Description
Part 1: Globalization and Information Research
Context: Companies that perform well in their country of origin usually consider expanding operations in new international markets. Deciding where, how, and when to expand is not an easy task, though.
Many issues need to be considered before crafting an expansion strategy and investing significant resources to this end, including:
the level of demand to be expected for the company’s products/services
presence of local competitors
the regulatory, economic, demographic, and political environments
Carefully researching and analyzing these and other factors can help mitigate the inherent risk associated with an overseas expansion strategy, thus increasing the likelihood of success.
As a data analyst in your company’s business development department, you’ve been tasked with the responsibility of recommending countries for international expansion. You’ll write a report to the company’s executive team with your research, analysis, and recommendations.
Instructions:
Write a 525-word summary covering the following items:
According to the article listed above, what were the most important strategic moves that propelled Netflix’s successful international expansion?
The article mentions investments in big data and analytics as one of the elements accompanying the second phase of overseas expansion. Why was this investment important? What type of information did Netflix derive from the data collected?
According to the article, what is exponential globalization?
Not all international expansion strategies are a resounding success, however. Research an article or video that discusses an instance in which an American company’s expansion efforts in another country failed. According to the article/video you selected, what were the main reasons for this failure? Do you agree with this assessment?
Explain some of the reasons why certain companies’ expansion plans have failed in the past.
Part 2: Hypothesis testing
Context: Your organization is evaluating the quality of its call center operations. One of the most important metrics in a call center is Time in Queue (TiQ), which is the time a customer has to wait before he/she is serviced by a Customer Service Representative (CSR). If a customer has to wait for too long, he/she is more likely to get discouraged and hang up. Furthermore, customers who have to wait too long in the queue typically report a negative overall experience with the call. You’ve conducted an exhaustive literature review and found that the average IQ in your industry is 2.5 minutes (150 seconds).
Another important metric is Service Time (ST), also known as Handle Time, which is the time a CSR spends servicing the customer. CSR’s with more experience and deeper knowledge tend to resolve customer calls faster. Companies can improve average ST by providing more training to their CSR’s or even by channeling calls according to the area of expertise. Last month your company had an average ST of approximately 3.5 minutes (210 seconds). In an effort to improve this metric, the company has implemented a new protocol that channels call to CSR’s based on the area of expertise. The new protocol (PE) is being tested side-by-side with the traditional (PT) protocol.
Instructions:
Access the CallCenterWaitingTime.xlsx file. Each row in the database corresponds to a different call. The column variables are as follows:
prototype: indicates protocol type, either PT or PE
QueueTime: Time in Queue, in seconds
service time: Service Time, in seconds
Perform a test of hypothesis to determine whether the average TiQ is lower than the industry standard of 2.5 minutes (150 seconds). Use a significance level of α=0.05.
Evaluate if the company should allocate more resources to improve its average TiQ.
Perform a test of hypothesis to determine whether the average ST with service protocol PE is lower than with the PT protocol. Use a significance level of α=0.05.
Assess if the new protocol served its purpose. (Hint: this should be a test of means for 2 independent groups.)