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behavior and are reflected in key performance indicators such as conversion rate,
business volume, or clickthrough rate. Due to numerous network effects, the store
manager cannot forecast the consequences of his decisions. This is insucient be-
cause their decisions are often non-transparent and the effects of their decisions
are dicult to predict.
The shop configuration includes many aspects. Hence, we focus in a first step
on the configuration of payment methods that should be offered in an online
store. Today, there are a numerous payment methods. Out of these, a store
manager has to identify the relevant ones that are accepted by the customers
and suited for the business. Assuming the following scenario, a store manager
wants to offer the possibility to pay with credit card. On the one hand, the credit
card payment will attract new customers and thus, the sales volume will rise.
On the other hand, regular customers will switch to the new payment method.
This may have negative effects for other offered payment methods as they are
mostly transaction-based. Hence, a lower number of transactions results in higher
costs per transaction. After all, is it beneficial to integrate the credit card as an
additional payment method?
An approach for the decision support is simulation. The simulation model
represents the fundamental attributes of a system. This model is used to study
the behavior of the system over time in order to draw conclusions about the
behavior of the real system. As a result, different scenarios can be tested with-
out changing the real system [1]. Since the customer acceptance is one of the
critical factors for optimizing the payment methods, we want to develop a sim-
ulation model that forecasts the consumers' payment behavior according to the
specific payment configuration. Typical question that we want to answer are, for
instance: Does the integration of additional payment methods always result in
new consumers? Are there payment methods that have a very high or very low
consumer acceptance? Which is the configuration with the lowest dropout rate?
The paper is structured as follows. In the subsequent section, we describe
our simulation model. We give first an overview of the entire simulation and
present afterwards our agent-based payment simulation in detail. In Section 3
we calibrate and run our simulation in order to validate and evaluate the sim-
ulation results. In Section 4 we compare our simulation with other simulation
approaches. Finally, we conclude in Section 5 with a discussion of our simulation
approach and describe future work.
2
Simulation Model
2.1 Overview of the Shopping Simulation
The simulation of the payment behavior is part of a larger simulation chain.
Before presenting the payment simulation in detail, we give a general overview
of this simulation chain. We can essentially differentiate two parts: the customer
group and the e-commerce system (see Fig. 1). The customer group represents
people participating in online business as consumers. They have specific proper-
ties and a certain behavior. These customers can interact with the e-commerce
