Premium Product Promotions: A Market Research Firm Is Trying

Premium Product Promotions A Market Research Firm Is Trying To Deter

Premium Product Promotions, a market research firm, is trying to determine if there are differences in the brand of beer preferred by various customer groups. Formulate and test a hypothesis for them using a = 0.05 if the following data are the preferences of a sample of 800. CUSTOMER GROUP BRAND OF BEER Pale Golden Heavy Housewives Businessmen Factory Workers College Students

Paper For Above instruction

Premium Product Promotions A Market Research Firm Is Trying To Deter

Market research plays a crucial role in understanding consumer preferences, especially when companies aim to tailor their marketing strategies and product offerings to specific customer segments. In this context, a market research firm seeks to investigate whether preferences for different brands of beer vary significantly across distinct customer groups. The primary objective is to determine whether the distribution of preferred beer brands is independent of customer demographic segments, or if there exists a statistically significant association between the group type and brand preference. This analysis is essential for developing targeted marketing campaigns and optimizing product positioning in competitive markets.

The fundamental question posed is whether there are statistically significant differences in beer brand preferences among groups such as housewives, businessmen, factory workers, and college students. To address this, the research involves formulating an appropriate null hypothesis (H0) and alternative hypothesis (Ha), selecting a suitable test, and interpreting the results based on a significance level (α) of 0.05.

Formulating the Hypotheses

The null hypothesis (H0) asserts that there is no association between customer group and preferred beer brand, implying that preferences are independent of group membership. Conversely, the alternative hypothesis (Ha) suggests that at least one customer group exhibits a different preference pattern, indicating a dependence between group type and beer brand choice. Specifically:

• H0: The distribution of beer preferences is independent of customer group.
• Ha: The distribution of beer preferences depends on customer group.

Data Collection and Sample Distribution

While specific data details have not been provided in this context, it is standard practice in such analyses to construct a contingency table summarizing the observed frequencies of each brand preference within each customer group. With a total sample size of 800 and multiple groups, the observed counts would typically be tabulated as follows:

Statistical Test and Calculation

The appropriate statistical method for analyzing the independence of categorical variables in such a contingency table is the Chi-Square Test of Independence. This test assesses whether there is a significant association between customer groups and brand preferences.

To perform the Chi-Square Test, the following steps are necessary:

1. Calculate the expected frequencies for each cell under the assumption that there is no association. The expected frequency for each cell is determined by:
2. Expected frequency = (row total × column total) / grand total
3. Compute the Chi-Square statistic using:
4. χ² = Σ (Observed - Expected)² / Expected
5. where the summation is over all cells in the table.
6. Compare the calculated χ² value against the critical value from the Chi-Square distribution table with the appropriate degrees of freedom, which is (number of rows - 1) × (number of columns - 1).

If the calculated χ² exceeds the critical value at α = 0.05, reject the null hypothesis, indicating that preferences vary significantly across groups.

Interpretation and Conclusion

Should the statistical analysis reveal a significant association, this implies that customer group membership influences beer brand preferences. Such findings are valuable for marketing strategies, enabling companies to customize promotions and product packaging tailored to each demographic segment.

Conversely, if no significant association is found, the company can infer that preferences are uniform across groups, and broader marketing campaigns might suffice without segment-specific modifications.

In conclusion, the utilization of the Chi-Square Test of Independence provides a robust framework for understanding consumer preferences and informing targeted marketing efforts in the competitive beverage industry.

References

• Agresti, A. (2018). An Introduction to Categorical Data Analysis. Wiley.
• Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics. Sage Publications.
• McHugh, M. L. (2013). The Chi-square test of independence. Biochemia Medica, 23(2), 143–149.
• Yates, F. (1934). Contingency table involving small numbers and the χ² test. Supplement to Journal of the Royal Statistical Society, 1(2), 217–235.
• Freeman, E. (2018). Practical applications of Chi-Square analysis in market research. Journal of Marketing Analytics, 6(3), 188–197.
• Moore, D. S., McCabe, G. P., & Craig, B. A. (2012). Introduction to the Practice of Statistics. W.H. Freeman.
• Hochberg, Y., & Benjamini, Y. (1990). More powerful procedures for multiple significance testing. Statistics in Medicine, 9(7), 811–818.
• Greenwood, S., & McGuirk, P. (2014). Segmenting consumers in the beverage industry: A cluster analysis approach. International Journal of Market Research, 56(2), 217–237.
• Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics. Pearson.
• Everitt, B. S. (2002). The Cambridge Dictionary of Statistics. Cambridge University Press.