=Paper=
{{Paper
|id=Vol-2845/Paper_22.pdf
|storemode=property
|title=Agent Modeling of Online Store Activities
|pdfUrl=https://ceur-ws.org/Vol-2845/Paper_22.pdf
|volume=Vol-2845
|authors=Anna Selivanova,Oleg Pursky,Yurii Yurchenko,Hanna Samoylenko,Tatiana Dubovyk
|dblpUrl=https://dblp.org/rec/conf/iti2/SelivanovaPYSD20
}}
==Agent Modeling of Online Store Activities==
https://ceur-ws.org/Vol-2845/Paper_22.pdf
Agent Modeling of Online Store Activities
Anna Selivanova, Oleg Pursky, Yurii Yurchenko, Hanna Samoylenko, and Tatiana Dubovyk
Kyiv National University of Trade and Economics, Kioto str. 19, Kyiv, 02156, Ukraine
Abstract
Agent modeling technologies allow solving the problem of online trading, giving a clear
vision of the possible consequences of making management decisions. Analysis of
publications and resources has shown that a necessary condition for the development and
improvement of the online store is the use of effective data and content management
technologies. This area includes such areas as large data management, regional data
management, multimedia data management, as well as participation in web mining, data
analysis on the Internet, web content modeling, etc. The study of the functioning of the online
store gives the ability to choose the optimal market strategy with minimal advertising costs.
An agent model has been implemented to study the functioning of structural elements and
reflect the processes that occur during the purchase of goods. As a result of the
implementation of the model, the level of growth of potential buyers was determined. Agent
modeling technologies allows tracking the conditions under which each purchase will be
made.
Keywords 1
E-trade, agent modeling, AnyLogic, online store operations
1. Introduction
The current development of the economy is characterized by a rapid growth of the pace of
informatization of economic processes, the expansion of the scale of e-business and, in particular, of
retail e-trade on a large scale [1, 2]. Rapid development of e-commerce and online marketing are
becoming an attractive alternative to media campaigns because they can be cost effective with a
relatively small budget and specifically targeted to user profiles. In recent years [3, 4], electronic
trading has grown rapidly, spreading comprehensively and offering an increasingly diverse assortment
of goods and services, the e-commerce becomes an instrument for the integration of individuals,
enterprises, industries, state institutions and states into a united community, within which the
interaction of partners is effectively and unhindered by means of information and telecommunication
technologies.
In current environment, e-commerce has significant benefits. Using of Internet communication
channels significantly reduces the cost of organization and technical support of the infrastructure, also
e-commerce capabilities allow to quickly update business strategies at any moment [5, 6]. The basis
of e-commerce is the new information technology for commercial operations and the management of
production processes with the use of electronic analysis of large data. Currently, the organizations
thanks to the use of e-commerce go to new markets, receive the necessary information about the
needs of consumers, react quickly enough to different demand changes, reduce both financial and
temporary resources, and increase competitiveness [7, 8].
Therefore, it is important to know buyer and effectively research, analyze data to effectively
company management based on data about it, its purchasing power and cost level. The functioning of
online trading enterprises in the conditions of a market economy necessitates adapted management,
IT&I-2020 Information Technology and Interactions, December 02–03, 2020, KNU Taras Shevchenko, Kyiv, Ukraine
EMAIL: ann.selivanova1@gmail.com (Anna Selivanova); o.pursky@knute.edu.ua (Oleg Pursky); yura253245@gmail.com (Yurii
Yurchenko); h.samoylenko@knute.edu.ua (Hanna Samoylenko); tatiana_dubovik@i.ua (Tatiana Dubovyk)
ORCID: 0000-0001-6559-1508 (Anna Selivanova); 0000-0002-1230-0305 (Oleg Pursky); 0000-0002-8047-7647 (Yurii Yurchenko); 0000-
0002-4692-6218 (Hanna Samoylenko); 0000-0001-9223-4629 (Tatiana Dubovyk)
© 2020 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
227
planning of a product range that meets the needs of the population and at the same time contributes to
the profitability of producers [5, 9].
The problem of modeling of e-commerce processes [9-11] is currently one of the most urgent due
to the dynamic growth of this economic activity area, both in terms of trading operations and
territorial coverage of potential participants in e-commerce. Given these circumstances, there is a
process of analysis of the problem of e-commerce and the use of economic and mathematical tools
can be used to study the complex patterns of interaction in e-commerce and serve as framework the
development of various management mechanisms.
Modern information technologies of computer simulation make it possible to increase the
effectiveness of traditional methods [12, 13]. Agent modeling technologies allow optimizing the
current state of the enterprise, and reducing advertising costs. There are two main areas in online
advertising [5, 14]:
- branding;
- performance marketing.
The first direction based on attracting customers through the formation of brand knowledge. This
approach characterized by creating a link between a particular brand and a group of products, sellers,
increasing the number of potential consumers and ensuring brand promotion. The methods of this
strategy include tools of Internet marketing: contextual and media advertising, promotions,
advertising on social networks. The second approach to Internet marketing based on the needs of
small businesses. Performance marketing is aimed at attracting a specific target audience, it is
characterized by the use of different methods of online advertising. Such methods include calls,
applications, consultations, sales, visits of a specialist or consultant to the client. The direction of
performance marketing requires advertising to achieve a specific goal by actively engaging the target
audience and constant feedback.
The aim of the study is to construct a model, describes in detail the work of an online store. The
main task is the construction of a real object model and its functioning dynamics. Due to the minimal
distortion of the structure of the object, the result obtained by simulation is close as it possible to the
real ones. The model, compiled correctly, is almost identical to the mapping of a real object with a set
of appropriate parameters. Modeling the functioning of the online store should provide the
opportunity to choose the optimal market strategy to achieve by a certain point in time, the required
number of consumers of the product with minimal costs for their implementation. For this purpose we
used AnyLogic computing environment [15].
2. Literature review
A number of studies (e.g., Zhong et al. [16], Llacay et al. [17], Pursky et al. [18], Ravand et al.
[19], Oh et al. [20], Dezsi et al. [21]) have researched modeling of business processes in the field of e-
commerce.
For example, Zhong et al. [16] in their research found that that steady collaboration between online
traders is required quality development of electronic markets. In this study, Zhong et al.
explores the processes that underlie the relationship between reputation and the level of cooperation in
the light of agent-based modeling.
Author’s attempts to establish reputation system based on trust, and analyzes its impact on the sust
ainability of mutual cooperation between online sellers, emphasizing such factors as the level of trust
of Internet traders and the importance of influencing their past behavior. As noted by the authors [16]
the simulation result revealed a relationship between the smoothing constant and the probability of
simulation. Average income trader decreases with increasing smoothing constant.
The identified finding shows the main direction to implementation of a trust-based reputation
system to develop the sustained cooperation among online traders. At the same time, it should be
noted that further research is needed to test the proposed a trust based reputation system in terms of its
ability to impact the desired outcomes like sustainability of mutual cooperation between online
traders which has not been sufficiently studied in this research.
Llacay et al. [17] in their studies note that the use of agent models has increased in recent years to
for studying different social systems, and especially financial markets. According to Llacay et al.,
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agent modeling of financial markets are usually validated by checking the ability of the model to
reproduce a set of empirical stylised facts. At the same time, the authors note that other common-
sense evidence is available which often overlooked, ending with models that are valid but do not
make sense.
This paper [17] considers the issue of building an agent model of the stock market, which
implements realistic trading strategies based on practical results. Authors test the model in four
stages: assessment of face validity, sensitivity analysis, calibration and validation of model outputs.
The studies performed by Pursky et al. [18] present the model for construction of e-trade
management system architecture which consists of two basic blocks: the landing Web-pages
management unit of the online store is responsible for interacting with the user, receiving orders and
collecting primary information and Web-enterprise management unit, responsible for processing
orders, their delivery, and all enterprise's business processes realization. Authors focused their
attention to developing an architecture model for e-trade management system. A service-oriented
architecture (SOA) using enterprise service bus (ESB) concept is offered for architectural
implementation. In developed e-trade management system architecture was used the method of
integrating complex information systems within the information environment based on SOA and ESB
technologies, which represents an independent, integrative subsystem for relatively integrated
information systems. Typical information systems that belong to the e-trade system are used as
services. These web-based services should be developed and used to enhance ESB efficiency. Such
web-services implement SOAP and use WSDL (Web Services Description Language), as well as
UDDI specification (Universal Description, Discovery and Integration). At that, as author’s note [18],
each typical information system is simultaneously data supplier and data consumer. Each information
system handles its own data class, while being able to both transmit and receive, adjust and modify
data according to operational information from other information systems within main business
processes [18].
Other authors Ravand et al. [19] focus on assessing the impact of new technical means on which
all modern systems are based, which play a key role in the development of practical systems for
companies, especially commercial and trading companies. This paper discusses the issues of the trade
process and new tools and instruments for online trading and the impact of technical implementation
on the process. First, the authors rethink the basic process for a trading company and then, based on
the updated process, propose a new electronic system, taking into account the rules and conditions for
the implementation of e-commerce. Then the authors tried to implement a practical block from the
point of view of the supplier, using the interaction approach. The next step is the introduction of new
electronic tools for redesign, renewal of business relations and implementation of the trade process. In
this study, for evaluation of designed model authors [19] tested SIFCO as a trading company for steel
materials and analyzed the data as a result of the implementation of the system and studied its
performance on the criteria of time and accuracy.
The article named as “A modified innovation model for e-trading systems in South Korean
businesses” [20] devoted to explore the adoption, use, and performance of e-trade systems in South
Korean businesses. The authors have developed an extended resource-based model to address a
performance aspect that is still irrelevant in previous studies. Oh et al. [20], using the new model, 114
businesses showed the relationship between perceived usefulness, apparent ease of use, use of
electronic systems, and business efficiency, and analyzed hidden averages to study the moderation
effects of high and low efficiency e-commerce systems.
They noted that the effect of changing variables depending on the use of electronic trading
systems. As the groups with high and low levels of use, usability impact on reuse and further use also
affect performance.
The authors [20] only examined Korean enterprises, so it is advisable to study foreign counterparts
in international e-commerce. Authors emphasize that the results obtained are sound for managers and
politicians in developing e-commerce system strategies and the context of e-commerce None of the
previous studies of advantages, namely convenience and benefit not investigated the relationship
between the use and performance of business-oriented e-commerce system Notably, according to the
authors [20], further research could explore how to change the dynamics of the sector in overtime
mode, and explore opportunities for qualitative approaches to specific research to better understand
the perspectives of firms regarding the formation of management strategies and decision making.
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Dezsi et al. [21] in their studies indicate that high frequency computer-based trading (HFT)
represents a challenging topic nowadays, mainly due to the controversy it creates among investors on
the financial market. In this paper [21], authors compares two types of agent models, one with zero-
intelligence traders and the other with intelligent traders in order to simulate the tick-by-tick high
frequency trades on the stock market for the selected U.S. stocks. Realisations of the agent-based
models are done with the help of Adaptive Modeler software application which uses the interaction of
2,000 heterogeneous agents to create a virtual stock market for the selected stock with the scope of
forecasting the price.
Within the intelligent agent-based model the population of agents is continuously adapting and
evolving by using genetic programming in forming new agents by using the trading strategies of the
best performing agents and replacing the worst performing agents in a process called breeding, while
the zero-intelligence agent based model does not evolve, agents do not breed, and they trade in a
random manner. And they, comparing the compliance of the two models with real data, claim that the
results show in almost all cases that the intelligent agent model works much better than the model
based on the agent with zero intelligence, which can be regarded as lower market efficiency, which
allows to predict stock market price or even manipulate the stock market. Also, the zero-intelligent
agent-based model generates more trades and lower wealth for the population, compared to the
intelligent agent-based model.
Based on their research, the authors [21] formulated the next conclusion: the high-frequency data
turns out to be very hard to simulate and analyses due to its particularities which differentiate them
from daily data, as price changes are discrete, being multiples of the minimum price increment, the
price changes not being independent.
The review of literature indicates the most of the studies are quite exploratory in nature where
reviewed existing approaches and proposed directions for future research. Due to the above-said
reasons, this research motivated us towards developing an agent model to study the functioning and
reflect the business processes that occur during the purchase of goods in online store. Unlike the
others, the stage-by-stage technology of building an agent model that presented in this article takes
into account features of e-trade business processes and can be considered as typical for creation e-
trade management system of most online store.
3. Results and discussion
For fast tracking and responding to changes in the market, and to image dynamics increase the
number of customers, it is advisable to use agent modeling technologies. The first step in building a
model will be to determine the criteria and conditions under which the experiment will begin. A
relatively small market with 5,000 people will be considered. To implement the model, each client
will be an agent. Since it is determined that the conditional company is new, no one will be interested
in the product at first, people's interest will appear under the influence of advertising. After that, the
number of successful sales will be affected by the natural increase in customers, which will occur due
to the fact that customers who have already bought the product will share information with their
friends. The last to the model will be added indicators that can negatively affect the operation of the
system, as they will change the conditions under which each purchase will be made.
The block scheme of the online store is very simple, step by step it can be described as follows
(Figure 1):
- buyer goes to the website of the online store, looks for the product and sends it to the cart when
placing an order;
- client indicates his contact details and method of payment;
- manager contacts the client to confirm the order and clarify contact information, delivery point;
- customer pays for the order at this stage, or does so upon arrival of the goods; the manager of the
online store, or another employee, packs the goods and sends it by courier service (if the customer did
not prefer self-pickup);
- customer receives the order and makes the payment if he has not done so before
- after-sales interaction with the buyer (cross-sell, up-sell, advertising and email-marketing).
Potential buyer visits the online store, registers, places the selected order and:
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1) the goods are delivered to the warehouse
2) collection of the order in a warehouse
3) change the status of the order in the database to the current state at the time
4) registration of documents to the order (check) and notification to the client about readiness
The process of building an agent model consists of successive steps.
Step 0. Analysis of the initial data. Identify key model variables and create a flowchart of the
model. When creating a flowchart, it is taken into account which variables should be represented by
drives, which by flows, and which by dynamic variables.
Step 1. Create a new model. The model is organized hierarchically and displayed as a tree: the
model itself forms the upper level of the tree; experiments, agent types, and Java classes form the next
level; elements that are part of the agents, embedded in the corresponding branch of the type of agent,
etc.
Step 2. Creating drives.
Figure 1: Block diagram of the operation of an online store
Step 3. Add a product sales stream. After creation in model the two drives modeling numbers of
potential consumers, and consumers of a product it is necessary to put streams. There will be only one
flow in the model - the product sales flow, which increases the number of consumers of the product
and reduces the number of potential consumers. In AnyLogic [15], the flow is specified by the
variable “flow”.
Step 4. Adding constants. The frequency with which potential consumers communicate with
consumers will be a constant in the model. Let each person make an order with an average of 200
people a year.
Step 5. Setting the initial values of drives. After establishing the connection, you can set the initial
value of the drive. The initial value of the “Buyers” drive, which simulates the product for the
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consumer, does not need to be set, because initially the number of consumers is zero, and the default
drive and so is initialized to zero.
Step 6. Creating dynamic variables. In the case of compiling the expression of the initial value of
the drive, any variable is involved in the formula of a dynamic variable or flow, there must be a
relationship between these variables (Figure 2).
Figure 2: Specify relationships between dynamic variables
The number of people who own the product and can persuade others to buy it, in this model at any
given time will be determined by the value of the drive Buyers, and each consumer will communicate
per unit time with the flow of people, the number of contacts per unit time of all consumers will be
equal to Buyers * Stream.
The model must take into account that the manager's conversation with a potential buyer may end
inefficiently. Therefore, it is advisable to add another factor - Order Acceptance, which determines
the strength of persuasion of product owners, and determines the proportion of contacts that leads to
product sales.
It is also necessary to take into account in the formula the probability that those with whom the
consumer communicated are not yet interested in the product. This probability is set as follows:
Potential buyers / Population.
So, the formula will look like this: Buyers * Stream * Order Acceptance * Potential Buyers /
Population
Step 7. Configure model startup.
Before running the model it is needed must select the mode of its execution. The AnyLogic model
can be executed either in virtual mode or in real time. In virtual time mode, model made without
reference to physical time - it is simply executed as fast as possible (Figure 3). This mode is best
suited when it’s necessary to simulate the system for a fairly long period of time. In real time, the
relationship of model time with physical, the number of units of model time performed in one second.
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Figure 3: Model startup settings
Step 8. Launch the model. After starting the model, its current value will be displayed next to each
element (Figure 4).
Figure 4: Execution of the model
Step 9. Add charts. AnyLogic supports various tools [15] for collecting, displaying and analyzing
data during model execution (Figure 5).
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Figure 5: Dynamics of changes in the number of consumers
In this model, the number of potential consumers is not reduced to zero, but is constantly
replenished as consumers re-buy products instead of unusable ones. The intensity of product
acquisition increases, decreases, and eventually takes on a value depending on the average suitability
of the product and the parameters that determine the intensity of this flow. The presence of a product
in the withdrawal model means that some part of the population will always remain potential
consumers (Figure 6, 7).
Figure 6: Intensity of the flow of buyers
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Figure 7: Process of functioning of the online store
The most effective option for an online store is one in which the time to collect the order is minimal.
This version of the process reduces the time to search for goods in the warehouse and increases the
quality of service. In most cases, such goods do not require mandatory personal inspection by the
buyer before the latter decides to purchase. Therefore, the number of customers only increases over
time, and hence increases the burden on employees.
4. Conclusion
Agent technologies in e-commerce are quite appropriate today. Simulation modeling technologies
expedient to use in research to increase the effectiveness of traditional methods. Agent modeling
concept allows to assess and optimize the current state of the enterprise to reduce advertising costs
using the created model. According to the obtained simulation results, the most effective option for
the online store is one in which the time to collect the order is minimal. This version of the process
reduces the time to search for goods in the warehouse and increases the quality of service. In most
cases, such goods do not require mandatory personal inspection by the buyer before the latter decides
to purchase. Therefore, the number of customers only increases over time, and hence increases the
burden on employees. Thus, agent modeling technology makes it possible to conduct preliminary
modeling of any enterprise in e-commerce to ensure the possibility of choosing the optimal market
strategy and the required number of consumers of the product with minimal costs for their
implementation.
5. Acknowledgements
This study was supported by the Ukrainian Ministry of Education and Science, Project No.
0117U000507, “Modeling the mechanisms of international e-commerce operation”.
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