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CASE STUDIES 777 AESTUDIES CASE QUESTIONS 1. Evaluate the questionnaire in light of the stated research objectives. 2. Using the computerised database, obtain frequency distributions for sufficient questions to enable you to adequately describe the sample. 3. Perform appropriate cross-tabulations to enable you to compare the respondents in both Melbourne and Sydney in terms of their strawberry buying and consumption characteristics. 4. Select a basis for segmenting strawberry consumers; using appropriate univariate, bivariate and/or multivariate techniques identify viable consumer segments. Profile the identified segments and justify their validity. 5. Conduct any additional analyses that you consider necessary in order to derive information for a marketing strategy that would aim to increase strawberry consumption in Australia.

CASE STUDY 6.2 The power of multivariate techniques Adapted by Mike Shaw,Monash University,Melbourne,from the article by Sarah Evans,Senior Marketing Research Analyst, Burke, Inc.1 Read through this description of multivariate techniques in research and answer the questions at the conclusion of the article. ‘As a market researcher, much of what I love about this field is our ability to uncover peoples’ perceptions and motivations and then help integrate these findings into marketing strategies. Often we need to examine our respondents’ data with multivariate techniques to fully understand the complexity of the information we have. Our goal is to make the data “speak” in a clearly understandable and believable voice.’ Sarah Evans The new researcher often gets caught up in applying the ‘techniques’ available and fails to keep in mind the study’s objectives. This ‘technique focus’ has become even more prevalent in the past five years as menu- prompted statistical software packages make running these techniques even simpler. Here I share with you brief comments on my views of the use of several multivariate techniques (analysis of variance, multiple regression, discriminant analysis, factor analysis, cluster analysis, multi-dimensional scaling and conjoint analysis) in hopes of easing the difficulties of analysis so that you have the best results possible to channel back to your marketing team.

Analysis of variance ANalysis Of VAriance (ANOVA) is an extremely helpful tool in practical marketing research, as it is used most often to help reduce familywise error. Familywise error is the cumulative effect of Type I error (saying that two numbers are different when in fact they are not different) across all paired comparisons. However, before you choose to use an ANOVA, you should make sure that your data are appropriate. ANOVA is an omnibus test meaning that it looks for overall differences among all nominally scaled independent variables on a given interval or metric dependent variable. In addition to having a nominal independent variable like brands, products, or subgroups, and an interval or metric dependent variable like performance ratings, importance ratings, and awareness levels, you also need to meet a couple of other assumptions of ANOVA: (1) the sampled populations are normally distributed; and (2) the population variances are equal. If it appears that these assumptions have been grossly violated, then you should use a non-parametric alternative such as the Kruskal- Wallis Test. Once you have determined that your data are appropriate for ANOVA, run the ANOVA and look to the F-value for significance. The F-value tests the null hypothesis that the levels of the treatment (a.k.a. independent variables) are homogenous by comparing the variance due to the treatment to the variance due to the error. Essentially, ANOVA determines whether the discrepancies between the treatment averages are greater than what could reasonably be expected from the variation that occurs within the treatment classifications.The larger this comparison, the larger F becomes and the greater the likelihood of rejecting the null hypothesis of no difference across treatment means. If you are using SAS or SPSS to run your ANOVA, the Market Research - Case studies 7/1/02 2:51 pm Page 778

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program will tell you the p-value associated with the F. As always, if you are using a 95 percent confidence level, then a p-value of less than .05 indicates a significant F. If the null hypothesis of homogeneity is rejected, then additional comparisons to isolate group differences must be conducted. A battery of tests are available for those paired comparisons including the Student- Newman-Keuls Range Test (SNK), Bonferroni alpha adjustment, Scheffe alpha adjustment, and Tukey alpha adjustment.The easiest of these is also the most conservative, the Bonferroni alpha adjustment.To conduct this test, run paired comparison t-tests as you normally would, but rather than comparing each test’s p-value to your overall alpha level (.05 if your desired confidence level is 95%), instead compare each p-value to a newly computed alpha that accounts for familywise error. To compute the Bonferroni adjusted alpha, use the following formula:

CASE STUDIES × original alpha 2 (number of categories) × (number of categories – 1)

Multiple regression Multiple regression is a tried and true technique in marketing research, primarily being used to make predictions and understand the relative contribution of predictors to some predicted variable.The question we are asked most often is ‘How high must a correlation be for it to be meaningful?’ The answer depends on what you are trying to do with the results of the analysis. If you want to see which predictor variables appear to be most highly related to some outcome variable, examination of the standardised regression coefficients (betas) gives you a good indication. If you want to predict with the model, you look at the standard error of the model. A manager would not applaud your efforts if you have a high R2 but the range of error in your prediction is 50% of the predicted value.A rigorous test is to use the model to predict for respondents not used to estimate the model (a hold-out sample). Judging the predictive ability of the model based on just the set of data used to build it generally leads to overestimation of its power.

Discriminant analysis As with multiple regression, the primary uses of discriminant analysis are prediction and determination of relative importance of predictor variables. The key difference between these two techniques is that multiple regression requires an interval- or ratio-dependent variable, whereas discriminant analysis uses a dichotomous or categorical dependent variable.Whereas multiple regression might be used to predict degree of purchase interest, discriminant analysis would be used if one only wanted to predict whether the respondent was a purchaser or non-purchaser. At times someone will suggest taking an interval- or ratio-scaled variable and reduce it to a nominally scaled variable. For example, you have measured the respondents’ ages in years. Later in the analysis you decide to build a model to predict ‘old’ versus ‘young’, and you divide the ages into two groups. This is dangerous as these are not naturally occurring groups and the rule you used for creating the groups can hide meaningful conclusions. We recommend using discriminant analysis for naturally occurring groups as our first preference. The primary method of looking at the ‘managerial’ significance of a discriminant analysis to see how well you predict group membership. Ideally, classification accuracy should be assessed on a hold-out sample, because, just as with multiple regression, applying the functions to the sample on which they were built leads to fictitiously high accuracy of prediction. The discriminant output should contain a summary table of predicted group membership versus actual group membership. Ask yourself: Are the functions assigning everyone to one group versus the other groups? Do the errors appear to be limited to one group? Additionally, consider the overall accuracy by comparing the hit rate (i.e. the percentage of respondents classified correctly) to what one would expect by chance. A good rule of thumb is that you want to improve at least 20% over chance alone, where chance is computed by the sum of squared prior probabilities for each group. For example, if 30% of respondents belong to group A and the remaining 70% belong to group B, then chance is (.3)2 + (.7)2 or .58 and we’d want a hit rate of at least (1.2) × (.58) or 70%. Market Research - Case studies 7/1/02 2:52 pm Page 779

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Factor analysis STUDIES CASE Most times factor analysis is used for data reduction and understanding how variables are interrelated.We may have asked 20 questions on a topic but the questions really assessed a few common perceptions held by the respondents.We want to look at what ‘groups’ of responses exist in our data.We may have conducted a study about a particular automobile and examination of the grouping of the responses reveals that respondents tend to view the automobile using only two or three major constructs (e.g. style, prestige, etc.), although we asked many questions. In other situations we may want to use a group of questionnaire items to predict some outcome (e.g. using evaluations of service interactions with our company to predict customer satisfaction).We observe that the evaluations are highly intercorrelated and when used in subsequent analysis would create difficulties in interpretation due to the common variances they share. One option we might consider is using factor scores representing a group of variables rather than the original variables. We might also consider examining the variables that appear to make up the various factors and using their average scores or even selecting one variable from each of the factors to represent all the variables that make up that factor.We have these choices to make and our decision depends on our confidence in our ability to fairly interpret and communicate the results.

Cluster analysis Cluster analysis is used primarily for segmentation research. Generally people approach segmentation at two levels.The first we will call simple market segmentations that are performed in categories in which needs and motivations vary mostly by consumers rather than by occasions. For example, one segment of consumers is seeking a high-performance camera that does not require much interaction on the part of the photographer, another segment is seeking a high-performance camera with lots of add-on toys to experiment with, and yet another segment is looking for a point-and-click camera that yields clear pictures even if taken with an unsteady hand.These needs segments do not depend on the occasion of using the camera; consumers do not want an ensemble of three or four cameras to choose from depending on the occasion. The second type we will call occasion-based segmentation, and these are performed in categories where needs and motivations do vary by occasion. For example, the selection of a will not always be based on the same needs. It will depend on time of day,who is in the party,day of the week, and cause for celebration, among other things. Occasion-based segmentations are commonly performed in food and drink categories as any one consumer may have several sets of needs depending on the circumstances of the occasion. For both market- and occasion-based segmentations using cluster analysis, data should be at least interval level and you should have complete data on every respondent. Avoid, if possible, using substitution values for missing data, for instance replacing missing values with the mean of the remaining data. This may be unavoidable, but in the end you have to recognise that it will influence the outcome and you have essentially ‘made-up data’. Once you have the results, profile each of the segments by the variables included in the cluster analysis. First, understand which variables everyone is seeking and which variables no one is seeking; these variables are market-level characteristics, not segment-level characteristics. Separating them from the rest of the attributes will make identifying the segment-level needs easier. Second, sort the remaining attribute means from high to low within each segment. Jot down the key themes and give each segment a tentative name. Next, profile each of the clusters by variables outside of the cluster analysis including demographics, pyschographics, product usage, and behaviours. If the clusters aren’t different on these variables that were not used in the clustering, it is likely that they will be of little use to management. If the clusters do display differences on these ‘outside’ variables, use this information combined with the variables from the cluster analysis to name the clusters and describe them, keeping in mind the overall goal of marketing products/services to each of these segments.

Multi-dimensional scaling The simplest definition of multi-dimensional scaling (MDS) is that it draws a picture of relationships you have measured with numbers. Let’s assume we want to try to understand how people perceive the following seven Market Research - Case studies 7/1/02 2:52 pm Page 780

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quick-service : McDonald’s, , Nando’s, Bill’s, Hungry Jack’s, Pizza Hut, KFC.The respondents rate the restaurants on a set of attribute scales. There are many ways to create the ‘pictures’ that help you understand how these restaurants are seen.With the rating scales, you could factor analyse the results in a restaurant by restaurant correlation matrix and plot the resulting factor loadings.You could treat the ratings for each restaurant as a ‘group’, perform discriminant analysis, and plot the discriminant scores. The rating data can be used in an MDS program that essentially creates a picture in which the items that are closer together more often are closest on the picture and those distant from each other are further apart. At this point you may say that this is too simple to be meaningful. On the contrary, its simplicity is part of its attraction. On the surface it is easy to understand and the pictures give a vivid portrayal of what respondents

CASE STUDIES seem to be saying. Mathematically, it is more rigorous than it may first appear. Thus, if you get a multi-dimensional picture of your data and the analysis scheme tells you that it is a good fit, then you can have confidence that it is showing what is very likely a real structure for the way people see things.

Conjoint analysis Unlike the preceding methods, conjoint analysis is not so much a multivariate technique as it is a family of research procedures for designing and analysing experiments. The purpose of the experiment is usually to determine the impact on choice or preference of each of the features of a product or service.What is common across conjoint studies is the assumption that a product or service is a bundle of features that are considered jointly. For example, a candy bar is the combination of its ingredients, its size, its price and its brand. Conjoint has a wide variety of applications including the following examples: • Identifying the product or service with the optimum combination of features. • Determining the relative contributions of each attribute and each attribute level to the overall evaluation of product/service. • Predicting market share among products/services with differing sets of features. • Measuring market opportunities for products not currently on the market. • Determining the profitability of possible products based on a comparison of feature costs to expected price and market share. • Understanding the potential for multi-product or multi-brand strategy,including an estimate of cannibalism. • Assessing the impact of deleting a product or brand from the market. • Determining how to change a current product to compete with new products entering the market. • Estimating the effect of eliminating some product features that are costly to provide but are of marginal value to customers. • Segmenting customers who place differing importance on features, possibly understanding the size of the segment who buys strictly on price or the size of the segment who buys strictly on brand. Irrespective of the approach you use to conjoint design and analysis, choosing the features and levels to include is of vital importance to the study’s success.The tendency of new researchers is to want to include an overabundance of features thinking that the consumers are as involved in their category as they are. Humans tend to simplify decision processes, and as a result, including the five to eight most important features is generally sufficient to predict purchase interest.When selecting the final variables for inclusion in the conjoint, be sure to include only features that can add to or detract from overall choice, differentiate between products, be acted on, and be easily communicated. Another issue for the new researcher is the tendency to over-generalise the solution. If the change in levels of price tends to have a great impact on the preference for a product, one cannot make the general statement that ‘price is the most important characteristic’. What you have measured is that the change from one level of price to another among the prices you chose to test was more determinant of choice than a change from one level to another on other characteristics.You could have chosen price levels that were closer together and gotten a very different result. Market Research - Case studies 7/1/02 2:52 pm Page 781

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Note STUDIES CASE 1. Adapted from the professional perspective ‘Data Analysis: Multivariate Techniques’ provided by Sarah Evans in Malhotra, N., Marketing Research: An Applied Orientation, 3rd edn Prentice Hall, New Jersey, 1999, p. 670.

QUESTIONS 1. List all the techniques described in this article and give a short definition of each. 2. Develop a practical research problem for each of the methods mentioned by describing a marketing management decision situation that would require their use. 3. Four of the methods described contrast the output obtained with the output from a cross-tabulation or frequency distribution. Show how the multivariate techniques provide more detailed information. 4. Explain why the researcher must specify the nature of the survey measurements before the survey is conducted and demonstrate this by reference to the methods described in this article.

PART 7

CASE STUDY 7.1 Strawberry fields for ever (Part C) Prepared by Peter Oppenheim, School of Business, University of Ballarat, Ballarat. Following an analysis of the data as discussed in Case Study 6.1 the principal research analyst convened a meeting with the executive committee of the VSIDC. At this meeting the analyst presented an initial set of research results. The results included an analysis of the sample and a comparison of the sample with the demographics of the population as a whole in order to demonstrate the representativeness of the sample.The initial results also included a segmentation analysis and a detailed profile of each of the identified strawberry consumer segments.The presentation concluded with a recommended marketing strategy, which was based on the research findings and the $450 000 budget that the VSIDC had allocated to promotion for the following season. The analyst pointed out that, consistent with the research objectives, the primary objective of the recommended strategy was to change the image of strawberries and increase strawberry consumption in Australia. The chairman of the VSIDC congratulated the analyst on the presentation and asked if the written report could include charts and diagrams as well as the tabulated results.The analyst nodded and commented that their written reports always included results in a graphical form. The executive committee then left the meeting feeling that they had made the correct decision to undertake the market research before launching an expensive marketing strategy, which without the research, would have been based on hunch and intuition.

QUESTION • Using the results obtained from your analysis in Case Study 6.1, prepare a written report for the VSIDC.Your report should be structured appropriately and include details of the recommended marketing strategy.

CASE STUDY 7.2 Brentford Square Community Shopping Centre (Part E): Weighting the data Prepared by Mike Shaw and Paula Tomsett,Lynx Research Group Pty Ltd, Melbourne. This case study uses the data files included on the companion SPSS CD-ROM.The file bscatfull-Catchment area survey full data set.sav is the complete listing of the data file for use with SPSS 10. bscat1-Part1Catchment area survey for student ed.sav is the file for use with the student edition of SPSS 10. For the Incentre survey the data files are labelled on the companion SPSS CD-ROM.