Every marketer has an implicit model of consumer behavior in his head. This results from his many understandings of customer behavior obtained over time. These understandings are based on his personal consumer experiences, his knowledge of customers garnered from his marketing activities, and the corpus of knowledge available through previous consumer behavior studies. Because consumer behavior is simply a subset of human behavior, consumer behavior models pull from various disciplines that study human behavior, such as psychology, sociology, economics, anthropology, decision sciences, and so on.
Several additional attempts have been made to understand and eventually mimic consumer behavior, fueled by the experience of the Howard-Sheth model and other parallel studies. These efforts can be divided into several groups based on the following primary criteria −
The goal of modelling.
Basic disciplines are supported.
Analytical approaches are supported.
Modelled the basic unit of customer behavior.
The objectives of modelling have mainly been confined to the following −
Models with this goal concentrate on the numerous structures that play essential roles in purchasing and behavior. These structures are illustrated in the schematic designs that depict total consumer behavior. These models are essentially snapshots of customer behavior. As a result, they effectively capture the values that various variables in the model take. However, they need to understand causality. One of the most common applications of such models is to communicate marketers' visualizations of their customers to their audiences. Models created with this goal are also proven to be helpful beginning points for creating more sophisticated and higher-level objective models.
Data sources for reaching these goals include market definitions or consumer profile surveys. Such surveys can give demographic, socioeconomic, psychographic, and purchasing stage data. This data can depict customers in terms of their significant aspects and on an aggregate level. Consequently, one microwave oven marketing business, for example, can define its customers as informed but need clarification on the value of these ovens in their cooking style. The corporation can additionally profile such clients based on their other distinguishing qualities for marketing purposes. By recognizing specific trends in the data gathered from such consumer descriptions, the firm might hypothesize about the potential causes of low sales among the target customers. In order to be more specific or demonstrate causalities, the marketer must approach modeling in new ways.
Rather than the condition of consumers, this purpose focuses on the processes that influence customer behavior. One example is the Nicosia model, designed with this goal. The objectives bring the marketer one step closer to understanding the causation models. They also aid in the connection of various constructs and give recommendations for these links within consumer behavior models. When these links are quantified, the marketer gains more control over designing the input mix to achieve the desired customer moods.
This is the ultimate goal of consumer behavior modeling. It requires data to characterize the states of consumer behavior and their connections. Unsurprisingly, it is the most difficult to attain. For one reason, the information base for reaching this goal depends on the complete knowledge base in marketing and the other disciplines associated with human behavior prediction. These fields' knowledge has yet to progress to the point where they can try this endeavor. In marketing settings, the variables involved are micro, such as a consumer's brand preference, product perception, learned associations about a product category or brand, consumption occasions, etc. The accuracy degrades significantly when forecasting such micro-level occurrences using standard variables.
Current model advancements in consumer behavior may also be recognized by applying the fundamental academic fields upon which such models are primarily based. Economics, which provided the first conceptualization of consumer behavior, thought the customer was a rational economic entity. His actions drew attention to economics at both the micro and macro levels. The discipline's restricted assumptions and the complexity of human elements found in marketing practice decrease the discipline's value in modeling customer behavior.
Psychology, emphasizing the why of human behavior, has also made substantial contributions to consumer behavior understanding and modeling efforts. Certain psychological constructs are used in almost every consumer behavior model. Understanding these domains and their interactions with other conceptions is frequently derived from psychology, the mother discipline.
The fundamental issue with psychological conceptions had been their operationalisability in the marketing context, the weaker connections discovered, and the omission of nonpsychological elements. Sociology has also been employed extensively in analyzing consumer group phenomena (such as market segmentation) and other customer social dynamics (such as diffusion of innovations). Consumer behavior modelers are becoming aware of the importance of specific disciplines.
Customer choice or decision is one of marketers' most crucial topics of interest. While economics and psychology (through cognitive psychology) have grown in this area, the burgeoning subject of Decision Sciences is the most explicitly focused on it. The benefits of this discipline in the field of consumer behavior are numerous. (i) It aids in following the flow of the consumer decision-making process; (ii) it aids in sorting out the relevant qualities or features that contribute to the choice; and (iii) it aids in identifying trade-offs between these key attributes and their levels utilized in the minds of consumers—another benefit of using decision sciences. The availability of decision sciences approaches to consumer behavior is a paradigm in consumer behavior.
The man-to-physical-world-interaction emphasis distinguishes anthropology from the other disciplines. With its focus on consumer-product interactions, marketing should be a strong borrower in this field. Regrettably, in the past, this has not occurred. This is due to several factors. Anthropology itself has been obsessed with far more significant concerns, such as the influence of the wheel on society or the distribution of blood types between populations, among other things.
They frequently set up their laboratory among isolated tribals in remote places to make their investigations more scientific- and to separate extraneous factors from the primary variable of interest. Those things imbued anthropology with a mystical atmosphere. Nonetheless, anthropologists and marketers are becoming more aware of the need to collaborate and benefit from reciprocal connections.
System dynamics and simulation were created primarily to simulate complicated situations. These approaches' instruments have been carefully tuned to manage many variables and their interactions. TConsumer behavior scenarios incredibly nearly meet these conditions. As a result, there is an increasing awareness of and use of system dynamics and simulation in consumer behavior models. These disciplines are used to develop, test, and improve models.
Models of consumer behavior always deal with a plethora of factors. Modern consumer behavior models employ various analytical methodologies to deal with this problem. Most of these strategies may be found in the mathematics, statistics, and operations research literature. Nevertheless, with the ready availability of advanced computational power, these strategies have lately played considerably larger roles.
Hence, stepwise regression analysis and correspondence analysis, for example, are frequently employed to find the critical variables and their correlations in observed data: For reducing data and extracting the essence from a vast quantity of data, factor analysis and multidimensional scaling techniques are applied. Consumers frequently evaluate distinct features and their associated trade-offs. Conjoint analysis has shown to be a valuable technique for such goals. The sequencing and orientations of constructs are critical in consumer behavior models. Graph theory can aid with this task.
Early consumer behavior models focused on individual customer behavior. They focused even more on fast-moving consumer nondurable goods. This was likely easier since such models did not need consideration of interactions between multiple persons purchasing the same item. However, these social units must be considered with the increasing importance of families, institutions, and companies in product purchases. As a result, more and more models with decision-making units bigger than people are emerging. The most prevalent decision-making units among them are industries (for example, Webster's "General Model for Understanding Organizational Purchasing Behavior"), distribution channel members, and families (for example, Sheth's "Family Decision Making Model").
Models with this goal concentrate on the numerous structures that play essential roles in purchasing and behavior. Data sources for reaching these goals include market definitions or consumer profile surveys. By recognizing specific trends in the data gathered from such consumer descriptions, the firm might hypothesize about the potential causes of low sales among the target customers.
It requires data to characterize the states of consumer behavior and their connections. Unsurprisingly, it is the most difficult to attain. For one reason, the information base for reaching this goal depends on the complete knowledge base in marketing and the other disciplines associated with human behavior prediction.
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