Coffeetime: Using Research in Decision-Making

Running Head: RESEARCH AND DECISION MAKING

Research and Decision Making

University of Phoenix

MBA 510

CoffeeTime: Using Research in Decision-Making

CoffeeTime is a global coffee retailer looking to expand into Southeast Asia. Before opening up shop, they conducted research to learn more about India’s emerging market.

Total Access provided market research to CoffeeTime. From this research, CoffeeTime learned basic geographical facts about India, such as its population and area size. They learned detailed demographical information about the people including age, gender, religion, degree of affluence, their cultural outlook (modern or traditional), leisure habits, and educational level. Demographics were broken down by city so that CoffeeTime could use the information to decide which cities in India they should enter.

What CoffeeTime does not know that they might learn from further research is whether Indians might be receptive to an American coffee bistro. CoffeeTime does not know whether the people of India want to drink expensive coffee drinks at an upscale coffee bar, and whether or not they would visit CoffeeTime. By studying demographics, you cannot predict people’s behavior. Even if you survey them, what they say they will do and what they actually do could be very different.

How do the limitations of the data available to Coffee Time affect the validity and reliability of the data? The validity and reliability of the data is in question due to the limiting aspects of the test and focusing on coffee drinkers. Focusing strictly on coffee drinkers and not the percentage of the population that drinks coffee in India will greatly skew the results. Limiting the scope to this population can pose a tremendous threat to the outcome and resources CoffeeTime has already spent and could potentially invest in India.

Another aspect CoffeeTime should consider is the reliability of the secondary data that has led them to the decision to expand in India. How old is the data? Who did the data collection? Is the data trustworthy? CoffeeTime had to choose between several possible countries to enter and expand their company. Taking the advice from a secondary source is helpful but should not be the sole determining factor in determining where to focus the resources.

CoffeeTime needs to ensure that the expansion of its franchises into the South Asian marketplace are successful. After a comprehensive marketing analysis was completed, there was additional data that could have been used to achieve a more accurate and precise picture of India’s marketplace. At first glance, CoffeeTime will need to primarily compile and analyze inferential statistics that have been colleted by the marketing and research firm. Inferential statistics are defined as “the methods used to determine something about a population on the basis of a sample,” (Lind, 2004). Therefore, if the data collected is to be an accurate representation of the population, the sample must be a mirrored image of the true sample population. This type of research has proven to be a successful tool for analyzing data as many large television stations use Nielsen to sample the likes and dislikes of their viewing population.

The data being analyzed should also include both quantitative and qualitative to ensure the variables being studied are properly represented. The measurement of this data must be correct and precise and should not simply convey the average of the sample population. In fact, an average is not always an accurate depiction of the data being presented because the average does not always reflect the typical occurrence, (Lind, 2007). Given this information, more detailed competitive data could have been presented to allow a better decision to be made in terms of placement in the competitive marketplace. For example, Ahmedabad has no competitive coffee franchises, and the inclusion of this city’s data in the analysis could skew the overall statistics.

It can be reasonably assumed that CoffeeTime will likely perform well in that market because of the absence of competitive coffee shops and the affluent population of the city. The people can afford to pay the prices for specialty coffee and the population is large enough to support the coffee shops. Mumbai, however, has an affluent population but the competition already has a presence in the marketplace. Because the population is affluent, CoffeeTime has the potential to be successful in this city despite the competitive element. This is an example of how statistics can be skewed if all variables and sample populations are not properly collected and analyzed.

There are several ways that Coffee Time can