Statistical Analysis in Marketing

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Table of Contents

Introduction
Discussion
Conclusion

Introduction

The assignment offered data on monthly user visits to two Web sites over the past three years, for a total of 36 records for each Web site. To measure the specific number of hits, it is recommended that automatic services that continuously record website visits and produce error-free analytical reports be used. This will reduce data bias and perform statistical tests to make adequate decisions.

Discussion

Conducting a statistical test to determine patterns in distributions will allow a reliable and data-driven decision to be made about which Web site to choose for advertising. Descriptive statistics include measures of central tendency [=AVERAGE()] and measures of variation [=STDEV.S()]. Thus, for the first website, the average number of visits over the past three years was 10500 (SD = 110), and for the second, 10978 (SD = 133). Consequently, we can conclude from this data that, on average, the second website was visited more often than the first website during the last three years. The higher standard deviation value for the second website indicates that the data in the distribution are more scattered relative to each other, with each website averaging 133 units different from 10978. A T-test of Independent Samples conducted shows statistically significant differences, t(70) = -16.62, p <.05. Thus, the primary conclusion is that the second website is more favorable for advertising because it provides a broader audience.

However, the decision must be based on deeper results than the mean and standard deviation. If a manager needs a Web site to provide 12,000 hits per month, he or she should use =NORM.DIST(), which outputs the probability for a particular event. So, the calculated probability for the first website is 2.26×10^-43 (=NORM.DIST(12000, 10500, 110, FALSE)), and for the second website is 3.76×10^-16 (=NORM.DIST(12000, 10978, 133, FALSE)). In other words, the probability of reaching 12,000 hits is significantly higher for the second website than for the first. This also supports the conclusion that the second website is more favorable for advertising.

Conclusion

Thus, several instruments showed at once that the second website was more attractive for advertising. The results showed that the average number of visits to the second website was statistically significantly higher than for the first website. At the same time, it was shown that the probability of reaching the 12,000-view mark was higher for the second website than for the first. It follows that the manager should take a closer look at the second website for advertising.

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Statistical Analysis in Marketing

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