Systems Thinking and Cognitive Process in Marketing

Systems Thinking and Cognitive Process in Marketing

DOI: 10.4018/978-1-4666-0969-3.ch007
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Abstract

Cognitive process in marketing builds behavior of consumers and other players involved in carrying out various marketing functions. The cognitive maps in a firm help developing customer portfolios for evaluating and optimizing profit. This chapter discusses that in developing marketing strategies in a competitive business arena, such cognitive frameworks are often driven by the infinite choices ranging from what products or services to sell, how to determine market segment, and how to target consumers towards managing the competitive markets. It has been observed that as income is one of the critical variables, the cost factor also plays significant role in determining the market size and its scale of economy. The discussion in the chapter accentuates that cognitive analysis and behavior integration in business firms drive the marketing efficiency and profit as sustainable value drivers.
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Introduction

…recent neuroscientific research shows that the health of your brain isn't, as experts once thought, just the product of childhood experiences and genetics; it reflects your adult choices and experiences as well. The brain's alertness is the result of cognitive fitness-a state of optimized ability to reason, remember, learn, plan, and adapt. Certain attitudes, lifestyle choices, and exercises enhance cognitive fitness…

Gilkey and Kilts (2007)

Most people make buying decision as a singular event that occurs at a particular point of time. In reality, buying decision making is a process governed by the need, perceived value, affordability, and attributes of the products. Some consumers rely on self- reference criteria for buying decision making while most often consumers use an advocacy process, possibly the least beneficial way to get things done. They view decision making as a challenge, arguing passionately for their preferred solutions, analyzing information selectively and withholding relevant conflicting data so they can make a convincing decision. Purchase decisions are made through an assessment of a myriad of factors, balancing perceptions of value components against price in a subtle, complex, and often subconscious decision matrix. Customer-centric pricing requires the simultaneous and continuous assessment of product attributes, customer perceptions, and the circumstances of time and place by listening to customers' actions. It is a means of assuring that companies assess the value they create for customers and extract that value from the marketplace (Cross and Dixit, 2005).

It has been argued that developments in information technology affect the performance of marketing decision-makers through different routes. Advances in information technology enhance the possibilities to collect data and to generate information for supporting marketing decision-making. Potentially, this will have a positive impact on decision-making performance. Managerial expertise will favor the transformation of data into market insights. However, as the cognitive capabilities of marketing managers are limited, increasing amounts of data may also increase the complexity of the decision making context. In turn, increased complexity enhances the probability of biased decision processes (e.g., the inappropriate use of heuristics) thereby negatively affecting decision-making performance. Marketing management support systems, also being the result of advances in information technology, are tools that can help marketers to benefit from the data explosion. These systems are able to increase the value of data and, at the same time, make decision-makers less vulnerable to biased decision processes (Bruggen et al, 2000). The analysis leads to the expectation that the combination of marketing data, managerial judgment, and marketing management support systems will be a powerful factor for improving marketing management.

The links between customer satisfaction, repurchase intentions, purchase behavior, and customer profitability with empirical data on attitudes, behavior, and profitability at the customer level of analysis have been explored through one of the empirical studies (Magnus and Vilgon, 1999). Purchase behavior and profitability data, derived from the accounting system of a firm, are matched with the responses of the firm's customers to survey questions distributed prior to the behavior and profitability outcomes. The analysis reveals a strong link between customer behavior and customer profitability, while modest links exist between repurchase intentions and subsequent behavior. Only a weak and non-significant direct link can be observed between customer satisfaction and customer profitability. This study then questions customer satisfaction's commonly assumed role as a proxy for profitability. The customer satisfaction is largely value driven and it has been observed that the values generate customer loyalty over the period. However not all loyal customers are profitable, and not all profitable customers are loyal. Traditional tools for segmenting customers do a poor job of identifying that latter group, causing companies to chase expensively after initially profitable customers who hold little promise of future profits. Reinartz and Kumar (2002) suggest an alternative approach, based on well-established “event-history modeling” techniques, that more accurately predicts future buying probabilities. The consumer decision making process is complex and asymmetric that is influenced by various intrinsic and extrinsic factors of the Brand Company and market.

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