Netflix Company Analysis

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NETFLIX Company Analysis Michael M. Akers521 June 3, 2011 Academic Crib Sheet Continue your business analysis using the company you selected in Week Four. Write a paper no more than 2,400 words in which you research the company’s business environment. Review the company’s income statement, balance sheet, and cash flow to determine the financial health of the company. Be sure to compare your company to at least two other companies in the industry. Be sure to answer the following: • What have you learned about the company by reviewing each statement? • Is there information in any of these statements that concerns you? If so describe what it is and what it concerns. • How can management use this information moving…show more content…
In reality, there are many other potential variables housed in the database that could influence gross margin. These might include quantity sold, discount rate, commission paid, customer location, other purchases made, and length of time as a customer. If the discount rate is greater in the summer than in the autumn, the increase in gross margin could simply be a result of a lower discount rate and have nothing to do with a change in season. By removing the effect of discount rates on gross margins, gross margins might be found to be higher in the summer. In the final analysis, data mining examines all potential explanatory factors and associated data elements to ensure that the best pattern is retrieved from the data and that no potentially misleading effects are introduced into the chosen patterns. Going one step further, in place of showing the effect of only one condition, such as season, on gross margin, data mining can show the combined effect of a pattern, such as a particular time, location, and discount rate that produces the maximum gross margin. By replicating that pattern, a longer-term strategy that will systematically increase gross margin and associated profitability can be established. That optimal pattern becomes a basis for predicting future trends (Oakey, 1995). Today, data mining is considered
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