Today's online case presented us with a data set comprised of external and internal data, asking us to find the relationship (if any) between these data, and then develop recommendations based on the relationship found.
Many of you identified the importance of the external data in this data set: customer satisfaction. This is a crucial external measure; if we don't have satisfied customers, we don't have a business. The key in this case was to compare the customer satisfaction data with both the new applicant processing time and the plastic production turnaround time, which most of you did. Our goal is to understand the relationship between customer satisfaction and these internal metrics. Is there a relationship? In other words, does the speed at which we process new applicants and/or produce a new credit card have an impact of customer satisfaction? And if so, how much?
Using Excel to calculate the correlation coefficient between customer satisfaction and new application processing time, you found r = -0.41. The correlation between customer satisfaction and plastic production turnaround time was -0.21. Both of these suggests a negative relationship: customer satisfaction goes down as both of these times go up. But the relative strengths of these relationships is relatively weak. You visually confirmed this in your scattergrams.
Ashlyn, Andrew, James and Korey agree that better information is needed before the business can make any decisions about how to improve customer service. Better customer satisfaction surveys could be a part of this. Ethan gets us to the heart of the matter: our correlation coefficients suggest that while new application processing time and plastic production turnaround time are somewhat related to customer satisfaction, there are other drivers of customer satisfaction not captured in these metrics. To James' point, the departments need to work more effectively together to identify other potential variables (causes) and identify relevant metrics.
Another statistic that can help us understand the strength of the relationship between variables is the coefficient of determination, or r squared. The coefficient of determination tells us how much of the variance in a dependent variable is due to the variance in an independent variable, expressed as a percentage. See the excellent 3-minute video below for more background and discussion.
Take a minute to calculate the coefficients of determination for our case r-values. How much of the variance in customer satisfaction is accounted for by new applicant processing time and plastic production turn around time? What other internal company operations factors might be drivers of customer satisfaction?
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