The real-world business situation proposed would be the consumer behavior in Starbucks, particularly with reflection to how well prepaid cards have been faring. As noted by Moon and Quelch (2003), the emphasis of innovation is noted on what measures have been put in place to enhance the customer experience. Norvell and Horky (2018) supported this by noting how card programs and other associated means of increasing profitability and revenue have been vital toward maintaining the inherent sales potential that exists in the company.
Report on Data Collected
The essay writer data collected involved the denomination of the prepaid card, the customer’s age, the frequency of being in Starbucks, and the number of cups of coffee per day. This information would then be analyzed further to determine any relationship between the factors. The following graphs summarize the data collected.
The graph shows the denomination and the frequency of consumers who would purchase the cards. A majority of consumers would purchase cards in the $24 range. The graph is important because it may indicate that, should there be a strong relationship with the other factors, then possible marketing strategy to promote prepaid cards around the range would provide better sales.
Analyzing the Data Using Multiple Regression
Multiple regression involves considering the linearity of each parameter and cross-checking such pair-by-pair. This is just an extension of linear regression that is designed to compare multiple variables so that the presence of any relationship may be established (Fry, 2019). Microsoft Excel’s Data Analysis tools were used to determine the multiple regression information.
Regression statistics were shown as follows:
| Regression Statistics | |
| Multiple R | 0.57159159 |
| R Square | 0.32671694 |
| Adjusted R Square | 0.24903043 |
| Standard Error | 21.8801988 |
| Observations | 30 |
The regression statistics shows an R-square value of 0.33. This indicates a weak relationship, but it still is something to draw perspective on.
The following table shows the results of multiple regression:
| Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | Lower 95.0% | Upper 95.0% | |
| Intercept | 17.2762853 | 20.893918 | 0.82685714 | 0.41584234 | -25.671778 | 60.2243488 | -25.671778 | 60.2243488 |
| Age | 0.12314115 | 0.39850609 | 0.30900694 | 0.75977889 | -0.6959998 | 0.94228214 | -0.6959998 | 0.94228214 |
| Frequency in Days | 4.12571423 | 1.79109146 | 2.30346374 | 0.02950357 | 0.44407301 | 7.80735545 | 0.44407301 | 7.80735545 |
| Cups of Coffee per day | -5.0952959 | 4.20487871 | -1.2117581 | 0.23650298 | -13.738548 | 3.54795603 | -13.738548 | 3.54795603 |
With the Prepaid Card denomination as the dependent variable and age, frequency in days per month, and the number of cups of coffee per day as independent variable, the p-values indicate the significance of the relationship between the variables. Age and Prepaid card denomination show no significant relationship (p = 0.76). Frequency in Days and Prepaid card denomination show a significant relationship (p < 0.05), albeit weakly correlated. Cups of Coffee per day and Prepaid card denomination show no significant relationship (p = 0.24).
Multiple regression was chosen because of how it well represents the analysis of the data presented, especially comparing the various variables with respect to the Prepaid card denomination.
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Implication of the Data Analysis
The significant relationship between the Frequency in Days and the Prepaid card denomination indicates that an increase in frequency would merit an increase in prepaid card denomination. The more time spent in Starbucks would predict potential purchase of higher Prepaid card denomination.
The limitation of the analysis involved a small sample size of 30 people over a short span of time. Future analysis would be even more effective considering more people and a longer span of study, especially in improving the R-square value in terms of the correlation noted in the process.
The recommended course of action would be to proceed with the sales of the prepaid cards and promote an environment of customer loyalty as the data would show that the more a person frequents the establishment, the higher the denomination the person would purchase.
References
Fry, G. S. (2019). Business statistics a decision-making approach. Pearson Education Limited.
Moon, Y., & Quelch, J. A. (2003). Starbucks: delivering customer service. Harvard Business School.
Norvell, T., & Horky, A. (2018). Bonus gift card programs: a methodology to measure the impact on revenue and profit. The International Review of Retail, Distribution and Consumer Research, 28(4), 397-413.
