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Strategic Market Research by Dr. Anne E. Beall Prepared by Matthew A. Gilbert, MBA Chapter 7: Analyzing Numeric Data to De...
Hypothesis-Driven Analyses <ul><li>Testing hypotheses works better than just expecting data to magically provide an answer...
Predicting Perceptions & Behavior <ul><li>Only way to accurately identify if something has an effect is to use  experiment...
Predicting Perceptions & Behavior <ul><li>Example: You can measure the effect of color on the attractiveness of a specific...
The “Smile” Experiment <ul><li>Same photos of people created with “regular” and “beautiful” smiles. Respondents saw only o...
The “Smile” Experiment
Correlation Analysis <ul><li>In cases where you cannot do an experiment, you can learn which variables are related by usin...
Correlation Analysis <ul><li>Low correlations indicate that the two things are not related to one another and that one var...
Determining Major Segments <ul><li>Another major question organizations want to answer is what the major segments in a giv...
Segmentation <ul><li>Segmentations should be customized for a specific company’s product or service. </li></ul><ul><li>Unl...
Segmentation <ul><li>Example: Cell Phone Segmentation  </li></ul><ul><ul><li>Client wants to identify people who use a lot...
Segmentation <ul><li>Example: Cell Phone Segmentation (Continued) </li></ul><ul><ul><li>Might wind up with a segmentation ...
Segmentation <ul><li>Example: Cell Phone Segmentation (Continued) </li></ul><ul><ul><li>Then profile the groups to determi...
Segmentation <ul><li>Example: Cell Phone Segmentation (Continued) </li></ul><ul><ul><li>Company now has clear direction on...
Segmentation <ul><li>Regional Bell Operating Companies (RBOC) </li></ul><ul><ul><li>Local clients who wanted to enter long...
Segmentation <ul><li>Regional Bell Operating Companies (RBOC) </li></ul><ul><ul><li>Problem: Any segmentation had to use d...
Segmentation <ul><li>Overlapping Segmentation </li></ul><ul><ul><li>When a segment needs to serve different purposes for t...
Determining the Best Configuration <ul><li>Question: How can organizations identify what the best configuration is for the...
Determining the Best Configuration <ul><li>LCD Display (Attribute) </li></ul><ul><ul><li>Large (Level 1) </li></ul></ul><u...
Determining the Best Configuration <ul><li>CD Player </li></ul><ul><ul><li>Present </li></ul></ul><ul><ul><li>Not Present ...
Determining the Best Configuration <ul><li>Then show different configurations to respondents and ask them how likely they ...
Other Analyses <ul><li>How related are two variables? </li></ul><ul><li>Which set of things is most related to a variable?...
Other Analyses <ul><li>How related are two variables? </li></ul><ul><ul><li>Correlation </li></ul></ul><ul><li>Which set o...
Other Analyses <ul><li>Do specific groups differ in a set of attitudes or group of behaviors? </li></ul><ul><ul><li>Mutliv...
Other Analyses <ul><li>What are the natural groups in the market? </li></ul><ul><ul><li>Cluster Analysis </li></ul></ul><u...
Other Analyses
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Strategic Market Research (Chapter 7): Analyzing Numeric Data to Determine What Drives Markets

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What determines whether market research makes a difference for an organization? The difference is the approach. Strategic market research is an approach that makes a large impact on the companies that use it. In Strategic Market Research, author Anne Beall shares her unique approach for conducting market research. In addition to talking about qualitative as well as quantitative research, Strategic Market Research provides real-life examples of how these concepts have been applied in businesses and non-profit organizations. Implementing the strategic approach from the beginning to the end of a project provides information that inspires and changes organizations.

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Transcript of "Strategic Market Research (Chapter 7): Analyzing Numeric Data to Determine What Drives Markets"

  1. 1. Strategic Market Research by Dr. Anne E. Beall Prepared by Matthew A. Gilbert, MBA Chapter 7: Analyzing Numeric Data to Determine What Drives Markets
  2. 2. Hypothesis-Driven Analyses <ul><li>Testing hypotheses works better than just expecting data to magically provide an answer. </li></ul><ul><li>Process </li></ul><ul><ul><li>Begin with hypotheses. </li></ul></ul><ul><ul><li>Create a set of cross-tabulations that test the assertions. </li></ul></ul><ul><ul><li>Divide the data by the number of categories. </li></ul></ul><ul><ul><li>Review data for each of your groups. </li></ul></ul><ul><ul><li>See if hypothesis are supported. </li></ul></ul>
  3. 3. Predicting Perceptions & Behavior <ul><li>Only way to accurately identify if something has an effect is to use experimental design. </li></ul><ul><ul><li>Experimental Design: Takes an independent variable and manipulates it in some way and then measures the effect of this manipulation on a dependent variable . </li></ul></ul><ul><ul><li>When the only thing that changes is the independent variable, any effect on a dependent variable is due to that independent variable. </li></ul></ul>
  4. 4. Predicting Perceptions & Behavior <ul><li>Example: You can measure the effect of color on the attractiveness of a specific car by manipulating only the color of a car and measuring perceptions of the attractiveness to see if the color has an effect. </li></ul><ul><ul><li>You might learn that color has a large effect on perceptions of a VW Bug and that the car is more attractive when it is shown in unusual colors. </li></ul></ul>
  5. 5. The “Smile” Experiment <ul><li>Same photos of people created with “regular” and “beautiful” smiles. Respondents saw only one. </li></ul><ul><li>Respondents rated attractiveness, intelligence, happiness, career success, friendliness, kindness, wealth, popularity with opposite sex, sensitivity. </li></ul><ul><li>Results: When people have a beautiful smile with white, straight teeth, people perceive them as more attractive, intelligent, happy, successful, friendly, interesting, kind, wealthy, popular and sensitive. </li></ul>
  6. 6. The “Smile” Experiment
  7. 7. Correlation Analysis <ul><li>In cases where you cannot do an experiment, you can learn which variables are related by using a correlation analysis . </li></ul><ul><li>You can determine if gender, age, or income are highly correlated with purchasing a new TV by correlating these variables with actual purchases or respondents’ stated intention to purchase the TV. </li></ul>
  8. 8. Correlation Analysis <ul><li>Low correlations indicate that the two things are not related to one another and that one variable does not vary with the other. </li></ul><ul><li>High correlations indicate the opposite. </li></ul><ul><li>Important: Correlations do not indicate causation. </li></ul><ul><ul><li>Just because two things are highly correlated does not mean that one causes the other. </li></ul></ul><ul><ul><li>Having a high income does not cause a person to be interested in buying a new television. </li></ul></ul>
  9. 9. Determining Major Segments <ul><li>Another major question organizations want to answer is what the major segments in a given market are. </li></ul><ul><ul><li>Organizations want to understand which specific groups are most likely to purchase their products and why. </li></ul></ul><ul><ul><li>They want to learn how large these groups are, what they are like demographically, and how to communicate with them effectively. </li></ul></ul><ul><ul><li>Segmentations are a way of describing a market as well as a way of providing direction for an organization’s marketing efforts. </li></ul></ul>
  10. 10. Segmentation <ul><li>Segmentations should be customized for a specific company’s product or service. </li></ul><ul><li>Unless a segmentation is based on data collected for that specific category, it won’t be very useful. </li></ul><ul><li>Example: If you want to know about the segments in the dog food market, conduct a segmentation study specifically about dog food and the major brands. </li></ul><ul><ul><li>Don’t buy secondary segmentation that was designed for all businesses and expect to figure out the segments of dog-food buyers. </li></ul></ul>
  11. 11. Segmentation <ul><li>Example: Cell Phone Segmentation </li></ul><ul><ul><li>Client wants to identify people who use a lot of cellular service and who are most likely to purchase their brand. </li></ul></ul><ul><ul><li>Wants to offer certain packages to these individuals. </li></ul></ul><ul><ul><li>Conduct an analysis that determines if age is highly related to cell phone usage and that younger consumers use cell phone service the most. </li></ul></ul><ul><ul><li>Might determine that what predicts brand usage among young users is the cell phone service their parents use. </li></ul></ul><ul><ul><li>Predictor of brand usage among older users might be the brands their friends are using. </li></ul></ul>
  12. 12. Segmentation <ul><li>Example: Cell Phone Segmentation (Continued) </li></ul><ul><ul><li>Might wind up with a segmentation as follows: </li></ul></ul><ul><ul><ul><li>1: Under 25 whose parents use Carrier A </li></ul></ul></ul><ul><ul><ul><li>2: Under 25 whose parents use other carriers </li></ul></ul></ul><ul><ul><ul><li>3: 30 to 45 whose friends use Carrier A </li></ul></ul></ul><ul><ul><ul><li>4: 30 to 45 whose friends use other carriers </li></ul></ul></ul><ul><ul><ul><li>5: 40 to 55 </li></ul></ul></ul><ul><ul><ul><li>6: 51 to 64 </li></ul></ul></ul><ul><ul><ul><li>65+ </li></ul></ul></ul>
  13. 13. Segmentation <ul><li>Example: Cell Phone Segmentation (Continued) </li></ul><ul><ul><li>Then profile the groups to determine if their behavior and attitudes are what we predicted </li></ul></ul><ul><ul><li>Assume that predictions are borne out in data and that Segment 1 had the highest usage and greatest loyalty toward Carrier A. </li></ul></ul><ul><ul><li>Segment 1 is followed by Segment 3. </li></ul></ul>
  14. 14. Segmentation <ul><li>Example: Cell Phone Segmentation (Continued) </li></ul><ul><ul><li>Company now has clear direction on which segments are most valuable to them and which ones they should target. </li></ul></ul><ul><ul><li>Additional profiling might reveal greater details to help design products and services targeted segment wants. </li></ul></ul>
  15. 15. Segmentation <ul><li>Regional Bell Operating Companies (RBOC) </li></ul><ul><ul><li>Local clients who wanted to enter long distance market. </li></ul></ul><ul><ul><li>Deregulation was in infancy and there were special rules about data usage: local phone companies not allowed to use their own data to target customers for long distance. </li></ul></ul>
  16. 16. Segmentation <ul><li>Regional Bell Operating Companies (RBOC) </li></ul><ul><ul><li>Problem: Any segmentation had to use data that could be purchased from external data vendors. Clients purchased large databases of information for a geographic area and assign every household in the database to a segment. </li></ul></ul><ul><ul><li>Solution: Created an algorithm to assign everyone in the database to a specific segment. After all households were assigned to a segment, clients would contact people with an offer for long distance service. Thinking through how segments would be used and what data constraints there were for each was very important. </li></ul></ul>
  17. 17. Segmentation <ul><li>Overlapping Segmentation </li></ul><ul><ul><li>When a segment needs to serve different purposes for two different groups in an organization. </li></ul></ul><ul><ul><li>Present segments in a way to helps to visualize where and how they overlap. (See Figure 2 on Page 61). </li></ul></ul>
  18. 18. Determining the Best Configuration <ul><li>Question: How can organizations identify what the best configuration is for their product or service? </li></ul><ul><li>Answer: Use conjoint or discriminant analysis to identify the best configurations for a certain group of people or for a market overall. </li></ul><ul><ul><li>Benefit: Enable us to figure out what the potential demand is for a large number of product configurations. </li></ul></ul><ul><ul><li>Discriminant: Enables you to control some configurations. </li></ul></ul><ul><ul><li>Conjoint: Doesn’t enable you to control configurations. </li></ul></ul>
  19. 19. Determining the Best Configuration <ul><li>LCD Display (Attribute) </li></ul><ul><ul><li>Large (Level 1) </li></ul></ul><ul><ul><li>Small (Level 2) </li></ul></ul><ul><li>Alarm </li></ul><ul><ul><li>Music </li></ul></ul><ul><ul><li>Music or Loud Sound </li></ul></ul><ul><ul><li>Music, Loud Sound or Bright Light </li></ul></ul>
  20. 20. Determining the Best Configuration <ul><li>CD Player </li></ul><ul><ul><li>Present </li></ul></ul><ul><ul><li>Not Present </li></ul></ul><ul><li>Price </li></ul><ul><ul><li>$35 </li></ul></ul><ul><ul><li>$30 </li></ul></ul><ul><ul><li>$25 </li></ul></ul>
  21. 21. Determining the Best Configuration <ul><li>Then show different configurations to respondents and ask them how likely they are to buy each one. </li></ul><ul><li>Create a “Simulator” which represents results in a tabular format (See Table 2 on Page 63) </li></ul>
  22. 22. Other Analyses <ul><li>How related are two variables? </li></ul><ul><li>Which set of things is most related to a variable? </li></ul><ul><li>Are there significant differences between groups? </li></ul><ul><li>Do specific groups differ in a set of attitudes or group of behaviors? </li></ul><ul><li>Are preferences for products or services similar or different than what would be expected by chance? </li></ul><ul><li>What is the underlying structure of a set of attitudes? </li></ul><ul><li>What are the natural groups in the market? </li></ul><ul><li>What is the optimal configuration for a product or service? </li></ul>
  23. 23. Other Analyses <ul><li>How related are two variables? </li></ul><ul><ul><li>Correlation </li></ul></ul><ul><li>Which set of things is most related to a variable? </li></ul><ul><ul><li>Regression Analysis </li></ul></ul><ul><li>Are there significant differences between groups? </li></ul><ul><ul><li>t-Test, Analysis of Variance (ANOVA, f-test) </li></ul></ul>
  24. 24. Other Analyses <ul><li>Do specific groups differ in a set of attitudes or group of behaviors? </li></ul><ul><ul><li>Mutlivarate Analysis of Variance </li></ul></ul><ul><li>Are preferences for products or services similar or different than what would be expected by chance? </li></ul><ul><ul><li>Chi-Squared Analysis </li></ul></ul><ul><li>What is the underlying structure of a set of attitudes? </li></ul><ul><ul><li>Factor Analysis </li></ul></ul>
  25. 25. Other Analyses <ul><li>What are the natural groups in the market? </li></ul><ul><ul><li>Cluster Analysis </li></ul></ul><ul><li>What is the optimal configuration for a product or service? </li></ul><ul><ul><li>Conjoint of Discriminant Analysis </li></ul></ul>
  26. 26. Other Analyses
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