MKF2121: Marketing Research Methods Assessment

Assignment Overview

A marketing research analysis report communicates the results of a study to clients. Typically, a manager commissions such a report to solve a Management Decision Problem (MDP) and inform future decisions.

Scenario:

• You have been commissioned by Kimberly-Clark (Huggies brand) to draft a brief research report using Case 3.3: “Kimberly-Clark: Competing Through Innovation”.

• Your goal is to analyze the KC survey dataset to identify customer preferences, market segments, and reactions to their direct mail campaign.

Questions

1. How would you treat missing values in the following variables that are to be treated as dependent variables: Mailer_Noticed (Q1), Mailer_Opened (Q2), and Likely_Purchase (Q3)?

2. How would you treat missing values in the following variables that are to be treated as independent variables: Info_New_Different (Q6), Info_Appropriate (Q7), Info_Believable (Q8), and Info_Understanding (Q9)?

3. Recode Diaper_Size (Q13) by combining Newborn, One, and Two into a single category and combining Five, Six, and Some Other Size into a single category.

4. Recode the remaining demographic variables as follows:
   a. Age_Range (Q15) categories should be Under 25, 26–30, 31–35, and 36+
   b. Marital_Status (Q16) should be recoded by combining Single, Divorced, Widowed, and Separated into a single category
   c. For Education (Q18), combine Grade School and Some High School into a single category as well as combine Graduated College and Post Graduate Work into another category
   d. Ethnic_Origin (Q19) should have Hispanic/Latino, Asian, and Some Other Race combined into a single category
   e. Household_Income (Q20) should have the two highest income categories combined into a single category denoted by $50,000 or more

5. Calculate an overall rating score for Diaper Dash brand that is the sum of Overall_Quality (Q4a), Brand_I_Trust (Q4b), and Brand_I_Recommend (Q4c). Run a frequency distribution, calculate the summary statistics, and interpret the results.

6. Cross-tabulate Mailer_Noticed (Q1) and Mailer_Opened (Q2) against the original categorical demographic variables. What problems do you see? How could these problems have been averted?

7. Recode the purchase likelihood (Likely_Purchase, Q3) into two groups by combining codes 2, 3, 4, and 5 into a single category. Cross-tabulate the recoded purchase likelihood against the recoded categorical demographic variables.

8. Does the response to the mailer in terms of likelihood of purchase (Likely_Purchase, Q3) differ for respondents in Cell L and Cell M (Screening Q4)? How would your analysis change if this variable were treated as ordinal rather than interval scaled?

9. Do the responses to the mailer in terms of likelihood of purchase (Likely_Purchase, Q3) differ depending on the gender of baby (boy vs. girl, Screening QH)? How would your analysis change if this variable were treated as ordinal rather than interval scaled?

10. Do the ratings of Diaper Dash (Overall_Quality (Q4a), Brand_I_Trust (Q4b), and Brand_I_Recommend (Q4c)) differ for respondents in Cell L and Cell M (Screening Q4)? How would your analysis change if these variables were treated as ordinal rather than interval scaled?

11. Do the ratings of Diaper Dash brand (Overall_Quality (Q4a), Brand_I_Trust (Q4b), and Brand_I_Recommend (Q4c)) differ depending on the gender of baby (boy vs. girl, Screening QH)? How would your analysis change if these variables were treated as ordinal rather than interval scaled?

12. Do the respondents evaluate Diaper Dash higher on Brand_I_Trust (Q4b) than they do on Brand_I_Recommend (Q4c)? What analysis would you conduct if these variables were treated as ordinal rather than interval scaled?

13. Do the respondents evaluate the mailer higher on Info_Understanding (Q9) than they do on Info_New_Different (Q6)? What analysis would you conduct if these variables were treated as ordinal rather than interval scaled?

14. Can the ratings of Diaper Dash brand (Overall_Quality (Q4a), Brand_I_Trust (Q4b), and Brand_I_Recommend (Q4c)) be explained in terms of the recoded demographic characteristics?

15. Can the mailer ratings (Info_New_Different (Q6), Info_Appropriate (Q7), Info_Believable (Q8), and Info_Understanding (Q9)) be explained in terms of the recoded demographic characteristics?

16. Can each of the ratings of Diaper Dash brand (Overall_Quality (Q4a), Brand_I_Trust (Q4b), and Brand_I_Recommend (Q4c)) be explained in terms of message ratings (Info_New_Different (Q6), Info_Appropriate (Q7), Info_Believable (Q8), and Info_Understanding (Q9)) when responses to the mailer are considered simultaneously?

17. Can each of the mailer ratings (High_Quality_Brand (Q10a), Info_is_Informative (Q10b), and Info_I_Want (Q10c)) be explained in terms of message ratings (Info_New_Different (Q6), Info_Appropriate (Q7), Info_Believable (Q8), and Info_Understanding (Q9)) when responses to the mailer are considered simultaneously?

18. Can the Likely_Purchase (Likely_Purchase, Q3) be explained in terms of message ratings (Info_New_Different (Q6), Info_Appropriate (Q7), Info_Believable (Q8), and Info_Understanding (Q9)) when responses to the mailer are considered simultaneously? Interpret the results of your analysis.

19. Recode the purchase likelihood (Likely_Purchase, Q3) into two groups by combining codes 2, 3, 4, and 5 into a single category. Run a two-group discriminant analysis with recoded Likely_Purchase as the dependent variable and responses to the message ratings (Info_New_Different (Q6), Info_Appropriate (Q7), Info_Believable (Q8), and Info_Understanding (Q9)) as the independent variables. Interpret the results.

20. Recode each of the ratings of Diaper Dash brand (Overall_Quality (Q4a), Brand_I_Trust (Q4b), and Brand_I_Recommend (Q4c)) into two groups (1–8 = Group 1, 9–10 = Group 2). Run three two-group discriminant analyses with message ratings (Info_New_Different (Q6), Info_Appropriate (Q7), Info_Believable (Q8), and Info_Understanding (Q9)) as the independent variables. Interpret the results.

21. Recode each of the ratings of Diaper Dash brand (Overall_Quality (Q4a), Brand_I_Trust (Q4b), and Brand_I_Recommend (Q4c)) into three groups (1–7 = Group 1, 8–9 = Group 2, 10 = Group 3). Run three three-group discriminant analyses with message ratings (Info_New_Different (Q6), Info_Appropriate (Q7), Info_Believable (Q8), and Info_Understanding (Q9)) as the independent variables. Interpret the results.

22. Factor analyze Diaper Dash brand ratings (Overall_Quality (Q4a), Brand_I_Trust (Q4b), and Brand_I_Recommend (Q4c)). Use principal components with varimax rotation. Interpret and explain the results.

23. Factor analyze the message ratings (Info_New_Different (Q6), Info_Appropriate (Q7), Info_Believable (Q8), and Info_Understanding (Q9)). Use principal components with varimax rotation. Interpret and explain the results.

24. Factor analyze the mailer ratings (High_Quality_Brand (Q10a), Info_is_Informative (Q10b), and Info_I_Want (Q10c)). Use principal components with varimax rotation. Interpret and explain the results.

25. Cluster the respondents based on message ratings (Info_New_Different (Q6), Info_Appropriate (Q7), Info_Believable (Q8), and Info_Understanding (Q9)). Interpret the results.

26. Construct 16 full profiles using the following attribute levels: style (print/colors, plain white), absorbency (regular, super absorbent), taping (regular tape, resealable tape), and leakage (regular, leak-proof). Rank the 16 full profiles in terms of preference. Calculate the part-worth functions and the importance of each attribute.

27. Develop an SEM model that posits brand ratings (Q4a–Q4c), message ratings (Q6–Q9), and mailer ratings (Q10a–Q10c) as exogenous variables. These three exogenous variables determine mailer impressions (Q1–Q3).
    a. Specify the measurement model.
    b. Estimate the measurement model and assess its reliability and validity.
    c. Specify the structural model.
    d. Estimate the structural model and assess its validity.
    e. Draw conclusions and make recommendations.

28. Write a report for Kimberly-Clark based on all the analyses that you have conducted. What would you recommend that Kimberly-Clark do in order to increase market share?

29. If the survey conducted by Kimberly-Clark were to be conducted in Australia, how should the marketing research be conducted?

Assessment Requirements – Brief Summary

The assessment required students to conduct a comprehensive marketing research analysis for Kimberly-Clark (Huggies brand) using Case 3.3: “Competing Through Innovation.” The key objectives were to:

1. Data Preparation and Cleaning

   • Handle missing values for dependent (Mailer_Noticed, Mailer_Opened, Likely_Purchase) and independent variables (Info_New_Different, Info_Appropriate, Info_Believable, Info_Understanding).

   • Recode categorical variables (Diaper_Size, Age_Range, Marital_Status, Education, Ethnic_Origin, Household_Income).

2. Descriptive and Exploratory Analysis

   • Calculate overall rating scores for Diaper Dash brand and summarize results.

   • Cross-tabulate key variables with demographics to identify patterns and potential issues.

3. Comparative Analysis

   • Examine differences in mailer response and brand ratings across demographic and screening variables.

   • Analyze relationships between mailer response, message ratings, and brand ratings.

4. Advanced Statistical Techniques

   • Conduct discriminant analyses for purchase likelihood and brand ratings.

   • Perform factor analysis for brand, message, and mailer ratings.

   • Cluster respondents based on message ratings.

   • Conduct conjoint analysis to calculate part-worth utilities and attribute importance.

   • Develop and estimate a Structural Equation Model (SEM) to examine causal relationships between brand ratings, message ratings, mailer ratings, and mailer impressions.

5. Reporting and Recommendations

   • Compile a professional research report with actionable recommendations for Kimberly-Clark to increase market share.

   • Suggest adjustments for conducting similar research in different markets, e.g., Australia.

Learning Objectives Covered:

• Data cleaning and recoding for analysis

• Descriptive, bivariate, and multivariate analysis

• Advanced statistical modeling (SEM, factor analysis, discriminant analysis)

• Market segmentation and profiling

• Conjoint analysis for product preferences

• Report writing and actionable recommendations

Assessment Approach – Step by Step

Step 1: Data Cleaning and Variable Recoding

• The Academic Mentor guided the student to identify missing values and determine appropriate handling (e.g., mean imputation or removal) for both dependent and independent variables.

• Recode demographic and product variables to create meaningful, aggregated categories for analysis (e.g., combining diaper sizes, income ranges, education levels).

Step 2: Descriptive and Exploratory Analysis

• Students were guided to calculate overall brand scores (sum of Overall_Quality, Brand_I_Trust, Brand_I_Recommend).

• Perform frequency distributions and summary statistics to interpret customer preferences and survey response patterns.

• Cross-tabulations with demographic variables were conducted to identify trends or inconsistencies in the dataset.

Step 3: Comparative Analyses

• Mentor explained how to compare mailer responses by different demographic or screening groups using cross-tabs and chi-square tests.

• Discussed the difference between treating variables as ordinal vs. interval, and how it affects analysis techniques.

Step 4: Advanced Statistical Analysis

• Discriminant Analysis: Guide on recoding Likely_Purchase and brand ratings into groups and performing two-group or three-group discriminant analyses using message ratings as predictors.

• Factor Analysis: Use principal components with varimax rotation for brand, message, and mailer ratings to identify underlying factors.

• Cluster Analysis: Segment respondents based on message ratings for market targeting.

• Conjoint Analysis: Rank full product profiles, calculate part-worth functions, and determine attribute importance for consumer preferences.

• Structural Equation Modeling (SEM): Specify, estimate, and validate measurement and structural models linking brand, message, and mailer ratings to mailer impressions.

Step 5: Interpretation and Reporting

• Mentor emphasized interpreting statistical outputs in business terms rather than purely numerical results.

• The student learned to translate analytical results into actionable marketing recommendations, such as tailoring messages or product features to increase purchase likelihood.

• Guidance was provided for report structuring, including executive summary, methodology, analysis, conclusions, and market-specific recommendations.

Outcome and Learning Achievements

• A comprehensive marketing research report was produced, integrating descriptive statistics, cross-tabulations, discriminant analysis, factor and cluster analyses, conjoint analysis, and SEM modeling.

• Student demonstrated competence in handling real-world survey data, performing advanced statistical techniques, and interpreting results for decision-making.

• Key learning objectives achieved: data preparation, segmentation, predictive modeling, multivariate analysis, and professional research reporting.

Final Outcome:

A fully structured, analytically robust research report ready for Kimberly-Clark, with actionable insights and evidence-based recommendations for improving customer engagement and market share.

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