Professor, Faculty of Business and Commerce, Keio University
Akira Shimizu
Consumer behavior theory, marketing theory
I graduated from Keio University's Faculty of Business and Commerce and completed master's and doctoral programs at Keio's graduate school. Ph.D. (Commercial Science). Assumed my current position in 2009 after serving as a full-time lecturer, assistant professor, and professor in the Faculty of Economics at Meiji Gakuin University. He is formulating and empirically verifying marketing strategies based on consumer behavior theory. Author of Japan's First Marketing and Strategic Consumer Behavior Theory (published by Chikura Shobo, in Japanese) and New Consumer Behavior Theories from Japan (Springer, sole author).
Elucidating Marketing Theories by Using Consumer Behavior Theory
Professor, Faculty of Business and Commerce, Keio University Akira Shimizu
My specialty is research that elucidates companies' marketing strategies, utilizing various theories related to consumer behavior and analyzing data on consumer behaviors and attitudes. Companies must determine whether they will accept their products and services to execute effective marketing strategies targeting consumers. The intuitive insights of people in contact with many consumers, the so-called people on the ground, have become important. However, companies cannot move forward if those intuitions cannot be quantified appropriately and translated objectively into proposals. Since these are for-profit companies, a vague "general direction" is insufficient; objective evidence is needed to verify that profits will be generated. Therefore, quantifying consumer attitudes and needs and analyzing the obtained figures is essential for executing marketing strategies.
Research in this field was initially conducted by surveying consumer thinking and attitudes using questionnaires and then analyzing that data. Researchers did this with backgrounds in sociology and psychology. More specifically, the method involves consumers responding to various scales used in psychology and sociology with 5-point answers (strongly agree, agree, neutral, disagree, strongly disagree), and strategies are developed by analyzing those numeric values. In recent years, it has become clear that a person's circumstances and changes greatly influence individual attitudes and needs in social conditions, such as the drive for sustainability. It's true of personal desires, but it has also reached the point where people act with an eye toward society.
Since the 1980s, when POS systems were adopted in the retail industry, researchers with science and mathematics backgrounds specializing primarily in data analysis have emerged at the forefront of research. This is because consumer purchase history data became easy to gather on a large scale, and various mathematical statistics methods became usable due to the rapid development of computers. The underlying rationale is that past purchase history is an objective fact that shows a consumer's buying "habits," making prediction accuracy when forecasting what they will buy or prefer far higher than subjective attitude data obtained from questionnaires. Big data includes consumer purchase histories, clickstream data leading up to online shopping, and data from opinions posted on social media after purchases, all of which are linked together, making objective data about consumers increasingly rich. With the development of AI added to the mix, applying advanced mathematical formulas to analyze consumer data and apply it to marketing strategies is likely to continue for some time.
On the other hand, in a situation where so many factors influence consumer preferences and choices, accurately measuring the effects of actions taken by companies requires controlling many external factors. For this reason, many studies are being conducted using laboratories or larger-scale experimental stores. The orthodox method is to prepare multiple experimental materials and measure differences in their impression through questionnaires in a controlled situation. Still, in recent years, due to the need for more objective data, attempts have been made using consumers' biological responses, such as eye tracking that measures the movement of subjects' eyes and equipment that measures brain waves. With the widespread adoption of wearable devices today, this type of biological response data will likely be vital for developing companies' marketing strategies.
In this way, marketing strategies using consumer theory and relevant data have developed as data has evolved. Still, I want to point out several issues that will likely be problems now and in the future.
One issue is data management and the ethics of analysts. When shopping online, it seems efficient and convenient to have products that match your preferences and are recommended to you. Still, everyone has likely felt that it's frightening how well their preferences are understood. Companies aim to increase consumer shopping convenience and gain favor by making recommendations based on results obtained from big data, questionnaires, and experiments. Still, people are strongly resistant to having their behavior revealed in detail. In Japan, there has been a movement to protect personal information since implementing the Act on the Protection of Personal Information. Still, the My Number (Individual Number) system is moving toward centralized management of various types of personal data. What is being tested amid these two contradictory social changes is the ethical stance of data managers, analysts, and companies. There is a definite need to analyze valuable data to stay ahead of rival researchers. Still, the extent to which ethical considerations should guide the process is a point that a researcher must consider.
The second issue is the accuracy of the questionnaire survey. Big data, including purchase history, is an objective record of consumer behavior. It is beneficial, but the reasons leading up to behavior, while they can be inferred from data, ultimately cannot be understood without surveying consumers. Data becomes more meaningful when these two are combined. However, with the spread of Internet surveys in recent years, consumers who are survey subjects have become accustomed to questionnaires, and cases where they anticipate the questionnaire's intentions and answer accordingly have increased tremendously. Is data genuinely being correctly understood? There is a growing need to identify and use this properly in research.
The third issue is rapid aging in developed countries. Previously, marketing strategies have been carried out targeting young people and consumers with young mindsets who are active shoppers. Young people are more likely to be interested in new products and services with larger profit margins. However, in Japan, where over 25% of the population is elderly, and the declining birthrate and aging population are progressing, it is clear that targeting only young people will lead to a decline. Moreover, with such a significant increase in the elderly population, it's no longer reasonable to categorize them all under a single "elderly" label. We have previously understood consumers and developed marketing strategies based on Western marketing theories. Still, since the target is changing, a shift in researchers' approach is required to construct Japan-specific theories and disseminate them to the world.
I have outlined my field of specialization above, but finally, I would like to briefly introduce the research I am currently focusing on against this backdrop. One area is research on "leading edge" demographics, where we measure the degree of adoption of new products and services by observing the behavior of these people. It has high prediction accuracy without using sophisticated analytic methods and is well-received by companies. Next, I am also researching consumer behavior up to the point where they take a product in hand, using consumers' biological responses. The purpose is to capture changes in consumer feelings more objectively by applying new technology in the hope that this can eliminate the drawbacks of questionnaire surveys. Finally, I am also analyzing changes in shopping behavior due to aging based on long-term purchase history data. Through this approach, we can understand changes in purchasing behavior due to aging, and I am currently exploring what exactly triggers these changes.
In the above way, research on corporate marketing strategies aimed at consumers requires flexible adaptation by combining various theories in response to societal and data changes. Not having a single fixed answer or method is both challenging and what makes this work exciting.
