Data: Globalization and Information Research Essay.
Globalization and Information Research
DAT/565: Data Analysis And Business Analytics
May 15, 2020
Part 1
Globalization and Information Research
Strategic Moves That propelled Netflix’s Successful International Expansion
This paper will analyze how Netflix successfully expanded to over 190 countries in only seven years.Netflix’s expansion process and how the company worked with the new markets are the two most important strategic moves that saw it expand so quickly and successfully overseas. The company did not move to enter these markets at once, rather, it took up country by country, region by region (Brennan, 2018). The move to Canada was a failure since it was the old system. Netflix learnt from this and engaged phase two where had all the data on the new market, in terms of preferences, internet connectivity, and level of attractiveness of the country. Netflix worked for hand in hand with the local stakeholders to develop content that was suitable for the regions in which they had expanded. After these two phases, Netflix now had learned a lot and decided to add more languages, create a more personalized library of content and opened its way for its content to be supported by a variety of devices and payment modes.
Big Data Collection and Analytics
Big data collection and analytics were very important to Netflix in the sense that, Netflix was able to understand the market better and therefore develop content that suited the local consumer (Brennan, 2018). The data was based on several factors, including time, the device used to watch the show, whether the view paused and if paused continued to watch it later and so on.
The Meaning of Exponential Globalization
Moreover, the company has used exponential globalization. Exponential globalization refers to “…a carefully orchestrated cycle of expansion, executed at increasing speed, to an increasing number of counties and customers.” (Brennan, 2018)
An American Company That Failed to Expand Globally
Other companies have failed to expand globally. Wal-Mart, for instance, failed in countries like Germany and South Korea, in that the locals were not considered to be manager, rather the managers were Americans who did not understand the needs of the locals. In addition to this, there were cases where they sold goods that did not suit the locality. They sold some goods that did not suite the locality, placed goods on very high racks yet the locals were relatively shorter.
Reasons for Failure
Many companies have failed to expand in the past because they either overlooked or were faced with some critical issues. These factors include human resources, taxes and tariffs, political and economic uncertainties and cultural differences(Lendler, 2006).
Part 2
Hypothesis Testing
The test is aimed at evaluating the quality of call center operations in a given company by using two metrics; that is Time in Queue (TiQ) and Service Time (ST). A month’s data analysis shows the company’s average TiQ and ST are 150 seconds and 210 seconds respectively. The company wishes to improve metrics by replacing traditional protocol (TP) with the new protocol (PE). We wish to test if the mean Time in Queue is less than the standard time, 150 seconds.
H°: μ̥ ≥ 150 seconds
Hª: μ̥ < 2.5 (claim)
α=0.05
| n=853 | ||
| Mean= 163.64 seconds | ||
| Standard Deviation=162.91 | ||
Computation from Excel, a PE case
| n= 811 | ||
| mean = 158.05 | ||
| Standard Deviation=156.83 |
Computation from Excel, a PT case
This is a one-tailed test for hypotheses (Based Black, 2017). t- Statistics is used because the sample size, n= 853 is far much larger than 30. =-1.647 (T-value Calculator, n.d.). The rejection region.
Calculating t- standardized statistics- new protocol case (PT)
Calculating t- standardized statistics- Old protocol case (PE)
Test statistics fall outside the critical region. This is because both t-statistic calculated (1.46 and 2.45) are greater than the critical value.
Summary
Since 1.46 and 2.45 are greater than -1.647, the value lies outside the rejection region,
therefore, we f reject the claim and conclude the mean queuing time is not less than 2.5 minutes
at a 5% significance level. None of the protocols is effective since the queuing time (TiQ)
remains greater than or equals to the standard time, 2.5 minutes (150 seconds). Instead of
adopting the new protocol (PE), it would be much efficient for the organization to drop the new
protocol and continue utilizing the old protocol (PT). This because the full integration of the new
protocol would require resources to be put in place, ranging purchasing material and hire human
personnel to train the staff, but with no productivity output. The old protocol is already
established and would function just like the new protocol. There is no need for either material or
personnel inputs to use the old protocol. The use of the old protocol would enable the
organization to spare resources that otherwise can be spent in the new protocol without gain.
Such resources can be diversified to research for a more effective protocol that would
significantly reduce the time for queuing.
References
Lendler, M. &. (2006). World Business NY Times. Wal-Mart Finds That Its Formula Doesn’t Fit Every Culture.
Black, K. (2017). Business Statistics: For Contemporary Decision Making, (9th Edition). Hoboken, NJ: Wiley.
Brennan, L. (2018, October 12). How Netflix Expanded to 190 Countries in 7 Years. Retrieved from https://hbr.org/2018/10/how-netflix-expanded-to-190-countries-in-7-years