Doomsday Package Marketing Research Report

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As for our project, we choose focus groups, personal interviews and questionnaire to get the important first hand data and information. In addition, we obtain the related secondary information on the Internet.

A focus group is used as a preliminary research technique to explore peoples’ ideas and attitudes. It is often used to test new approaches and to discover customer concerns. We think this method is suitable for our new product in Hong Kong. We choose a group of 8 people meet in a conference-room-like setting with a trained moderator. The moderator leads the group's discussion and keeps the focus on the areas you want to explore.

Personal interviews are a way to get in-depth and comprehensive information. They involve one person interviewing another person for personal or detailed information. Before we started our survey, each of our group member conducted two cases.

Questionnaires are a good way to obtain information from a large number of people. Questionnaires typically contain multiple choice questions, attitude scales, closed questions and open-ended questions. We sent out 120 and got 106 valid to analyze.

Secondary information will be searched and studied from news, dissertations, journal articles and even videos carried by both paper books and Internet. Such as income statistic and purchase power statistics we use to analyze the capacity of the potential market.

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4. RESULTS [pic 5]

4.1 Attraction Concept Maps

We use this acceptation-attraction graph to estimate market potential toughly.

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As we can see from the graph, x-axis represents how much people accept a new concept—our doomsday package in this case; y-axis represents how people are attracted by our new product. We use data from Q10 (I would get prepared for the possible shortage of supplies caused by catastrophic and unpredictable events) for acceptation; and use data from Q26(Are you willing to buy an emergency package) for attraction. Among these dots, more samples are contained, bigger and darker the dot would be. As the graph shows, dots are bigger and darker at top right corner, which means our new product is highly accepted and attracted by people. Thus, our market potential is promising.

4.2 Regression Analysis

We chose Q10 to reflect buying intention of doomsday packages in Hong Kong. At the meantime, we chose Q1/2/4/5/6/8/11/12/14/15 as attributes that influence buying intention of customers.

Then we run data collected from the questionnaire we designed. We collected 106 answers, however, there is only 88 efficient.

After run the data, we found the correlation between Q4 and Q10 is 0.096, Q6 and Q10 is 0.062, so the correlation is too small to have any consequences.

It’s important to find out how to increase buying intention of customers. So we run the adjusting data.

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The KMO statistic is a summary of how small the partial correlations are, relative to the original (zero-order) correlations. The partial correlation for each pair of variables in the factor analysis is comprised of the correlation between those variables after partial ling out the influence of all of the other variables in the factor analysis. If the variables share common factor(s), then the partial correlations should be small and the KMO should be close to 1.0. The KMO measure should equal 0.5 when the correlation matrix equals the partial correlation matrix. From the above chart, the value of KMO is 0.669, so the attributes we chose were suitable to do data analysis.

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With principal component analysis, we got above chart. It can be seen that eigenvalue of the first component contributes 35.233%, and the second contributes 54.813%. So these two principal components can reflect most information of original indices.

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Variable1-6 are corresponding to Q1/2/5/6/12/14 in our questionnaire:

VAR00001: Have you ever heard about supplies shortage caused by any reasons?

VAR00002: I think sudden shortage of basic necessities will happen.

VAR00003: My personal survival is possibly threatened by supplies shortage due to unexpected dangerous factors.

VAR00004: Have you ever experienced lack of supplies in short term (1 week to 3 months) due to catastrophic events?[pic 14]

VAR00005: How long would your essential supplies support when lack of supplies happen?

VAR00006: How much do you know about emergency package?

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The first component consists of variable 1/2/3/4, and the correlation between component 1 with buying intention is 0.617, that means the more people realize the importance of shortage of resources and worry about it, the buying intention is bigger. Maybe we can target these people who have been through the shortage of resources (food), and who believe the existing of doomsday.

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As can be seen from the graphs, the recursion is acceptable. So the data analysis is reasonable.

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Q39 reflects how likely customers will buy the package we design. The above chart shows that buying intention of doomsday package has correlation of 0.509 with buying behavior. As long as customers have buying intention, nearly half of them will the packages we design. So the benefits with the packages suit the customers’ needs.

However, as variable4 (Q6: Have you ever experienced lack of supplies in short term (1 week to 3 months) due to catastrophic events?) has least correlation because it shows nearly similar significance in both main factor 1 and 2, so delete it to do data analysis again.

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KMO increases, so it is more suitable to do analysis.

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