Working Paper
36 Institute of Interdisciplinary Research
Working Papers in Interdisciplinary Economics and Business Research
MAREA Trading Simulations Experiments with the Focus on Marketing Campaign Roman Šperka, Michal Halaška
January 2017
Working Papers in Interdisciplinary Economics and Business Research Silesian University in Opava School of Business Administration in Karviná Institute of Interdisciplinary Research Univerzitní nám. 1934/3 733 40 Karviná Czech Republic http://www.iivopf.cz/ email:
[email protected] +420 596 398 237
Citation Šperka, R. and M. Halaška, 2017. MAREA Trading Simulations Experiments with the Focus on Marketing Campaign. Working Paper in Interdisciplinary Economics and Business Research no. 36. Silesian University in Opava, School of Business Administration in Karviná.
Abstract Roman Šperka, Michal Halaška: MAREA Trading Simulations Experiments with the Focus on Marketing Campaign The aim of this paper is to present the use of innovation of decision function in the implementation of a multi-agent simulation model of a trading company with focus on marketing campaigns. We modeled a real trading company dealing with retailing of cables The subject of the presented research are simulation experiments in MAREA software framework to evaluate the effectiveness of marketing campaign on a company’s Key Performance Indicators (KPIs) during 365 days. We implemented three different scenarios and compared the trading results. In the first scenario company does not campaign at all, in the second scenario company uses one marketing campaign during simulation time, and in the third scenario company uses marketing campaign regularly throughout whole simulation time. We assumed that marketing communication affects customers’ preferences in our model, but cannot force them to buy goods they are not willing to buy. We incorporated price sensitivity and direct effects of marketing communication on customers, though implemented model is well suited for advertising, but can hardly simulate marketing forms as e.g., sponsorship or public relation. Further we assumed only positive effects of marketing campaigns. Based on the assumptions we determined and evaluated several hypotheses. Firstly, we present a multiagent system details and a formal description of a decision function which is used to establish the preferences of customers. Secondly, we present MAREA software framework and lastly we discuss the simulation results. The results obtained show that while one marketing campaign may have momentarily effects on company’s KPIs, it does not have effect on the final results of the simulation. On the other hand, the regular campaigning has statistically significant impact on company’s KPIs. Key words multi-agent system, framework, model, simulation, software, business process, trading, MAREA JEL: F1, F31 Contacts Roman Šperka, Department of Business economics and management, School of Business Administration, Silesian University, Univerzitní nám. 1934/3, 733 40 Karviná, Czechia, e-mail:
[email protected]. Michal Halaška, Department of Business economics and management, School of Business Administration, Silesian University, Univerzitní nám. 1934/3, 733 40 Karviná, Czechia, e-mail:
[email protected]. Acknowledgement The work was supported by SGS/19/2016 project called “Advanced mining methods and simulation techniques in business process domain”.
Introduction The importance of business information systems has been rapidly growing recently because of the globalization. The managements of business companies have to increase the flexibility and the decision speed to keep pace with the situation on targeting markets. The complexity of business operations often does not allow to take measures without known impacts of such decisions. This is where the modeling and simulations find their place (e.g., Suchánek and Vymětal, 2011). While analytical modeling approaches are based mostly on mathematical theories (Gries et al, 2016; Liu and Trivedi, 2006) the approach used in this paper is based on simulations. The simulations, we experiment with, can be described as agent-based (Macal and North, 2005) in the field of social sciences (primarily in business economics and marketing). In our opinion only several problems can be identified while using classical simulation approaches (e.g., Scheer and Nuttgens, 2000). There is a lot of other influences that cannot be captured by using typical business process models (e.g., the effects of collaboration of business process participants or their communication, experience level, cultural or social factors, etc.) as shown in, e.g., (Sierhuis, 2001; Rand and Rust, 2011). Meaning that while empirical and statistical modelling is appropriate approach for modelling extant data sets and making predictions, it just rarely contains theory of consumer behaviour. At this situation we require the rules of behaviour to be written for a whole population of consumers rather than for a particular individual. Agent based simulations offer a tool that helps to understand how micro level processes affect macro level outcome (Siebers et al, 2007). Intelligent software agents representing business process participants are more accordant with people and can model a typical human behaviour like communication, coordination or cooperation, what matches with the basic characteristics of a multi-agent system (MAS). Software agents can also be specialized (e.g., adaptability in a new environment or in life experience). They are able to plan the tasks and to assign the work to other agents. Intelligence of a MAS is created emergently during the interaction both among the agents themselves, with their environment, and its components. The presented research is based on the decision function in a control loop model (Barnett, 2003; Vymětal and Šperka, 2013) of a generic business company. The control loop consists of controlled units like sales, purchase, production and others, managed by a regulator unit (the management of the company). The outputs of the controlled units are measured by the measuring unit and compared with the desired key performance indicators (KPIs). The differences found are sent to the regulator unit, which takes the necessary measures in order to keep the system in the closeness to the KPIs values. However, it was shown that a business company must be looked upon as a system with social functions and responsibilities, where individuals besides the company KPIs also follow their personal aims and preferences (e.g., the paper from (Sharma, Sharma and Devi, 2009), summarizing the Corporate Social Responsibility research of many other authors). The same can be observed in the market, where the customers and the suppliers follow their own targets. The principles described could be used to improve decision making processes of the company’s management. Previous research results of our approach to this challenge using software agents were presented in (Vymětal and Šperka, 2013), (Vymětal and Šperka, 2014; Vymětal, Spišák and Šperka, 2012; Spišák and Šperka, 2011; Šperka and Spišák, 2013). Business process simulation framework called MAREA was implemented and described recently in (Šperka and Vymětal, 2013). We had implemented fundamental model of marketing campaign (Lilien, Kotler and 1
Moorthy, 2003) in this particular research. We used innovation of decision function, which is focused on preferences of customers instead of price of the traded goods (e.g., Vymětal, D., Spišák, M., and Šperka, 2012; Vymětal and Ježek, 2014). We have 3 different scenarios for our marketing campaign and our intention is to compare their results. Modeled company does not use marketing campaign in the first scenario. Company carries out one marketing campaign during simulation time in the second scenario, and company does marketing campaigning regularly during whole year once a month in the third scenario. Innovation of decision function is therefore more suitable as a decision function and provides better results in our opinion. We assume that marketing communication affect customers preferences in our model, but cannot force them to buy goods they are not willing to buy. We work with short term and direct effects of marketing campaign on customers, though our model is well suited for advertising, but can hardly simulate sponsorship or public relation. Further we assume only positive effects of marketing campaigns. We determined five hypotheses based on these assumptions, which will be evaluated simultaneously with simulation results:
H1: One marketing campaign will not affect cash level results with respect to the first scenario where is no marketing campaign. H2: One marketing campaign will not affect profit results according to the first scenario where is no marketing campaign. H3: Regular marketing campaigning will affect cash level results with respect to the second scenario where is one marketing campaign during simulation. H4: Regular marketing campaign will affect profit results with respect to the first scenario where is one marketing campaign during simulation. H5: Higher value of probability of creating sales request will not affect the number of purchases by customers.
The structure of paper is as follows. First part we presents multi-agent system details with focus on basic principles, workflow, innovation of a decision function and MAREA software framework. Second part is primarily focused on parameterization of a simulation model and simulation results. Last part discusses the results, implications of several marketing campaigns and evaluated the hypotheses.
1. Multi-agent system details 1.1.
Principles and workflow
Simulation framework was implemented and used to trigger simulation experiments and ensure the outputs of trading process simulations. The framework covers trading process supporting the selling of goods by company sales representatives to the customers – sellerto-customer negotiation on one hand and purchasing goods from suppliers on the other hand (Fig. 1). It consists of the following types of agents: sales representative agents (representing sellers, seller agents), purchase representative agents, customer agents, supplier agents, an informative agent (provides information about the company market share, and company volume), and manager agent (manages the seller agents, calculates KPIs). All the agent types are developed according to the multi-agent approach. The interaction between agents is based on the FIPA contract-net protocol [3]. The number of customer agents is significantly higher than the number of sales representative agents in the model because the reality of the market might be basically the 2
same. In the lack of real information about the business company, there is a possibility to randomly generate different parameters (e.g. company market share for the product, market volume for the product in local currency, or a quality parameter of the seller). The influence of randomly generated parameters on the simulation outputs while using different types of distributions was previously described in (Vymětal, Spišák and Šperka, 2012). The number of customer agents is significantly higher than the number of sales representative agents in the model because the reality of the market is the same. The behaviour of agents is influenced by two randomly generated parameters using the normal distribution (amount of requested goods and a sellers’ ability to sell the goods). In the text to follow, the seller-to-customer negotiation workflow is described and the mathematical definition of both decision functions is proposed. Decision function is used during the contracting phase of agents’ interaction. Depending on value of decision function, customer decides, if he is willing to accept the sales quote from sales representative or not. One stock item simplification is used in the implementation. Participants of the trading process in our multi-agent system are represented by software agents - the sales representative and customer agents interacting in the course of the quotation, negotiation and contracting. There is an interaction between them. The behaviour of the customer agent is characterized in our case by the innovation of decision function (1).
Target, units, unit price, salesprice limits
Disturban ces
Manager controlling unit
Product, purchase price limit
Controlled Product, campain parameters Controlled subsystem subsystem sales marketing Customer
Targets, budget
Quotes Orders
Sales rep
Marketing
Controlled subsystem purchase Purchase rep
negotiation Orders, stock
Targets & Budgets DB
Marketing campaigns & actions
Quote request
Supplier Quote
Management info
negotiation Purchase orders
Measuring unit – ERP
Turnover,units, gross profit, number of customers, payments,cash flow, etc.
Difference
Enterprise results
Fig. 1. Generic model of a business company. (Source: Šperka and Vymětal, 2013) 1.2.
Decision functions
Purchases are initiated randomly by customer agent. If a customer agent decides not to buy anything, his turn is over; otherwise he creates a sales request and sends it to his sales representative agent. Requested amount is generated randomly based on normal distribution. The sales representative agent answers with a proposal message. We had used in such a function in our simulations in the past, where the decision function for the m-th seller 3
pertaining to the i-th customer determined the price that i-th customer accepts (adjusted according to (Vymětal, Spišák and Šperka, 2012)). 𝑐𝑛𝑚 =
𝜏𝑛 𝑇𝑛 𝛾𝜌𝑚
(1)
𝑂𝑣𝑛
𝑐𝑛𝑚 - price of n-th product offered by m-th seller, 𝜏𝑛 - market share of the company for n-th product 0 < 𝜏𝑛 < 1, 𝑇𝑛 - market volume for n-th product in local currency, 𝛾 - competition coefficient, lowering the success of the sale 0 < 𝛾 < 1, 𝜌𝑚 - m-th sales representative ability to sell 0,5 ≤ 𝜌𝑚 ≤ 2 𝑂 - number of sales orders for the simulated time, 𝑣𝑛 - average quantity of the n-th product, ordered by i-th customer from m-th seller. For the purpose of our simulation of marketing campaigns we have decided to use an innovation of this decision function. We innovated this function because the new one is more suitable in our opinion. The reason is that the innovation incorporates the preferences of customers, not just purely the price of goods. In the text to follow we will introduce this innovation of a decision function and then we will present appropriate reasoning. Decision function for the i-th customer determines the quantity that i-th customer accepts. In our simulation, if quantity xi < quantity demanded by customer, the customer realizes that according to his preferences and budget, offered quantity is not enough, he reject sales quote and the negotiation starts at this point (Vymětal and Ježek, 2014). m
xim = αi∗ p i
(2)
x
𝑥𝑖 – quantity offered by m-th sales representative to i-th customer, 𝛼𝑖∗ - preference of i-th customer (randomized), 𝑚𝑖 - budget of i-th customer (randomized), 𝑝𝑥 – price of the product x. Vymětal and Ježek (2014) derived their (2) innovation of a decision function based on Marshallian demand function, Cobb-Douglas preferences and an utility function, which is based on a fundamental microeconomic theory. They assume the sold goods to be normal goods. The innovation of a decision function (2) is more suitable in our opinion, because it enables to change customer preferences through marketing campaign, since we were able to make changes only through affecting the price in the previous version. Analogically, according to Vymětal and Ježek (2014) the innovation of a decision function (2) gives better results on real data. Currently, we have implemented one customer type and its behaviour is depended on randomized preferences. Thus, in the future we would like to implement different types of customers with their specific behaviour in our model (Schramm et al., 2010; North et al., 2010; Schwaiger and Stahmer, 2003; Said et al., 2002). The aforementioned parameters for both decision functions represent global simulation parameters set for each simulation experiment. Other global simulation parameters are: limit sales price, number of customers, number of purchase representatives, number of suppliers, number of sales representatives, number of iterations, and mean sales request probability. The more exact parameters can be delivered by the real company, the more realistic simulation results can be obtained. 4
Customer agents are organized in groups and each group is being served by concrete sales representative agent. Their relationship is given; none of them can change the counterpart. Sales representative agent is responsible to the manager agent. Each turn, the manager agent gathers data from all sales representative agents and stores KPIs of the company. The data is the result of a simulation and serves to understand the company behaviour in a time – depending on the agents’ decisions and behaviour. Customer agents need to know some information about the market. This information is given by the informative agent. This agent is also responsible for the simulation turn management and represents outside or controllable phenomena from the agents’ perspective. 1.3.
MAREA framework
MAREA application consists of two main components, the simulation of multi-agent system (MAS) and ERP system (Fig. 2). In simulation designer one can design simulation model and adjust simulation parameters. ERP system stores data, through editor one can also read data, insert data and also save trading company ERP data. It also keeps track of KPIs like cash level, turnover or profit. KPIs are used by a manager agent and agents decisions are based on it. ERP system in MAREA is different from ERP systems used in real companies, because it is based on principals of REA models, that means, it does not use double-entering bookkeeping. But one can work through data and get results like one would get from widely used ERP systems, it is just unpleasant. The benefit of using ERP system based on REA principals is that we are able to perform simulations in few minutes instead of several hours. Since MAREA is simulation framework, this choice seems reasonable. Simulation of negotiation between agents about sales and purchases is one of the key functions of implemented multi-agent system. The messages the agents send to each other during negotiation are recorded in the ERP system. All messages about sales (from the initial request to closing the deal) are part of a sales request entity; likewise all messages about purchase (from the initial request to closing the deal) are part of the purchase request entity. The ERP system has been configured to calculate KPIs by summing up other values. For example, Cash level is calculated as a total of all transactions that change Cash level – payments for purchases, income from sales, payment of bonuses, initial cash, etc. Turnover and Gross profit is calculated as a total of gross profits and turnovers of specific product types. The values of the most important KPIs in all simulation steps can be exported to an Excel file and analysed later by typical Excel tools like a contingency table or by a data analysis like histograms etc. The negotiation steps can also be exported to Excel in order to analyse the agents behaviour.
5
Fig. 2. Description of the simulation monitor. (Source: Šperka and Vymětal, 2013)
2. Simulation results We implemented three scenarios in MAREA and analyzed simulation results for all of them. Our intention was to compare the simulation results of a trading company that (1) does not use marketing communication at all, the results of the same company that (2) did disposable marketing campaign, and lastly the results of the same company that (3) uses tools of marketing communication on regular basis. This means that our company uses marketing communication after 30 iterations at any time in the second scenario (2) where every iteration is equal to one day. The company uses marketing communication regularly every 30 days (that means once a month) in the third scenario (3). Note that we implemented some basic ideas of marketing communication and some specific tools are naturally better suited for our model than the others. This means that our model better simulates advertising or direct marketing, but it can be hardly used for simulation of a company that is using sponsorship or public relations. We want the simulation model to be confronted with some fundamental principles of marketing theory. Later in this section we will also evaluate five hypothesis based on these assumptions. We modeled a trading company that deals with retailing of cables for our simulation experiment. It employs 1 sales representative and 2 purchase representatives. Purchase representatives cooperate with 3 suppliers. Suppliers supply items in cooperation with purchase representatives to the stock. For simplicity we are dealing with just one type of item. We would like to simulate trading with more items with expectations in some synergic effects in trading results in the future. Parameterization of each scenario is listed in Tab. 1. The more exact parameters can be delivered by a real company, the more realistic simulation results can be obtained. Advertising cost ratio is a cost of marketing campaign redistributed on production units. More complex problem was to determine both coefficient of marketing 6
competition and a coefficient of company competition. One needs to be quite familiar with the real company and its environment. Tab. 1 Parameters of scenarios First scenario
Second scenario
Third scenario
Number of customers
100
100
100
Number of iterations
365
365
365
Advertising cost ratio
-
0,3
0,1
Probability of creating sales request
4
4
14
Coefficient of marketing competition
1
1,6
1,6
Coefficient of company competition
1
1
1
Source: own research Implemented simulation model has lot more parameters than the ones listed in Tab. 1. But the rest of the parameters remained unchanged through all three scenarios. Advertising cost ratio parameter is adjustable to the cost of a marketing campaign. Probability of creating sales request means that there will be more negotiation acts between customers and sales representatives. Later on we will show that this parameter does not have effect on the number of actual purchases by customers which is what we would expect. The reason is that the marketing communication can have influence on customers' preferences, but it does not push customers to buy goods, when they do not need it. This aspect is applicable only to some degree. Parameter of coefficient of marketing competition is a parameter that can influence customers’ preferences, in other words their higher willingness to buy. The result of nonprice advertising is that customers are less price sensitive (Kaul and Wittink, 1995; Mela, Gupta and Lehmann, 1997). For now, we assumed only positive effect of marketing campaign on customers, even though we know that marketing campaign may have also negative effects on customers. Coefficient of marketing competition goes back to 1 after two weeks without marketing campaigning in our simulation experiments. As previously stated, our set up is better suited for advertising, but does not appropriately model public relations, or sponsoring, etc. (Kotler and Keller, 2016; Varey, 2002). We would like to add some features to MAREA simulation framework in future like: coalitions, genetic and evolutionary algorithms, symbolic paradigm of artificial intelligence and others. With coalitions we would be able to model interactions between groups of agents, genetic and evolutionary algorithms and symbolic paradigm would help us, among other things, with historic experiences of customers with buying products, and this feature would provide a tool for better simulations of for instance a branding. The implemented model operates also with coefficient of company competition. We used only one company simulation model, but if we would like to add competition, this parameter and few others would allow us to lower chances that customers would buy items from our company. We would also like to add some more parameters in our implementation of marketing campaign (Poh, Yao and Jašic, 1998) in the future. As Siebers et al. (2007) stated, 7
the effectiveness of simulation depends upon right level of abstraction on one hand. The number of free parameters should be kept as low as possible. And too much abstraction and simplification might threaten the fit between reality and the breadth of simulation model on the other hand. For our simulation experiments we determined 5 hypotheses:
H1: One marketing campaign will not affect cash level results with respect to the first scenario where is no marketing campaign. H2: One marketing campaign will not affect profit results according to the first scenario where is no marketing campaign. H3: Regular marketing campaigning will affect cash level results with respect to the second scenario where is one marketing campaign during simulation. H4: Regular marketing campaign will affect profit results with respect to the first scenario where is one marketing campaign during simulation. H5: Higher value of probability of creating sales request will not affect the number of purchases by customers. Tab. 2 Simulation results - KPIs for all scenarios Cash level
Turnover
Gross profit Profit
Active customers
Purchases made by customers
First scenario
30445,50
67625,59
32692,95
25445,50
82
205
Second scenario
30279,83
66841,37
32408,41
25279,83
80
206
Third scenario
39647,92
86371,40
44283,65
34647,92
88
220
Source: own research We tracked initial cash level, cash level, turnover, gross profit and profit in all simulation experiments. Initial cash level is a level of cash at the start of our trading cycle. This level was set on 5500 in all scenarios. Cash level is calculated as a difference between sales orders and payments. Turnover is a summary of what customers pay for goods. Gross profit is a profit before taxes, depreciation and amortization. And lastly, a profit is calculated as a difference between revenues and expenses. Tab. 2 presents company’s results after one year of trading in each scenario. Each value in Tab. 2 is the average of 10 simulation runs.
8
45000,00 40000,00 35000,00 30000,00 25000,00 First scenario
20000,00
Second scenario
15000,00
Third scenario
10000,00 5000,00 0,00
Fig. 3 Development of cash level for each scenario through simulations (Source: own research) Fig. 3 shows the development of cash level for all three scenarios where cash level series are daily averages of 10 simulations for every scenario. As you can see from the graph one marketing campaign may have momentarily effect on cash level, but at the end of the simulation, the results were the same as in first scenario. One can also see that regular campaigning has a quite significant impact on a progression of cash level. We expected both those results as you can see in our hypotheses H1 and H3. Table 3 shows that our hypotheses H1 and H3 were both accepted. ANOVA results confirm that in the case of hypotheses H1 only one campaign does not have impact on resulting value of cash level in our simulations (P-value > 0,05). In a case of hypotheses H3 we assumed impact of regular campaigning and it was correct, because P-value < 0,05. Keep in mind that our initial cash level for every simulation was 5500 and that cash level is changing every day only if there are some sales orders or payments. Tab. 3 ANOVA results for all hypotheses F
P-value
F crit.
H1
0,0164
0,8994
4,4139
H2
0,0164
0,8994
4,4139
H3
28,1451
0,0000
4,4139
H4
28,1451
0,0000
4,4139
H5
1,6281
0,2182
4,4139
Source: own research Tab. 3 analogically shows that hypotheses H2 and H4 for profit are also both accepted, because in case of hypotheses H2 ANOVA confirms, that only one campaign does not have 9
impact on resulting value of cash level in our simulation (P-value > 0,05). We assumed impact of regular campaigning and it was correct because P-value < 0,05 for hypotheses H4. Finally we can also accept hypotheses H5 as Table 3 depicts, but keep in mind that this will apply only to some extent. We would like to change also this in the future.
Conclusion The paper presents an innovation of a decision function simulation model implementation and simulation experiments in MAREA software Framework with the focus on marketing campaigning in real trading company. Multi-agent system was developed to support several simulation experiments dealing with a trading process in a trading company. The setup of the application provides possibilities to edit the company parameters and to run trading simulations. This allows users to analyse trading behaviour back-to-back according to the parameters setup. We investigated the impact of the intensity of marketing communication on company’s KPIs. As we predicted and tested, while one marketing campaign may have momentarily effect on company’s KPIs, it does not have effect on the final results of the simulation. Further, regular campaigning had statistically significant impact on company’s KPIs. Keep in mind that the more exact parameters can be delivered by the real company, the more realistic simulation results can be obtained. We incorporated one company into simulation model. We would like to add more parameters in our implementation of marketing campaign in the future (Poh, Yao and Jašic, 1998). We are aware of some imperfections in our model. In our future work we would like to make our customer agents more sophisticated by adding more types of agents and other features like coalitions, genetic and evolutionary algorithms, and symbolic paradigm of artificial intelligence. Through coalition we will be able to better model relationships between different groups of all agents, not only different groups of customer agents, while with genetic and evolutionary algorithms we will be able to include past behaviour of customer agents in their decision making. Another problem with our model is that it is better suited for directly impactful tools of marketing communication like advertising, but it can hardly be used for sponsorship, public relations, etc. We also do not consider negative impacts of marketing communication in our simulations.
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