Assessment of Long-Term Effects of Marketing Mix Policies: A System Dynamics Approach

Assessment of Long-Term Effects of Marketing Mix Policies: A System Dynamics Approach

Nastaran Hajiheydari, Seyed Behnam Khakbaz
Copyright: © 2015 |Pages: 22
DOI: 10.4018/IJSS.2015070101
(Individual Articles)
No Current Special Offers


Understanding of the long term effects of marketing mix policies on firm's financial outcomes is counted as an essential issue in marketing studies. Managers really need to know the efficiency of their decisions in marketing efforts. A large number of researches have been already conducted so as to disclose these consequences, although all of them have got some limitations. In the present paper, the authors have made use of system dynamics to develop a dynamic marketing system and analyze the effect of different marketing mix policies on firm's financial indicators. For this purpose, they have introduced two dynamic marketing models based on the related literature review. The first one, that is a qualitative model, represents the logic of marketing efforts based on system dynamic rules and the second one is a quantitative model which is based on five important loops of the qualitative model. The quantitative model has been manipulated in order to analyze several marketing mix policies (scenarios). The results indicate that decision making for selecting appropriate marketing mix is a complicated process usually leading to nonlinear and complex results. Considering this point, the authors' study suggests the utilization of dynamic simulation in order to predict and analyze the effects of marketing policies on firm's financial indicators. Finally, the findings clearly show the ability of system dynamics in measuring and anticipating firm's marketing initiatives and adapting the most effective policy for its success.
Article Preview

1. Introduction

Today, success of a large number of firms is measured by financial results, and marketing managers have always some problems in showing the effects of their activities on their firm’s success. Therefore, marketing departments are recognized as the main department responsible for costs and expenditures in these firms. On the other hand, marketing activities have long term effects on a firm’s success. Furthermore, in today’s highly competitive market, there are several cases of innovation, technologies are complex, and markets are growing rapidly. As a result, new marketing systems are emerging and growing. Marketing systems are not in equilibrium but in a dynamic form (Layton, 2011), so, marketing managers need to understand the dynamism which influences the structure of the industry in order to assess their decisions about the market (Pagani & Otto, 2013). Briefly, different marketing activities and policies have different long term effects on a firm’s success. So, marketing managers are not able to choose the best marketing policies.

To date, system dynamics are used so as to make different analyses in field of marketing. These analyses consist of predicting market demand, competitive behavior as well as strategic policy-making. Sheth and Sisodia (2002) have conducted one of the first researches on application of system dynamics in marketing. They suggested system dynamics and system thinking as two criteria for measuring customer equity, a parameter they believe to be a marketing productivity issue. They used system dynamics for four different systems: (a) market system, (b) acquisition and churn system (c) revenue per customer and total revenue system (d) customer NPV system. In their article, they proposed a system dynamics model. All of the systems (subsystems) aforementioned have been integrated with each other as well as with other subsystems and variables. In the system proposed in the present article, the system (a) is considered as a part of market section, the system (b) as a part of customer part, and finally the systems (c) and (d) are a part of financial section. Woodside (2005) used system dynamics for analyzing the causality among customer orientation, competitor orientation, firm orientation innovativeness and business performance. Their model is only a qualitative conceptual mental one consisting of the mentioned factors. In addition, no mathematical model was applied to his research. Fath and Sarvary (2003) conducted a research aimed at studying dynamic economic system in business to business (B2B) market by formulating it using game theory and mathematic concepts. Pagani and Otto (2013) applied system dynamics in order to analyze strategic decision-making in two different firms. They used customer adaptation and its loops as the heart of their model. Using the model proposed, we also apply customer variable as well as customer loops to a model more complete than that suggested by Pagani and Otto. Yawson and Kuzma (2010) carried out an analysis of system dynamics which was applied to agricultural food nanotechnology industry in order to describe consumer behavior and acceptance in food market.

How can we obtain the best performance from marketing efforts? This is a fundamental question for everyone working in this field. Scenario planning through system dynamics may be a good mean to answer this fundamental question. But as we know, the recommendations are neither right nor correct as we are striking a deal with uncertainty. Given the fact just mentioned, there is no best way. Using scenario planning, it is possible to identify the results of strategic decision making and to select one of them, which most fit the organization. Scenario planning is one of the most respectable fields in analysis of system dynamics which has been conducted by some researchers. Using system dynamics, Morgan and Hunt (2002) introduced a combination of determining marketing strategy and making strategic decisions, as a novel analysis. They focused on different marketing strategies in different situations and gained interesting results.

Developing a dynamic model for analyzing customer relationship management (CRM) in different industries is an application of system dynamics in marketing. Chan et al. (2010) developed a three tiers (module) dynamic model for CRM. Their dynamic model consists of a customer purchasing behavior model (module 1), Markov chain model (module 2) and a financial return model (module 3).

Complete Article List

Search this Journal:
Open Access Articles: Forthcoming
Volume 6: 2 Issues (2019)
Volume 5: 2 Issues (2018)
Volume 4: 2 Issues (2017)
Volume 3: 2 Issues (2016)
Volume 2: 2 Issues (2015)
Volume 1: 2 Issues (2014)
View Complete Journal Contents Listing