Globalization and Information Research

Globalization and Information Research

Part A: Globalization and Information Search

Information search through market research and development is one of the most critical marketing management aspects of a business that intends to venture into the global business sphere. Ideally, after a company succeeds within its local boundaries, expanding beyond the local boundaries is vital for its revenue streams and overall profit. However, formulating an expansion strategy is not an easy task. This is why some businesses fail when attempting to venture into new markets, as seen in the case of Starbucks. The current analysis focuses on Netflix to explore expansion strategies and the importance of data analytics in market globalization.

The Most Important Strategic Moves That Propelled Netflix’s Successful International Expansion

According to the article by Brennan (2018), the two core factors that have contributed to the company’s growth are its three-stage expansion strategy and how it appealed to its customers. In entering the market, the company did not enter all the markets at once. Netflix started with countries whose market segments were similar to its local market in the US. The experiences and lessons learned from these markets acted as a guideline for entering the new markets. As of 2015, the media giant was operating in approximately 50 countries. Secondly, in order to appeal to potential customers, the company adapted to the local cultures and preferences of those customers, through its content and partnerships with the local stakeholders.

Why was this investment important? What type of information did Netflix derive from the data collected?

Netflix used big data analytics as a crucial component in its expansion overseas. The second phase in its expansion strategy entailed detailed expansion. To obtain reliable data and information regarding the market choices, the company invested in technology on big data and analytics. The investment was important since it offered a basis for predicting consumer behavior and decision-making (Spotfire, 2014). Important information and dynamics obtained through analytics included the channels used by the potential customers to access information, their lifestyle and income factors, and the cultural aspects such as the appropriate content that could affect Netflix’s entry into the new markets. This enabled the company to tailor engagement and marketing strategies to the preferences of the particular target group.

What is Exponential Globalization?

According to the article, exponential globalization refers to a market entry and expansion strategy that entails a carefully strategized and planned expansion cycle, implemented aiming expansion to enter more countries to reach more customers and actualized with an increasing speed. In fact, Brennan (2018) suggests that other companies can employ the method to enter new markets.

US Companies with Failed Expansion Efforts Externally; Starbucks Case of Australian Market

The company is a leader in coffee chain stores in the world. However, its efforts to push its business into Australia in 2000 did not turn out as expected (Seale, 2020). This is because the local market dominated the Australian coffee supply, and Starbucks was deemed expensive for those who visited coffee chains more often. The market entry into Australia was a struggle until 2008, when the company decided to close its 61 stores, leading to a loss of $ 143 million (Seale, 2020). Starbucks even retained some of its chain shops in Australia until 2014, when it handed over the 24 chain shops to Withers Group, which now operates the chain shops in Australia. The assessment seems reliable since it provides links to external sites containing in-depth analysis of the instances when companies failed in new market entries.

Some of the reasons why certain companies’ expansion plans have failed in the past.

The article by Seale (2020) lists some of the reasons why companies may fail. These include poor timing, unaccommodating cultural practices, restrictions by law in the new markets, stiff competition from local producers, and poor market study and analysis, among other factors.

Part B: Hypothesis Testing

	Queue Time			Service Time
	Mean	147.9223		Mean	180.1207
	Standard Error	3.372181		Standard Error	4.662403
	Median	105		Median	124.5
	Mode	55		Mode	69
	Standard Deviation	137.9713		Standard Deviation	190.7601
	Sample Variance	19036.07		Sample Variance	36389.42
	Kurtosis	8.189286		Kurtosis	41.7482
	Skewness	2.647745		Skewness	4.91375
	Range	830		Range	2635
	Minimum	15		Minimum	27
	Maximum	845		Maximum	2662
	Sum	247622		Sum	301522
	Count	1674		Count	1674
	PE Queue Time			PE service Time
	Mean	147.5557		Mean	149.2802
	Standard Error	4.68738		Standard Error	6.364879
	Median	107		Median	78
	Mode	55		Mode	69
	Standard Deviation	136.9004		Standard Deviation	185.8937
	Sample Variance	18741.71		Sample Variance	34556.46
	Kurtosis	9.098346		Kurtosis	38.62937
	Skewness	2.741034		Skewness	4.846179
	Range	830		Range	2237
	Minimum	15		Minimum	33
	Maximum	845		Maximum	2270
	Sum	125865		Sum	127336
	Count	853		Count	853
	PT Queue Time			PT Service Time
	Mean	148.3033		Mean	212.1632
	Standard Error	4.856634		Standard Error	6.651303
	Median	103		Median	153
	Mode	55		Mode	90
	Standard Deviation	139.1576		Standard Deviation	190.5804
	Sample Variance	19364.84		Sample Variance	36320.9
	Kurtosis	7.354816		Kurtosis	48.89362
	Skewness	2.559449		Skewness	5.367946
	Range	792		Range	2635
	Minimum	19		Minimum	27
	Maximum	811		Maximum	2662
	Sum	121757		Sum	174186
	Count	821		Count	821

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.

H0 = The average TiQ is equal to the industry standard of 2.5 minutes (150 seconds)
Vs.
H1 = The average TiQ is lower than the industry standard of 2.5 minutes (150 seconds)
H0 : µ = 2.5minutes vs. H1 : µ < 2.5minutes

= (147.92-150)/(137.97/√1674) = (-2.08)/3.372
= -0.6168
Since the obtained z-statistic score, the p-value of (-0.6168), is less than the critical value obtained from the table (1.645), we reject the null hypothesis that the average TiQ is lower than the industry standard of 2.5 minutes.

Whether the Company Needs to Allocate More Resources

From the results obtained above, it is recommended that the company invest more resources to ensure that the Time in Queue is less than the current industry average.

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.

H0 = The average ST with service protocol PE is equal to PT protocol
Vs.
H1 = The average ST with service protocol PE is less than with the PT protocol
H0 : PE= PT vs. H1 : PE < PT
Where; X1= 149.28, n1=853, S12=34556.46
X2 = 212.163, n2=821, S22= 36320.9
Since n1 and n2 >30, we use a Z-test

but,
= S p = √((853-1)34556.46+(821-1)36320.9)/(853+821-2) = (29442103.92+29783138)/1672 = 35421.795
= √35421.795= 188.207

= (149.28-212.16)/(188.207√1/1147+1/529) = (-62.88)/9.89 = -6.36
Therefore, the p-value obtained is -6.36, which is less than the critical value of 1.64. This means that we reject the null hypothesis and conclude that the average ST with service protocol PE is equal to the PT protocol.

Whether the New protocol served its purpose

Technically, the new protocol was introduced to reduce the average service time customers were served. Since the results indicate that there is no difference in service time between the new protocol (PE) and the traditional protocol, the new protocol did not serve the intended purpose. It is recommendable for the organization to make necessary adjustments.
Therefore, as seen in the case of Netflix and the hypothesis testing for the customer service representative context, a business needs to analyze its current strategies before introducing a new strategy. Other than making decisions guided by illusions, data analysis on insightful metrics guides a business on the most suitable strategy and measures to employ, such as changes in customer numbers, revenue trends, and other metrics.

References

Brennan, L. (2018). “How Netflix Expanded to 190 Countries in 7 Years.” https://hbr.org/2018/10/how-netflix-expanded-to-190-countries-in-7-years
Seale, A. (2020). “Seven Epic Cases of Companies That Failed Internationally.” https://www.firmex.com/resources/blog/seven-epic-fails-by-businesses-that-tried-expanding-into-foreign-markets/
Spotfire. (2014). “Big Data Analytics: The Gateway to Success in New Markets.” https://www.tibco.com/blog/2014/03/04/succeed-at-entering-new-markets-via-big-data-analytics/

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Globalization and Information Research

Purpose 

This assignment is intended to give you an opportunity to strengthen your skills in gathering and analyzing business-related information. It provides a deeper understanding of how companies can look at globalization as part of their strategic and operational plans. The assignment has two parts: one focused on information research and analysis, and the other is on applied analytics.

Resources: 

• Microsoft Excel®
• “How Netflix Expanded to 190 Countries in 7 Years” from Harvard Business Review: https://hbr.org/2018/10/how-netflix-expanded-to-190-countries-in-7-years
• Call Center Waiting Time – SEE ATTACHMENT

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 500- to 750-word paper to include Part I and Part II below.

Part I – Responses to the following items:

1. According to the article listed above, what were the most important strategic moves that propelled Netflix’s successful international expansion?
2. 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?
3. According to the article, what is exponential globalization?
4. 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?
5. 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 TiQ 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 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 calls to CSR’s based on area of expertise. The new protocol (PE) is being tested side-by-side with the traditional (PT) protocol.

Instructions: Perform a test of hypothesis for the two scenarios below. Each test should follow the steps for the hypothesis testing process. Write a summary of your conclusions for the following calculations including the results of the hypothesis tests:

Instructions:

1. Access the Call Center Waiting Time file. Each row in the database corresponds to a different call. The column variables are as follows:
   • ProtocolType: indicates protocol type, either PT or PE
   • QueueTime: Time in Queue, in seconds
   • ServiceTime: Service Time, in seconds
2. 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.
3. Evaluate if the company should allocate more resources to improve its average TiQ.
4. 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.
5. Assess if the new protocol served its purpose. (Hint: this should be a test of means for 2 independent groups.)
6. Compute and submit your calculations in the spreadsheet to show your work.

Format your paper consistent with APA format and at least one (1) scholarly, peer-reviewed reference and one reference from the assigned readings (textbook). Points will be deducted for not including either of them.

Please Include:

• An Introduction paragraph (Do Not Write the word Introduction to begin the body of the paper – Use the title of the paper instead). There should be a thesis statement in the introduction paragraph.
• At least two (2) Level One Headings
• Conclusion Heading – Do not begin conclusion paragraph with “In conclusion.”

Submit the Excel Spreadsheet and the Paper. You must submit the spreadsheet to show your work.

CALL_CENTER_WAITING_TIME