Value of Statistics in Business
As Mario Triola (2008) illustrates, statistics can help businesses “use sample data to form conclusions about populations” (p. 4). In order to make sound decisions, businesses must collect data from a sample of their intended customer base and analyze that data to determine the best course of action. The key to this process is not selecting the proper tools to apply to the data, but implementing the proper method of data collection. This means randomly selecting a sample of the target population that is of the proper size and free of bias. Additionally, the analysis must be designed to answer a specific question.
Once the data is collected, statistical tools can be used to highlight and understand its important characteristics. For example, a common method for finding the center of a range of values is to calculate the average, or mean, of all values. Although the mean is an interesting measurement, as a statistical tool it is highly impacted by the presence of outliers in the data. An outlier is “an extreme value that falls well outside the general pattern” (Triola, 2008, p. 119). Depending on the data set, it may be more useful to use the median, or middle, value to get a better sense of where the center of the data is. From a business perspective, identifying outliers may help identify a process that is seriously out of whack.
Statistics in the Wild
The American Customer Satisfaction Index is an example of statistical analysis being put to use in real world situations. Every quarter, the National Quality Research Center, in conjunction with the University of Michigan, interviews over 80,000 people regarding their satisfaction with over 200 companies in 45 industries. They take the information generated by the interviews and apply a proprietary formula derived at the University of Michigan. This formula results in a 100 point customer satisfaction score for the company. These scores can then be compared to other companies in the same industry or across industries (American Customer Satisfaction Index, n.d.).
This research utilizes the classic data collection techniques. The sample size of 80,000 interviewees is large enough to guarantee an accurate reflection of the total population. The questions are designed to generate the data necessary to plug into Michigan’s proprietary formula. Although the result of the formula is interesting, it is useless without statistical analysis. ACSI provides industry means for free via their website, but will do a more in depth analysis for their corporate customers (ACSI, n.d.). In the end, the question that has been answered is “How satisfied are American customers?”
When designing an observational study, it is important to understand the goal in order to ensure the right question is asked. In the case of a local bank considering the opportunity to offer a credit card with transaction-based incentives, there are two key questions. The first is whether or not the credit card industry has room for another credit card. The Federal Reserve Board releases monthly information regarding the state of consumer credit in the United States (Federal Reserve Board, 2009). This data can be used to determine how sizable the industry is and how it can be utilized by a local bank with local customers.
In order to attract customers, it might be necessary to provide incentives based on the kinds of transactions customers are making. Therefore, the bank must understand which industries receive the most retail dollars and how much money is being spent. The U. S. Census Bureau provides detailed spending information for many retail industries (U. S. Census Bureau, 2009). This information can be used to determine how customers are spending their money currently, to forecast how they will spend it in the future, and determine how proposed incentives will be effected by the anticipated volume.
American Customer Satisfaction Index. (n.d.). American Customer Satisfaction Index. Retrieved April 9, 2009.
Federal Reserve Board. (2009). Federal Reserve statistical release: Consumer credit. Retrieved April 9, 2009.
Triola, M. F. (2008). Elementary statistics (10th ed.). Boston: Pearson.
U. S. Census Bureau. (2009). Monthly retail sales & season factors. Retrieved April 9, 2009.