motorola sets its phone unit free” Business Week (4078): 36-38.
Deng, Z., Lu, Yaobin, Wei, Kwok Kee and Zhang, Jinlong (2010). "Understanding customer satisfaction
and loyalty: An empirical study of mobile instant messages in China." International Journal of
Information Management 30(4): 289-300.
Dierkes, T., Bichler, Martin and Krishnan, Ramayya (2011). "Estimating the effect of word of mouth on
churn and cross-buying in the mobile phone market with Markov logic networks." Decision Support
Systems 51(3): 361-371.
Gilbert, N., Jager, W., Deffuant, G., & Adjali, I. (2007). Complexities in markets: Introduction to the special issue.
Journal of Business Research, 60(8), 813-815.
Gilbert, N., & Troitzsch, K. (2005). Simulation for the social scientist: Open university press.
Goode, M. M. H., Davies, Fiona, Moutinho, Luiz and Jamal, Ahmad (2005). "Determining Customer
Satisfaction From Mobile Phones: A Neural Network Approach." Journal of Marketing Management
21(7/8): 755-778.
Iyengar, R., Jedidi, Kamel and Kohli, Rajeev (2008). "A Conjoint Approach to Multipart Pricing." Journal
of Marketing Research 45(2): 195-210.
Jack, L. (2008). "Public gets taste for cut price communication." Marketing Week (01419285) 31(33): 3-
3.
Pepinsky, T. B. (2005). From Agents to Outcomes: Simulation in International Relations. European
Journal of International Relations, 11(3), 367-394.
Mah, A. (2004). "Product Innovation Case Study: '3' - A Hutchinson Brand." Marketing Review 4(2): 157-
188.
Poynter, K. (2006). "Vodafone: 'Stop the clock'." Marketing (00253650): 22-22.

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Questions?