Coors Light Essay

Executive Summary – Coors’ prominence in the beer industry has always been overshadowed by its bigger competitors like Budweiser, Miller and Molson, but new insights unearthed by this report may pave new roads for a more exciting future. The first part of our analysis describes the typical Coors drinker as an aged 25 to 44 male light beer drinker consuming almost seven bottles a week. He also works in a managerial or professional occupation earning over $30,000 annually. Coors’ three competitors also exhibit a similar consumer base with the exception of Molson being predominantly regular beer consumers.These conclusions are tested to be statistically significant. The second part of our report tests for associations.

In the end we have found that males prefer regular beer while females prefer light beer. Younger people also showed increased interest in light beers, and are more likely to be a brand switcher rather than be loyal to a brand. Coors has the largest percentage of people working in managerial or professional occupations while people who categorized their jobs as others mostly described themselves as non-loyalty drinkers.The brand switching matrix analysis confirms the previous research on competitors. We lose the most consumers to Budweiser, Miller and Molson, but we also have a potential to gain new brand loyalists from those same three companies. This analysis also uncovers the strength of Coors’ potential market expansion by showing a brand strength ratio of 2.

083. Moreover, we find that Blatz is most vulnerable to Coors in terms of the percentage of loyal consumers switching to Coors. The fourth division of the research attempts to better understand consumer lifestyles through factor analysis.We find that Coors has a relatively neutral stance in terms of innovation and risk-aversion, but has a higher score in terms of machismo. We identify that brands with the closest positioning are Busch, Michelob and Blatz. Of the three big competitors, Budweiser has the nearest profiling to Coors.

In the final part of our analyses, we run a couple regressions to understand beer consumption quantities and light beer preference. Our results conclude that consumption is negatively related to age and income. Males and skill laborers also tend to consume more drinks per week.As for light beer, preference is significantly correlated to females, young adults and individuals who like to drink at social events.

We also find that Coors potential new customers are more or less similar in demographics to the existing consumer base. Our recommendations arising from our research comes in two parts: consumer retention and market expansion. We believe that strategic product positioning in the retail scene will help mitigate customer losses while at the same time interest new consumers. We also think that skilled labor workers who are loyal to Coors should be specially targeted due to their higher consumption.If Coors’ management decides to expand its light beer market share, its marketing campaign should possess flair of youth and social sophistication without neglecting the female market.

Finally, because of similar consumer bases, we advise Coors to engage in sports marketing both in the retention and market expansion strategies. 1) Consumer Profiles and Segmentations – The characteristics of a typical Coors loyal drinker is a male who has a stronger preference with 68. 5% in favour of light beer versus 31. 5% regular drinkers (Exhibit 2) with average household income $50,000 and above.Also the most drinkers consume beer in social occasions and while watching sports (Exhibit 3). Moreover, we have also conducted a t-test which shows the data analyzed for Occasions of beer consumption to be significant as all the t-values are greater than 2.

Thus, we would be able to conclude confidently that both data graphs for social occasions and sport event drinkers to be a bimodal distribution where it segregates the sample into two groups of users: heavy consumption and light consumption (Exhibit 4).Inversely, this describes the positive skewness in tracking the frequency of Coors consumption (Exhibit 2), as mean number of drinks consumed is 6. 82 while the median is 6 and hence identifies the effects of outliers due to a few consumers with extremely high consumption. Furthermore, data also provides us with consumer information on major competitors as indicated to be Budweiser, Miller and Molson (Exhibit 5) which is consistent with the findings on brand loyalty showing those three brands to dominate the industry in the beer market (Exhibit 1).Therefore, three substitute brands may have a potential overlap with the market audience of Coors. This can be demonstrated in observing substitution effects of major competitor Budweiser as their users prefer Miller, Michelob and Coors when the Budweiser product is unavailable (Exhibit 6). Additionally, Coors’ second leading competitor Miller substitution rates show users willing to shift to Budweiser, Coors, and Michelob when the Miller brand is not offered (Exhibit 7).

Finally, another major Molson ubstitution data depicts users moving to Samuel Adams, Heineken, Coors and Budweiser when Molson is unattainable (Exhibit 8). In general, it is observed that Coors and its competitors have an overwhelming substitution effect where beer consumers switch interchangeably between several brands, suggesting brands may have similar consumers associations. As compared to the Coors loyalty customers, Miller, Budweiser and Molson portrayed similar customer characteristics (Exhibit 9. ) For example, the loyalty customers for all three competitor brands were male with age from 25-44 and had managerial or professional career.

These customers drank on average 8 bottles of beer per week and preferred light beer with the exception of Molson customers who favoured regular beer, which is proved to be significant. The hypothesis testing was conducted to test significance for consumption frequency, average income, age, gender distribution, and beer preference. The results in Exhibit 10 show that all the t-values are very small indicating that the null cannot be rejected with the exception for beer preference of Molson.

This statistically proves that there are mostly no significant differences between Coors’ customers and its close competitors’ customers. ) Association Tests – X^2 test was done to examine the association between different pairs of variables through cross tabulation. Chi-square was calculated to determine whether or not it is worthwhile to interpret the tables. Chi-square is only a test of significance, and not a measure of a relationship between variables. Given the large number of samples (N), the corresponding chi-square values were large which show that the tests were significant. The second chi-square value was not large enough but the specified level of significance value was 0.

04 which was less than 5%.Therefore, we can conclude that all four tests were significant to interpret the results. (See Appendix, Exhibit 11-14) Firstly, the total of 1392 male and female data was used to test the relationship of gender with the preference of beer type. Generally, the majority of the male population preferred regular beer and the female population favored light beer. A few percentage of people responded that they have no preference in the type of beer.

This indicates that the majority of the population had clear preferences in the type of beer that they consume.Secondly, a sample size of 1393 was examined to discover the association between ages versus preference of beer type. The age groups 25 to 44 and 45 and above all preferred regular beer over light beer. However people below 25 years old slightly favored light beer more than regular beer. This could be due to the fact that the respondents were in their early age of drinking.

Similarly, the total sample size of 1418 was categorized into three different age groups and their loyal beer brand. The results presented that the largest portion of population from all three sample groups chose Budweiser as their loyalty brand.Among 11 different beer brands, 25. 2% of customers were Budweiser loyal drinkers.

After Budweiser, Miller had the second largest group of loyal customers, followed by Molson. Many of the younger respondents answered that they were not loyal to a particular brand suggesting that age positively affects the likelihood of being loyal to a particular brand. Lastly, a test association between different occupations versus loyal drinkers was conducted. Budweiser was picked by the largest number of people from all different kinds of occupations. Coors had the largest percentage of people coming from managerial or professional occupations.The people who categorized their jobs as others mostly described themselves as non-loyalty drinkers. 3) Brand Switching Matrix – The brand switching matrix analysis was performed using valid data on substitution intentions. More specifically, only entries containing answers 1-12 were used and we assumed that their substitution intention would hold true; a total of 1128 samples were used.

Under these circumstances, the resulting brand switching matrix is shown in Exhibit 15a. As identified in earlier questions, we see that our major competitors are Miller, Budweiser, and Molson. We lose 20%, 28. %, and 13.

3% of our existing customer base to those competitors respectively. When looking at the substitution column of Coors, we immediately see a huge potential for the beer company’s growth. Not surprisingly, we see that Coors has the biggest potential to gain new customers from Miller, Budweiser, and Molson. These brands make up 24%, 30. 4%, and 10. 4% respectively of those indicating a switching intention to Coors. This affirms our assertion that Budweiser is our biggest competitor.

We lose the most customers, but at the same time we have the biggest chance to gain new patrons from Budweiser.Blatz is also significant because 22. 4% of Blatz’s total loyal consumer base show an intention in switching over to Coors.

Therefore, Blatz is most vulnerable in terms of loyal consumer base percentage. The strength of Coors’ growth prospects becomes evident when we examine the strength ratios of each brand, disclosed in Exhibit 15b. Strength ratios were calculated by dividing the number of volunteers switching to the brand by the number of participants leaving the brand. Thus all values above 1 indicate a healthy growth, and all values less than one suggest a decline in consumer base.

Our analysis shows that Coors has the highest strength ratio of all the examined brands with a quotient of 2. 083. The next largest ratio is only 1. 470 from Michelob.

Our competitors Miller, Budweiser and Molson have ratios 1. 006, . 0786, and 1. 041 respectively indicating that their consumer bases are either not growing or shrinking. This is positive news for Coors, signifying a significant possibility of increased market share. 4) Factor Analysis and Lifestyle – A factor analysis is performed to better understand the characteristics of our competitors’ as well as our own consumer bases.Only valid data to the 15 Likert questions on the survey are used.

We found that there are four significant latent factors determining the results of the Likert questions as shown in Exhibit 16a. Together, they explain 47. 85% of the observed variance. However, we found that limiting factors to three was better suited for our purposes for the following reasons. Firstly, even with suppression value of 0.

4, there was still overlapping in the rotated component matrix with four factors. Secondly, we believe a three factor analysis would be easier to interpret. As shown in Exhibit 16b, the three factor analysis still explains 40. 6% of total observed variance, indicating the analysis is still strong enough for interpretation. The associated component matrix is displayed in Exhibit 17. From this matrix, we conclude that questions 2, 14, 7, 5, 3, 9, 10, and 4 are associated with factor one, questions 13, 15, 1, and 1 are associated with a second factor; questions 6, 12 and 8 are associated with the last factor. While giving more weight to higher component scores, we determined the first factor describes a person who is an active and reliable leader and an outgoing social explorer.

Therefore, we named factor one as innovation.Meanwhile factor two statements describe a stable, secure, and realistic person. Thus, we named factor two as risk-aversion. Since the statements assigned for factor three describe male characteristics, we named the factor machismo. The mean factor scores are then cross tabulated with brand loyalty in Exhibit 18.

We find that Coors’ loyal consumers are relatively neutral in the factors innovation and risk-aversion. The beer company’s machismo factor score may not seem very strong either but, compared to other brands, it is ranked third highest behind Pabst and Budweiser.The factor scores reveal more information when used to produce profiling maps of innovation versus machismo and risk-aversion as presented in Exhibits 19 and 20. We see the consumers of Busch, Blatz, and Michelob consistently stand near Coors’ consumer position for both maps. Ironically, Miller and Molson both place relatively far from Coors. Although not as close as Busch, Blatz and Michelob, our analysis concludes that Budweiser has the most similar consumer base to Coors out of the three major competitors.

This conclusion further asserts Budweiser as Coors biggest competitor.Finally, being the most remote of the brands, Pabst appears to have a very different customer base. 5a) Optional Analysis: Regression on Consumption – To better understand consumption behavior, a linear regression analysis is preformed to test how beer preference, age, and gender affected weekly beer consumption. Income is not included in the regression because of multicollinearity. There is a positive correlation of 0. 46 between income and age since older people tend to have higher incomes. When at least two of the independent variables are correlated in the analysis, it may not give valid results about any individual predictor.

Beer preference is recoded into a dummy variable where all entries that indicated no preferences for light or regular beer are not used. We do this without significantly altering the results since very few people indicated no preference. The results shown in Exhibit 21a are derived from entries with valid data. While light beer preference does not play an important role, we see that gender and age are significant factors of consumption quantity. More specifically, males drink more than female by an average of 1.

635 bottles a week and younger people tend to drink more beer.Since age and income are highly positively correlated, we expect that higher income individuals drink less beer. Our hypothesis is confirmed when running the regression with income instead of age as shown in Exhibit 21b. We then decide to test if occupation plays a role in determining beer consumption patterns. To properly run the regression, four different tests are done with each test corresponding to the occupations managerial or professional, technical, sales or administrative, skilled labor and self-employed.

A dummy variable is setup that assigns 0 to the occupation in question and 1 to the all others.We also used income as one of the independent variable to control for the fact that different occupations earn different amounts of income. The results of the four regressions are shown in Exhibits 21c – f. Interestingly, none of the career categories showed any significant evidence of affecting beer consumption except for skilled labor, which exhibited a t-value of -3. 878 and a coefficient of -1. 517. Thus, our results illustrate that the skill labor population consumes on average 1.

517 more bottles of beer than their peers. b) Optional Analysis: Regression on Light Beer Preference – A regression is also performed to understand light beer preference. Using the same dummy variable for beer preference as before, the first regression is run with the independent variables gender, age, income.

The results shown in Exhibit 21g signify that females and younger people are more likely to drink light beer. We then include additional independent variables measuring beer consumption frequency at social events and accompanying meals. Separate regressions are done with the additional variables to avoid multicollinearity.The outcomes, shown in Exhibits 21h and 21i, indicate that people who usually drink beer at social events are more likely to drink light beer, while those who drink beer to accompany meals will usually chose regular beer. 5c) Optional Analysis: Descriptive Statistics on Potential Consumers – Part 3 of this report explores the number of volunteers with the intention of switching to Coors.

We then decide to further this analysis by probing the characteristics of these potential customers. Not surprisingly, our results show that the potential consumers are very identical to the existing consumer base of Coors.A comparative table is shown in Exhibit 22. Using a hypothesis test, we conclude that there is not enough evidence to prove that there is a significant difference for many of the attributes. However, the difference in the number of people working as skilled labor is tested to be significant. 6a) Recommendations: Consumer Retention – There are numerous ways in which Coors can maintain and manage its existing consumer base.

Firstly, our brand switching matrix show that we lose consumers mostly to Budweiser, Miller and Molson, thus strategic product placement in the retail scene may mitigate this problem.More specifically, Coors should strive to get its products placed in front of its competitors so that loyal customers are less likely to switch brands. Secondly, since individuals working in skilled labor drink more beer, and thus are more profitable, Coors should attempt to identify skilled laborers within its customer base and offer incentives for his/her loyalty to Coors.

Additionally, our consumers are predominantly male who like sports. Therefore, Coors should increase its marketing efforts within the sports arena. Sports prizes and incentives can be given to maintain the beer company’s loyal consumer base.Coors management should be reminded that it is a brand with more light beer drinkers, so the marketing efforts for different beer products should be weighted accordingly. Moreover, Coors can use its predominantly light beer image to differentiate them from Molson, whose consumer base drink more regular beer. Through light beer image campaigns, Coors can appeal to the population below 25 years old as younger generation preferred light beer. Coors management can thus, subtly remind its consumers who are about to switch to Molson that Coors is more famous for its light beer.

b) Recommendations: Market Expansion – There is plenty of room for Coors to expand as is evident from its impressive brand strength ratio. Therefore, we advise that increasing marketing funding will be justified. Much of our potential growth will come from Budweiser, Miller and Molson, thus furthering the reason for the product placement strategy in the retail scene. Not only will we prevent our existing consumers from leaving, but we will also have an opportunity to gain new ones. It is especially important that we target Budweiser over the other competitors because of feasibility.The positioning map shows that Budweiser is closest to Coors out of the three major competitors, thus many of the listed retention strategies will also work as market expansion strategies.

An example would be the sports marketing since Budweiser consumers score high on the machismo factor. Additionally, Busch, Blatz and Michelob also have loyal consumers with similar lifestyle traits to Coors’ loyal patrons. Finally, when increasing market share for the light beer sector, Coors management should use advertising that convey the message of youth, socialization, and a hint of female appeal.Appendix Exhibit 1. Consumer Loyalty |Brands |Frequency |Percent |Cumulative Percent | |Miller |173 |12.

2 |12. 2 | |Budweiser |358 |25. 2 |37. 4 | |Pabst |29 |2.

0 |39. 5 | |Blatz |54 |3. 8 |43. 3 | |Milwaukee |42 |3. 0 |46. 3 | |Molson |110 |7. 8 |54. | |Coors |73 |5.

1 |59. 2 | |Busch |49 |3. 5 |62. 6 | |Heineken |90 |6. 3 |69. 0 | |Michelob |96 |6. 8 |75.

7 | |Samuel Adams |90 |6. 3 |82. 1 | |Other |254 |17.

9 |100. 0 | |Total |1418 |100. 0 | | Exhibit 2. Coors Loyal Drinkers’ Characteristics ) Number of Beers Consumed in an Week b) Preference of Beer Type |Number of Drinks|Percent |Cumulative Percent | |6 |8. 2 |8.

2 | |3 |4. 1 |12. 3 | |2 |2. 7 |15.

1 | |7 |9. 6 |24. 7 | |8 |11. 0 |35. 6 | |14 |19. 2 |54. 8 | |2 |2. 7 |57.

5 | |12 |16. 4 |74. 0 | 3 |4. 1 |78. 1 | |9 |12. 3 |90. 4 | |1 |1.

4 |91. 8 | |3 |4. 1 |95. 9 | |1 |1. 4 |97. 3 | |1 |1.

4 |98. 6 | |1 |1. 4 |100. 0 | |73 |100. 0 | | | |Frequency |Percent |Cumulative Percent | |Regular |23 |31.

|31. 5 | |Light |50 |68. 5 |100. 0 | |Total |73 |100. 0 | | c) Gender | |Frequency |Percent |Cumulative Percent | | |Male |48 |65. 8 |65. 8 | | |Female |25 |34. 2 |100.

0 | | |Total |73 |100. 0 | | d) Average Household Income |Frequency |Percent |Cumulative Percent | |$50,000 and above |28 |38. 4 |38. 4 | | |$30,000-$49,999 |25 |34. 2 |72. 6 | | |Below |20 |27. 4 |100. 0 | | |$30,0000 | | | | | |Total |73 |100.

0 | | |Mean |6. 82 | Median |6. 00 | |Minimum |0 | |Maximum |24 | e) For average age and occupation of Coors’ loyal drinkers, refer to Exhibit 12 and 13. Exhibit 3.

Occasions for drinking beer Occasion for consumption: a) At social events |% |Frequency |Percent |Cumulative Percent| |0% |1 |1. 4 |1. 4 | |5% |1 |1. 4 |2. 7 | |10% |5 |6.

|9. 6 | |15% |3 |4. 1 |13. 7 | |20% |5 |6. 8 |20.

5 | |25% |2 |2. 7 |23. 3 | |30% |10 |13. 7 |37. 0 | |40% |7 |9. 6 |46. 6 | |50% |11 |15. 1 |61.

6 | |52% |1 |1. 4 |63. 0 | |60% |5 |6.

8 |69. | |65% |2 |2. 7 |72. 6 | |70% |1 |1.

4 |74. 0 | |75% |8 |11. 0 |84. 9 | |80% |6 |8. 2 |93. 2 | |90% |2 |2. 7 |95.

9 | |92% |1 |1. 4 |97. 3 | |100% |2 |2. 7 |100. 0 | |Total |73 |100. 0 | | ) While watching sport events |% |Frequency |Percent |Cumulative Percent | |0% |14 |19. 2 |19.

2 | |2% |1 |1. 4 |20. 5 | |3% |1 |1. 4 |21. 9 | |5% |3 |4. 1 |26. 0 | |10% |14 |19. 2 |45.

2 | |20% |16 |21. 9 |67. 1 | |25% |6 |8.

|75. 3 | |30% |5 |6. 8 |82. 2 | |35% |1 |1. 4 |83.

6 | |40% |6 |8. 2 |91. 8 | |50% |3 |4. 1 |95. 9 | |60% |3 |4. 1 |100. 0 | |Total |73 |100. 0 | | c) After exercising |% |Frequency |Percent |Cumulative Percent | | 0% |61 |83.

|83. 6 | |5% |4 |5. 5 |89. 0 | |10% |3 |4. 1 |93. 2 | |15% |2 |2.

7 |95. 9 | |20% |2 |2. 7 |98. 6 | |30% |1 |1. 4 |100. 0 | | Total |73 |100.

0 | | d) To accompany meals |% |Frequency |Percent |Cumulative Percent| |0% |22 |30. |30. 1 | |1% |1 |1. 4 |31.

5 | |5% |8 |11. 0 |42. 5 | |9% |1 |1. 4 |43. 8 | |10% |13 |17. 8 |61. 6 | |15% |2 |2. 7 |64.

4 | |20% |8 |11. 0 |75. 3 | |25% |1 |1.

4 |76. 7 | |30% |2 |2. 7 |79. | |35% |1 |1.

4 |80. 8 | |40% |3 |4. 1 |84. 9 | |50% |6 |8.

2 |93. 2 | |60% |5 |6. 8 |100. 0 | |Total |73 |100. 0 | | e) While unwinding after work |% |Frequency |Percent |Cumulative Percent| |0% |29 |39. 7 |39. 7 | |2% |1 |1. |41.

1 | |5% |8 |11. 0 |52. 1 | |10% |14 |19. 2 |71. 2 | |15% |2 |2. 7 |74. 0 | |19% |1 |1.

4 |75. 3 | |20% |7 |9. 6 |84. 9 | |21% |1 |1. 4 |86. 3 | |25% |2 |2.

7 |89. 0 | |30% |5 |6. 8 |95.

| |35% |2 |2. 7 |98. 6 | |50% |1 |1. 4 |100. 0 | |Total |73 |100.

0 | | f) Others |% |Frequency |Percent |Cumulative Percent| |0% |55 |75. 3 |75. 3 | |5% |5 |6. 8 |82. 2 | |10% |6 |8. 2 |90. 4 | |15% |2 |2. 7 |93.

| |20% |1 |1. 4 |94. 5 | |25% |1 |1. 4 |95.

9 | |50% |2 |2. 7 |98. 6 | |100% |1 |1. 4 |100. 0 | |Total |73 |100. 0 | | Exhibit 4.

T-Test for Occasion for consumption: At social events One-Sample Statistics for Occasion for consumption: At social events | |N |Mean |Std.Deviation |Std. Error Mean | |At social events |73 |47. 73 |25. 817 |3. 022 | |While watching sports events |73 |18. 97 |16. 239 |1.

901 | |After exercising |73 |2. 05 |5. 580 |. 653 | |To accompany meals |73 |16. 58 |19. 30 |2. 251 | |While unwinding after work |73 |9.

75 |11. 433 |1. 338 | |Others |73 |4. 93 |14. 637 |1. 713 | | |Test Value = 0 | | |T |df |Sig. 2-tailed) |Mean Difference |95% Confidence Interval of the | | | | | | |Difference | | | | | | | |1.

Miller |12 |20. 0 |20. 0 | | |2. Budweiser |17 |28. 3 |48. 3 | | |3. Pabst |2 |3. 3 |51.

7 | | |4. Blatz |2 |3. 3 |55. 0 | | |5.Milwaukee |1 |1. 7 |56. 7 | | |6.

Molson |8 |13. 3 |70. 0 | | |8.

Busch |3 |5. 0 |75. 0 | | |9. Heineken |3 |5.

0 |80. 0 | | |10. Michelob |6 |10. 0 |90.

0 | | |11. Samuel Adams |3 |5. 0 |95. 0 | | |12.

Non-listed |3 |5. 0 |100. 0 | | |Total |60 |100. 0 | |Exhibit 6. Major Competing Brands to Budweiser |Beer Brand |Frequency |Percent |Valid Percent |Cumulative Percent | |1. Miller |78 |26. 1 |26. 1 |26.

1 | |3. Pabst |12 |4. 0 |4. 0 |30. 1 | |4. Blatz |2 |. 7 |. 7 |30.

8 | |5.Milwaukee |6 |2. 0 |2. 0 |32. 8 | |6.

Molson |20 |6. 7 |6. 7 |39. 5 | |7. Coors |38 |12. 7 |12. 7 |52. 2 | |8.

Busch |37 |12. 4 |12. 4 |64. 5 | |9. Heineken |17 |5. |5. 7 |70.

2 | |10. Michelob |55 |18. 4 |18.

4 |88. 6 | |11. Samuel Adams |18 |6. 0 |6.

0 |94. 6 | |12. Non-listed |16 |5. 4 |5. 4 |100. 0 | |Total |299 |100. 0 |100.

| | Exhibit 7. Major Competing Brands to Miller |Beer Brand |Frequency |Percent |Valid Percent |Cumulative Percent | |2. Budweiser |68 |43. 9 |43. 9 |43. 9 | |3.

Pabst |8 |5. 2 |5. 2 |49. 0 | |4. Blatz |2 |1.

3 |1. |50. 3 | |5. Milwaukee |1 |. 6 |. 6 |51. 0 | |6.

Molson |9 |5. 8 |5. 8 |56. 8 | |7.

Coors |30 |19. 4 |19. 4 |76. 1 | |8.

Busch |5 |3. 2 |3. 2 |79.

4 | |9.Heineken |6 |3. 9 |3. 9 |83.

2 | |10. Michelob |14 |9. 0 |9. 0 |92. 3 | |11.

Samuel Adams |4 |2. 6 |2. 6 |94. 8 | |12.

Non-listed |8 |5. 2 |5. 2 |100. 0 | |Total |155 |100. |100. 0 | | Exhibit 8. Major Competing Brands to Molson |Beer Brand |Frequency |Percent |Valid Percent |Cumulative Percent | |1. Miller |7 |7. 1 |7. 1 |7. 1 | |2. Budweiser |13 |13. 3 |13. 3 |20. 4 | |4. Blatz |1 |1. |1. 0 |21. 4 | |5. Milwaukee |1 |1. 0 |1. 0 |22. 4 | |7. Coors |13 |13. 3 |13. 3 |35. 7 | |8. Busch |1 |1. 0 |1. 0 |36. 7 | |9. Heineken |17 |17. 3 |17. |54. 1 | |10. Michelob |6 |6. 1 |6. 1 |60. 2 | |11. Samuel Adams |30 |30. 6 |30. 6 |90. 8 | |12. Non-listed |9 |9. 2 |9. 2 |100. 0 | |Total |98 |100. 0 |100. 0 | |Exhibit 9. Major Competing Brands’ Consumer Characteristics | |Average Consumption |Beer Type |Gender |Age |Occupation |Income | |Miller |8 beers/week |Light, 59% |Male, 58. 4% |25-44, 53% |Managerial or |$30,000-$49,999, | | | | | | |Professional, 48. 2% |36. 1% | |Budweiser |8 beers/week |Light, 59. 8% |Male, 69. 8% |25-44, 53. % |Managerial or |$30,000-$49,999, | | | | | | |Professional, 49. 9% |38. 4% | |Molson |8 |Regular, 70. 6% |Male, 68. 8% |25-44, 65. 1% |Managerial or |Below $30,000, | | | | | | |Professional, 49. 5% |38. 5% | Exhibit 10. Hypothesis Testing for Coors Loyalty Customers and Close Competitors’ Loyalty Customers a) Consumption Frequency: Beer Brand |Mean |S. D |N | |Coors |6. 82 |4. 423 |73 | |Miller |6. 47 |3. 716 |173 | |Budweiser |7. 38 |5. 907 |358 | |Molson |7. 05 |3. 46 |110 | Hypotheses Testing for Miller: Pooled Variance Estimator=173(3. 716)2 + 73(4. 423)2 = 244. 3578 (173+73-2)1/2 t = 6. 47 – 6. 82 = -0. 022406 244. 3578 (173+73-2)1/2 Hypotheses Testing for Budweiser: Pooled Variance Estimator= 358(5. 907)2 + 73(4. 423)2 = 672. 0476 (358+73-2)1/2 t = 7. 38 – 6. 82 = 0. 000040231 672. 0476 (358+73-2)1/2 Hypotheses Testing for Molson: Pooled Variance Estimator= 110(3. 746)2 + 73(4. 423)2 = 220. 8825 (110+73-2)1/2 t = 7. 05 – 6. 82 = 0. 000077 220. 825 (110+73-2)1/2 b) Average Income: |Beer Brand |Mean |S. D |N | |Coors |1. 89 |. 809 |73 | |Miller |1. 91 |. 806 |173 | |Budweiser |1. 84 |. 791 |358 | |Molson |2. 07 |. 32 |110 | Hypotheses Testing for Miller: Pooled Variance Estimator= 173(. 806)2 + 73(. 809)2 = 10. 2535 (173+73-2)1/2 t = 1. 91 – 1. 89 = 0. 000125 10. 2535 (173+73-2)1/2 Hypotheses Testing for Budweiser: Pooled Variance Estimator= 358(. 791)2 + 73(. 809)2 = 13. 1212 (358+73-2)1/2 t = 1. 84 – 1. 89 = -0. 000184 13. 1212 (358+73-2)1/2 Hypotheses Testing for Molson: Pooled Variance Estimator= 110(. 832)2 + 73(. 809)2 = 9. 2110 (110+73-2)1/2 t = 2. 07 – 1. 89 = 0. 00145 . 2110 (110+73-2)1/2 c) Age: | |Mean |S. D |N | |Coors |1. 97 |. 552 |73 | |Miller |1. 82 |. 671 |173 | |Budweiser |1. 72 |. 635 |358 | |Molson |1. 83 |. 72 |110 | Hypotheses Testing for Miller: Pooled Variance Estimator= 173(. 671)2 + 73(. 552)2 = 6. 4105 (173+73-2)1/2 t = 1. 82 – 1. 97 = -0. 001498 6. 4105 (173+73-2)1/2 Hypotheses Testing for Budweiser: Pooled Variance Estimator= 358(. 635)2 + 73(. 552)2 = 8. 0434 (358+73-2)1/2 t = 1. 72 – 1. 97 = -0. 00150 8. 0434 (358+73-2)1/2 Hypotheses Testing for Molson: Pooled Variance Estimator= 110(. 572)2 + 73(. 552)2 = 4. 3285 (110+73-2)1/2 t = 1. 83– 1. 97 = -0. 002404 4. 3285 (110+73-2)1/2 d) Gender: |Male |Female |Total | |Coors |48 |25 |73 | |Miller |101 |71 |172 | |Budweiser |251 |106 |357 | |Molson |75 |35 |110 |Hypotheses Testing for Miller: Pooled Sample Percentage = 0. 608 Sp1-p2 = 0. 0682 t = 1. 067, d. f. = 243, Not enough evidence to reject null hypothesis Hypotheses Testing for Budweiser: Pooled Sample Percentage = 0. 6953 Sp1-p2 = 0. 0591 t = 0. 67, d. f. = 428, Not enough evidence to reject null hypothesis Hypotheses Testing for Molson: Pooled Sample Percentage = 0. 608 Sp1-p2 = 0. 0682 t = 1. 067, d. f. = 181, Not enough evidence to reject null hypothesis e) Beer Preference: |Regular |Light |Total | |Coors |23 |50 |73 | |Miller |70 |98 |168 | |Budweiser |142 |212 |354 | |Molson |77 |33 |110 |Hypotheses Testing for Miller: Pooled Sample Percentage = 0. 3859 Sp1-p2 = 0. 08708 t = 1. 16, d. f. = 239, Not enough evidence to reject null hypothesis Hypotheses Testing for Budweiser: Pooled Sample Percentage = 0. 3864 Sp1-p2 = 0. 0799 t = 1. 0779, d. f. = 425, Not enough evidence to reject null hypothesis Hypotheses Testing for Molson: Pooled Sample Percentage = 0. 5464 Sp1-p2 = 0. 1159 t = 3. 321, d. f. = 181, Enough evidence to reject null hypothesis Exhibit 11. Gender vs. Preference of Beer Type Light * sex Crosstabulation | |Sex |Total | | Beer Type | |Male |Female | | | |Regular |Expected Count |475. 4 |231. 6 |707. 0 | | | |% of Total |40. 8% |10. 0% |50. 8% | | |Light |Expected Count |455. |222. 1 |678. 0 | | | |% of Total |26. 1% |22. 6% |48. 7% | | |No Preference |Expected Count |4. 7 |2. 3 |7. 0 | | | |% of Total |. 4% |. 1% |. 5% | |Total |Expected Count |936. 0 |456. 0 |1392. | | |% of Total |67. 2% |32. 8% |100. 0% | Chi-Square Tests | |Value |df |Asymp. Sig. (2-sided) | |Pearson Chi-Square |112. 904(a) |2 |. 000 | |N of Valid Cases |1392 | | | 2 cells (33. 3%) have expected count less than 5. The minimum expected count is 2. 29. Exhibit 12. Age vs. Preference of Beer Type Light * age Crosstabulation | | |Age |Total | | Beer Type | |Below 25 |25-44 |45 and above | | | |Regular |Expected Count |218. 7 |386. 2 |102. | | |% of Total |30. 9% |54. 6% |14. 4% |100. 0% | Chi-Square Tests | |Value |df |Asymp. Sig. (2-sided) | |Pearson Chi-Square |10. 046(a) |4 |. 040 | |N of Valid Cases |1393 | | | a 3 cells (33. 3%) have expected count less than 5.The minimum expected count is 1. 01. Exhibit 13. Age vs. Loyalty (Brand Loyal Drinker or Switcher) Loyal * age Crosstabulation | | |Age |Total | | Beer Brand | |Below 25 |25-44 |45 and above | | | |Miller |Expected Count |53. 7 |94. 3 |25. 0 | | |% of Total |31. % |54. 5% |14. 5% |100. 0% | Chi-Square Tests | |Value |Df |Asymp. Sig. (2-sided) | |Pearson Chi-Square |93. 161(a) |22 |. 000 | |N of Valid Cases |1418 | | | a 1 cells (2. 8%) have expected count less than 5. The minimum expected count is 4. 19. Exhibit 14.Occupation vs. Loyalty Loyal * Occup Crosstabulation | | |Occupation |Total | | Beer Brand | |Managerial or Professional |Technical, Sales or Administrative | |Pearson Chi-Square |91. 012(a) |44 |. 000 | |N of Valid Cases |1382 | | | 13 cells (21. 7%) have expected count less than 5. The minimum expected count is . 65. Exhibit 15. a) Loyal * Substitute Brand Crosstabulation | | |Miller |Budweiser | |Miller |155 |156 |1. 006 | |Budweiser |299 |235 |0. 86 | |Pabst |23 |32 |1. 391 | |Blatz |49 |19 |0. 388 | |Milwaukee |18 |21 |1. 167 | |Molson |98 |102 |1. 41 | |Coors |60 |125 |2. 083 | |Busch |38 |53 |1. 395 | |Heineken |81 |89 |1. 099 | |Michelob |83 |122 |1. 70 | |Samuel Adams |77 |95 |1. 234 | |Others |147 |79 |0. 537 | Exhibit 16. a) Total Variance Explained: 4 Factors b) Total Variance Explained: 3 Factors |Factors |Variance Explained after Rotation | | |% of Variance |Cumulative % | |1 |19. 81 |19. 481 | |2 |10. 785 |30. 265 | |3 |9. 070 |39. 336 | |4 |8. 511 |47. 846 | |Factors |Variance Explained after Rotation | | |% of Variance |Cumulative % | |1 |21. 461 |21. 461 | |2 |9. 522 |30. 983 | |3 |9. 80 |40. 163 | |Brand Loyalty |Innovation |Risk-Aversion |Machismo | |Miller |-. 1348213 |. 1494557 |-. 0357058 | |Budweiser |-. 1079857 |. 0402900 |. 1710176 | |Pabst |-. 4699937 |. 0313613 |. 2301796 | |Blatz |. 0691795 |. 0070035 |-. 0447371 | |Milwaukee |. 1405362 |. 1502777 |-. 1167947 | |Molson |. 603851 |-. 1008641 |. 0221483 | |Coors |. 0740480 |-. 0743768 |. 1466451 | |Busch |-. 0541226 |-. 0580224 |-. 0127432 | |Heineken |. 0504498 |. 1526235 |. 1086077 | |Michelob |. 1641828 |-. 0092896 |-. 0370552 | |Samuel Adams |. 2739649 |-. 0456192 |-. 0056357 | |Others |-. 0864814 |-. 1462425 |-. 2852078 | Exhibit 17.Rotated Component Matrix: 3 Factors Exhibit 18. Mean Factor Scores | |Factor | | |Innovation |Risk-Aversion |Machismo | |State2 |. 741 | | | |State14 |. 728 | | | |State7 |. 648 | | | |State5 |. 638 | | | |State3 |. 566 | | | |State9 |. 11 | | | |State10 |. 448 | | | |State4 |. 411 | | | |State13 | |. 640 | | |State15 | |. 576 | | |State1 | |. 563 | | |State11 | |. 388 | | |State6 | | |. 611 | |State12 | | |. 08 | |State8 | | |. 422 | Exhibit 19. [pic] Exhibit 20. [pic] Exhibit 21. Regression Analysis a) Beer Consumption Average vs Sex, Age, and Light beer preference | | |Coefficients |t |Sig. | | | |B |Std. Error | | | | |(Constant) |10. 363 |. 631 |16. 434 |. 000 | | |sex |-1. 635 |. 89 |-5. 665 |. 000 | | |age |-. 567 |. 200 |-2. 841 |. 005 | | |Light |-. 068 |. 272 |-. 249 |. 803 | b) Beer Consumption Average vs Sex, Income and Light beer preference | | |Coefficients |t |Sig. | | | |B |Std. Error | | | | |(Constant) |10. 091 |. 95 |16. 962 |. 000 | | |sex |-1. 663 |. 289 |-5. 749 |. 000 | | |Light |-. 030 |. 271 |-. 110 |. 913 | | |income |-. 410 |. 161 |-2. 539 |. 011 | c) Beer Consumption Average vs Sex, Income, Managerial/Professional occupation and Light beer preference | | |Coefficients |t |Sig. | | |B |Std. Error | | | | |(Constant) |9. 623 |. 617 |15. 602 |. 000 | | |sex |-1. 624 |. 281 |-5. 779 |. 000 | | |Light |-. 051 |. 264 |-. 192 |. 848 | | |Managerial/ |. 437 |. 260 |1. 677 |. 94 | | |Professional | | | | | | |income |-. 305 |. 162 |-1. 884 |. 060 | d) Beer Consumption Average vs Sex, Income, Technical, Sales or Administrative occupation and Light beer preference | | |Coefficients |t |Sig. | | | |B |Std. Error | | | | |(Constant) |9. 48 |. 624 |15. 931 |. 000 | | |sex |-1. 606 |. 281 |-5. 711 |. 000 | | |Light |-. 070 |. 264 |-. 265 |. 791 | | |income |-. 369 |. 158 |-2. 327 |. 020 | | |Technical |. 030 |. 306 |. 099 |. 921 | ) Beer Consumption Average vs Sex, Income, Skilled labor and Light beer preference | | |Coefficients |t |Sig. | | | |B |Std. Error | | | | |(Constant) |10. 988 |. 635 |17. 302 |. 000 | | |sex |-1. 528 |. 280 |-5. 451 |. 000 | | |Light |-. 033 |. 263 |-. 26 |. 900 | | |income |-. 288 |. 158 |-1. 818 |. 069 | | |Skilled |-1. 517 |. 391 |-3. 878 |. 000 | f) Beer Consumption Average vs Sex, Income, Self-Employed and Light beer preference | | |Coefficients |t |Sig. | | | |B |Std. Error | | | | |(Constant) |9. 35 |. 658 |15. 107 |. 000 | | |sex |-1. 605 |. 282 |-5. 697 |. 000 | | |Light |-. 072 |. 264 |-. 271 |. 786 | | |income |-. 368 |. 158 |-2. 331 |. 020 | | |Self-Employ |. 042 |. 367 |. 113 |. 910 | g) Beer preference vs Sex, Income, and age | |Coefficients |t |Sig. | | | |B |Std. Error | | | | |(Constant) |1. 167 |. 058 |20. 102 |. 000 | | |sex |. 299 |. 028 |10. 777 |. 000 | | |age |-. 056 |. 023 |-2. 450 |. 014 | | |income |. 016 |. 018 |. 850 |. 395 | ) Beer preference vs Sex, Income, age, and drinking with meals consumption | | |Coefficients |t |Sig. | | | |B |Std. Error | | | | |(Constant) |1. 193 |. 058 |20. 514 |. 000 | | |sex |. 302 |. 028 |10. 908 |. 000 | | |age |-. 048 |. 023 |-2. 105 |. 035 | |Accompany meals drinking |-. 03 |. 001 |-3. 800 |. 000 | | |income |. 016 |. 018 |. 866 |. 386 | i) Beer preference vs Sex, Income, age, and social event consumption | | |Coefficients |t |Sig. | | | |B |Std. Error | | | | |(Constant) |1. 064 |. 066 |16. 218 |. 000 | | |sex |. 288 |. 028 |10. 331 |. 00 | | |age |-. 042 |. 023 |-1. 843 |. 066 | | |income |. 017 |. 018 |. 921 |. 357 | |Social event drinking |. 002 |. 001 |3. 302 |. 001 | Exhibit 22. Comparative Table: Existing Consumer vs Potential Consumer |Attributes |Existing Consumer Base |Potential Consumer Base | |Average Beer Consumption |6. 2 bottles a week |6. 9 bottles a week | |Beer Preference |Light |68. 50% |58. 70% | | |Regular |31. 50% |38. 90% | |Gender |Male |65. 80% |59. 50% | | |Female |34. 20% |40. 0% | |Average Income |$50,000 and above |38. 40% |38. 10% | | |$30,000-$49,999 |34. 20% |38. 10% | | |Below |27. 40% |28. 80% | |Occupation |Managerial/ Professional |52. 10% |42. 0% | | |Technical, Sales or |21. 90% |21. 40% | | |Administrative | | | | |Skilled Labour |8. 20% |19. 00% | | |Self-Employed |16. 40% |14. 0% | | |Others |1. 40% |2. 40% | |Age