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# Market Analysis: The Pizza Study

What is market analysis? How is it done? This article describes how market analysis works using data on a pizza study.

After having defined marketing research in my previous post and giving an example conceptual framework for a pizza study, I decided to get into the details of market analysis using a standard multivariate statistical analysis tool. I saw the need of writing this article upon reading several articles on market analysis. There is a need to demonstrate what is market analysis.

Before everything else, the concept “market analysis” should be defined first.

What is market analysis and how is it used?

## Market Analysis Defined

Marketing strategies work best when founded on a systematic evaluation of consumer preferences. What do consumers want? How do they respond to a product or service? Marketing research provides answers to these questions.

Hence, market analysis can be defined as the process of evaluating consumer preferences using a systematic approach such as marketing research, among others. Market analysis is a detailed examination of the elements or structure of the market.

Why is a market analysis done? An analysis is done to draw out important findings for interpretation, discussion and finally, a decision on what steps to make.

## The Pizza Study

Once again, the conceptual framework given in the pizza study is given below to serve as a reference in the following discussion.

To find out what customers want, let us have a sample data of feedback from 200 pizza shop customers. To understand how analysis works, you need to read the article on variables as these are important units of analysis. If you already understand what variables are, then proceed to read the rest of the discussion.

## Coding the Variables for Market Analysis

Let us have the following measures for the variables in this study namely pizza taste, service speed, and waiter courtesy:

Pizza Taste

3 – Moderate
4 – Good
5 – Very good

Service Speed
1 – Satisfied
0 – Not satisfied

Waiter Courtesy
1 – Courteous
0 – Not courteous

Level of Satisfaction
Let us assume that the following Likert scale applies to the customer’s level of satisfaction:

1- Not at all satisfied
2 – Slightly satisfied
3 – Moderately satisfied
4 – Very satisfied
5 – Extremely satisfied

If for example, the customer is satisfied with pizza taste, service speed, and waiter courtesy; he rates everything “5.” If he is not satisfied with courtesy, then he might rate it a “0.”

## Multiple Regression Analysis

Below is a data set representing the response of 200 pizza customers that serves as input to multiple regression analysis (you may try the data set if you know how to compute using multiple regression):

You may skip this table by clicking the link below:

A table summarizing the results of the pizza survey.

 Customer # Satisfaction Taste Speed Courtesy 1 5 5 1 1 2 5 4 1 1 3 4 4 1 1 4 4 4 1 1 5 4 4 1 1 6 3 5 0 0 7 5 5 1 1 8 4 5 1 1 9 5 5 1 1 10 4 4 1 1 11 4 4 1 0 12 4 4 1 1 13 4 3 1 1 14 5 3 1 1 15 4 3 1 1 16 3 4 0 1 17 4 4 1 1 18 5 4 1 1 19 5 4 1 0 20 5 4 1 0 21 4 4 1 1 22 3 5 0 1 23 4 5 1 1 24 4 4 1 1 25 3 4 0 1 26 4 5 1 1 27 5 5 1 1 28 5 4 1 1 29 4 4 1 1 30 3 4 0 1 31 3 5 0 1 32 3 5 0 1 33 3 4 0 1 34 4 4 1 1 35 4 3 1 1 36 4 2 1 0 37 5 4 1 1 38 5 3 1 1 39 5 3 1 1 40 4 3 1 1 41 4 4 1 1 42 5 4 1 1 43 4 4 1 1 44 5 3 1 1 45 4 4 1 1 46 5 4 1 1 47 4 5 1 1 48 5 5 1 1 49 4 5 1 1 50 5 5 1 1 51 4 5 1 1 52 5 4 1 1 53 4 4 1 1 54 5 4 1 1 55 4 4 1 0 56 5 5 1 1 57 3 5 0 1 58 3 5 0 1 59 3 5 0 1 60 4 4 1 1 61 4 4 1 0 62 4 4 1 0 63 4 4 0 0 64 5 5 1 1 65 5 5 1 1 66 5 5 1 1 67 5 5 1 1 68 5 4 1 1 69 5 4 1 1 70 5 4 1 1 71 4 4 1 1 72 4 4 1 1 73 4 4 1 1 74 5 3 1 1 75 4 3 1 1 76 5 3 1 1 77 4 4 1 1 78 4 4 1 1 79 4 4 1 1 80 4 5 1 1 81 4 5 1 1 82 4 5 1 0 83 5 5 1 1 84 4 4 1 1 85 4 4 1 1 86 4 4 1 1 87 4 5 1 1 88 4 5 1 1 89 4 5 1 1 90 3 5 0 1 91 4 5 1 1 92 4 4 1 1 93 4 4 1 1 94 5 4 1 1 95 4 4 1 1 96 4 4 1 1 97 4 4 1 0 98 3 4 0 1 99 3 4 0 1 100 3 4 0 1 101 3 4 0 1 102 4 3 1 1 103 4 4 1 1 104 4 4 1 1 105 4 4 1 0 106 4 5 1 1 107 4 5 1 1 108 4 5 1 1 109 5 5 1 1 110 4 5 1 1 111 4 4 1 1 112 4 4 1 1 113 5 4 1 1 114 4 4 1 1 115 4 4 1 1 116 4 4 1 1 117 5 5 1 1 118 5 5 1 1 119 5 5 1 0 120 5 5 1 1 121 5 5 1 1 122 5 4 1 1 123 5 4 1 1 124 5 4 1 1 125 5 4 1 1 126 5 4 1 1 127 5 4 1 1 128 4 4 1 1 129 4 4 1 1 130 4 4 1 0 131 4 4 1 1 132 4 5 1 1 133 4 5 1 1 134 4 5 1 1 135 4 5 1 1 136 4 5 1 1 137 5 4 1 1 138 5 4 1 1 139 5 4 1 1 140 5 3 1 1 141 4 4 1 1 142 4 4 1 1 143 4 4 1 1 144 4 4 1 1 145 4 4 1 0 146 4 4 1 1 147 4 3 1 1 148 4 3 1 1 149 4 3 1 1 150 3 4 0 1 151 4 4 1 1 152 4 4 1 1 153 4 4 1 1 154 3 4 0 1 155 4 4 1 1 156 4 5 1 1 157 4 5 1 1 158 4 5 1 1 159 5 5 1 1 160 5 5 1 1 161 5 5 1 0 162 5 5 1 0 163 5 5 1 1 164 5 5 1 1 165 4 5 1 1 166 4 5 1 1 167 4 5 1 0 168 4 5 1 0 169 4 5 1 1 170 4 4 1 1 171 5 4 1 1 172 5 4 1 1 173 5 4 1 1 174 5 4 1 1 175 4 4 1 1 176 4 5 1 1 177 4 5 1 1 178 4 5 1 1 179 4 5 1 1 180 4 5 1 1 181 5 5 1 1 182 5 5 1 1 183 5 5 1 1 184 5 5 1 1 185 4 5 1 1 186 4 5 1 1 187 4 5 1 1 188 4 4 1 1 189 3 4 0 1 190 4 4 1 1 191 4 4 1 1 192 4 4 1 1 193 4 4 1 1 194 5 4 1 1 195 5 4 1 1 196 5 4 1 0 197 4 5 1 1 198 4 4 1 1 199 4 4 1 1 200 3 4 0 1

## Result of the Regression Analysis

The following table presents the results of the multiple regression analysis using a simple spreadsheet software application with regression capability – Gnumeric. The first part shows the general relationship between the dependent and independent variables. The second part shows the details of the relationship between satisfaction score and pizza taste, service speed, and waiter courtesy.

 Part 1. Regression Statistics Multiple R 0.66 R^2 0.44 Standard Error 0.47 Adjusted R^2 0.43 Observations 200 Part 2. Details Coefficients Standard Error t-Statistics p-Value Intercept 2.9192 0.2691 10.8486 0.0000 Taste 0.0326 0.0526 0.6208 0.5355 Speed 1.3245 0.1076 12.3100 0.0000 Courtesy −0.0161 0.1098 −0.1469 0.8834

Notice that the overall relationship has R values. Among these R values, the most important for interpretation is the Adjusted R^2 value. This value represents the relationship between variables of the study. The value obtained here is 0.43. This means 43% of the variation in satisfaction score is accounted for by the three variables.

Closer scrutiny of the details in Part 2 reveals that service speed significantly relates to satisfaction score. The red font indicates this significant relationship (for better understanding, please read the post on how to determine the significance of statistical relationships).

## Interpretation of the Results

Based on the results of the statistical analysis, we can say with confidence that among the variables studied, service speed relates significantly to customer satisfaction. If you look closely at the entries in the data set, for every 5 or 4 satisfaction score, a 1 corresponds to service speed, meaning, the customer is satisfied with service speed. Take note, however, that this interpretation holds true only to the particular location where the study transpired.

Given this result, the marketing manager, therefore, should focus on the improvement of service speed to satisfy customers. This simple information can help the pizza business grow and gain a competitive edge. Market analysis guides decision-making and avoids incurring the unnecessary cost associated with the hit-and-miss approach.