Statistical analysis is essential for professional marketing research because it’s used to measure differences or associations between variables. ARI uses statistical techniques with moderate complexity to develop useful insights and focused recommendations. These techniques are rooted in reality and proven through academic publications. Reliance on statistics offered from do-it-yourself surveys is inadequate for important decisions because theses services seldom test the significance of the results. The statistical procedures most frequently used on ARI marketing projects include the following:

(1) Univariate techniques are used for analyzing data when there is a single measurement of each element in the sample or when there are several measurements on each element but each variable is analyzed in isolation. These techniques focus on averages and distributions. Sub classifications of univariate techniques are based on the way data is measured. i.e. metric or nonmetric. A satisfaction rating based on a 5-point scale is an example of metric data. Survey questions with a yes or no response offer data in a nonmetric format.

(1a) Analysis of variance (ANOVA) is a common univariate technique because it’s often used to measure differences between groups or business units. ANOVA is also used to determine the significance of performance differences when they do exist. Analysis of variance is very useful for customer satisfaction surveys because it helps clients identify meaningful differences. To see a detailed example of this procedure click here.

(1b) A cross tabulation performed with a chi-square test is another univariate technique which is used to reveal associations between two or more marketing variables. The chi-square value is used to test the significance of the association. This statistical technique is often utilized to identify customer types who are more or less likely to be influenced by a promotional strategy. For example, is there an association between motivation and the reach of a specific advertising medium? To see a detailed example of this procedure click here.

(2) Multivariate techniques are used for analyzing data when there are two or more measurements of each element and the variables are analyzed simultaneously. These techniques concentrate on the degree of relationships between marketing variables of interest. Sub classifications of multivariate techniques are based on dependence and interdependence techniques.

(2a) Regression analysis is a powerful predictive analysis technique in which one or more variables is used to predict the level of another based on a straight line formula. This is classified as a dependence technique because one or more variables can be identified as dependent variables and the remaining as independent variables. Regression analysis is frequently used to measure the significance and strength of relationships between marketing variables. i.e. sales and advertising expenditures. To see a detailed example of this procedure click here.

(2b) Factor analysis is one of the more complex statistical techniques because a whole set of interdependent relationships among variables is examined. Factor analysis is primarily used to identify the underlying dimensions which explain the correlations among a set of marketing variables. This insight is useful for market segmentation because it can be utilized to identify the underlying variables on which to group customers. To see a detailed example of this procedure click here.


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