A variety of factors can influence consumer behavior, and it is important to study these factors in order to develop a strategy for the most effective retailer behavior. A modern vision of consumer behavior depends on analyzing consumers’ purchasing trends, their use of goods, as well as their emotional responses to the proposed products (Kardes, Cronley, & Cline, 2014). The examination of consumer behavior analytics and associated retailer behavior analytics is also important while comparing consumers’ behaviors and sales in stores of Apple and Microsoft, two giants in the sphere of information technology (Arthur, 2014).
In order to create the market advantage, analysts in Apple and Microsoft should pay much attention to examining how consumer behavior can influence their sales, and what differences can be observed in relation to the progress of these stores (Erevelles, Fukawa, & Swayne, 2016).
Thus, Apple stores and Microsoft stores have many similarities in terms of size, design, and products, but there are also differences in consumer behavior and retailer behavior which influence these companies’ sales (Arthur, 2014). In spite of the fact that there are many studies which investigate aspects of consumer behavior and principles of Apple’s and Microsoft’s marketing strategies, there is still a gap in the literature regarding the comparison of consumer and retailer behavior analytics in relation to Apple and Microsoft stores (Arthur, 2014; Yaeli et al., 2014).
The goals of this paper are to conduct a market research study and examine differences in consumer behavior and retailer sales behavior observed in Apple and Microsoft stores. The information on RateMyProfessors.com and the literature on the topic were researched prior to conducting the study in order to determine the following objectives to cover the research gap:
- to determine statistically significant differences between Apple and Microsoft stores in terms of customer behavior analytics;
- to determine statistically significant differences between Apple and Microsoft stores in terms of retailer behavior analytics;
- to determine statistically significant differences between Apple and Microsoft stores in terms of retailer attributes and behavioral analytics;
- to determine predictor variables that could influence customer behavior analytics and retailer behavior analytics in relation to the studied stores.
In order to reach the set goals and address the stated objectives, a quantitative study based on a survey was developed with reference to analyzing the data related to 44 retail stores, including both Apple and Microsoft stores located in four different market segments: East, West, Midwest, and South segments. To reach the complex goal and address all objectives formulated for the study, it was important to develop a reliable research instrument which included three parts: a 15-item survey instrument, a 5-point Likert scale, and a close-ended questionnaire. The received data were analyzed with the help of software to conduct statistical tests, determine frequencies, indicate comparisons based on the crosstab analysis, and provide the results of a t-test, correlation, and PLS-SEM modeling.
The findings received as a result of the complex data analysis were used to address the purpose of the study based on comparing the performance of Apple and Microsoft retail stores in terms of the observed consumer behavior, retailer behavior, and associated analytics and sales. It was important to conduct a series of tests in order to receive the most credible and reliable data. Furthermore, it was necessary to compare the results in order to conclude about possible differences in the studied analytics and the stores’ performance.
References
Arthur, C. (2014). Digital wars: Apple, Google, Microsoft and the battle for the Internet (2nd ed.). Philadelphia, PA: Kogan Page Publishers.
Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big data consumer analytics and the transformation of marketing. Journal of Business Research, 69(2), 897-904.
Kardes, F., Cronley, M., & Cline, T. (2014). Consumer behavior (2nd ed.). New York, NY: Cengage Learning.
Yaeli, A., Bak, P., Feigenblat, G., Nadler, S., Roitman, H., Saadoun, G.,… Sandbank, T. (2014). Understanding customer behavior using indoor location analysis and visualization. IBM Journal of Research and Development, 58(5), 1-12.