Consider the use of statistical analyses to identify customers that are most likely to defect to a competing brand

Open-ended questions:

1. Consider the use of statistical analyses to identify customers that are most likely to defect to a competing brand; in your view, would it be
a. more difficult to assess the validity or the reliability of data analytic results? and
b. more critical to assess the validity or the reliability of data analytic results?
Why? Clearly justify your answer. (10 points)

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2. Under what circumstances would it be appropriate to use a one-tailed test of significance, and under what circumstances would it be appropriate to use a two-tailed test of significance? Make sure to give and overall explanation AND also provide an example of each situation.

3. What are the key differences between the notions of estimation and statistical significance testing, in terms of what each notion purports to accomplish? Be clear and explicit in your answer.

4. You just finished conducting descriptive analyses using a sample of Verizon (a telecommunications company) customers, and would now like to generalize your results onto the full universe of Verizon customers – what are the key considerations that you should address, in order to make sure that your conclusions are valid?

15. You are a new marketing analyst for Tide, a leading brand of laundry detergent. You were given a sample of Tide customers and were tasked with estimating the average customer spending of the entire population of Tide buyers. Describe the data analytic / statistical process you would employ to produce valid and reliable estimates of average customer spending. (15 points)


Sample Answer



It is more critical in assessing the validity or reliability of a defecting customer. This is because they can help to identify specific factors that may lead to defection. For example, it might be possible to identify which customers are more likely to switch based on their purchasing habits, demographics, or other behavioral cues. Using statistical analyses can help in identifying customers that are more likely to defect and subsequently make strategic decisions based on this information; this may include adjusting marketing campaigns, developing new products or services that attract these types of consumers, or even changing the way to do business altogether. It’s important to remember that not every customer axe will be accurate; however, by applying a consistent methodology across your entire cohort of customers, one can minimize the chances of losing them altogether.


In statistical analysis, two types of significance tests are carried out: one-tailed and two-tailed analysis. Each has advantages and disadvantages, and the circumstances determine the best test. A one-tailed test is appropriate when the research question is phrased so that a directional hypothesis can be tested. In other words, the theory predicts that the outcome will be greater than or less than a specific value. A one-tailed test, for example, could be used by a researcher to determine whether a new medication effectively reduces anxiety. If the medication significantly reduced anxiety levels, the researcher could conclude that the medication is effective. A one-tailed test, on the other hand, has limitations. First, finding a current value against which to compare the results can be challenging.

Second, there is always the possibility that the results could have gone in the opposite direction than expected; even if the results are significant, it is impossible to know whether they would have been even more meaningful if they had gone in the opposite direction. A two-tailed test is appropriate when the research question is not phrased in a way that allows for a directional hypothesis. For example, suppose a researcher wanted to see if there was a difference. The hypothesis predicts that the results will differ from a current value but does not specify whether the differences will be greater or lesser.


The critical distinctions between estimation and statistical significance testing are what each notion seeks to accomplish. In general, estimation is concerned with estimating the value of a population parameter, whereas statistical significance testing is concerned with determining whether a study’s results are due to chance. The process of determining the value of a population parameter, such as the mean or variance, based on sample data is referred to as estimation. Statistical significance testing, on the other hand, is used to determine whether the results of a study are due to chance or are likely to be true for the entire population. Both methods, however, can be used to conclude the population as a whole.


When attempting to generalize your results from a sample of Verizon customers to the entire universe of Verizon customers, there are a few key considerations that you should address. To begin, ensure that your sample is representative of the whole population; this includes making sure that the sample is representative in terms of demographics as well as customer behavior Your conclusions may be invalid if your sample is not representative. Second, you must ensure that your data has sufficient variability. If all of the Verizon customers in your sample have very similar characteristics, drawing meaningful conclusions about the Verizon customer base will be difficult. You must have enough data points to see patterns and trends emerge. Third, be aware of any potential biases in your data. For example, if you only surveyed people who expressed an interest in participating in a survey, your results may be inaccurate. Furthermore, suppose you conducted your survey online. In that case, you may be missing out on the perspectives of people who do not use the internet or do not have access to a computer/smartphone.


The average customer spending for Tide is $36 per year. This means that the total amount of money spent by all consumers who have bought Tide in the past is $864 million. interesting marketing statistics in this answer, so be sure to read it all! First off, it is interesting to note that the average customer spends $36 on Tide each year. This means that there are billions of dollars being spent by consumers on this product. Additionally, it is fascinating to learn about how much money was spent overall by all customers who have ever bought Tide products. Based on this information, we can assume that the total amount of money spent by all Tide buyers is quite sizable. To estimate how many people fall into this category, you will need to divide $864 million by 365 which equals approximately $17.39 per person. This number can then be used to create a chart or graph with data points representing different demographic groups and their corresponding average spend size.

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